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+{"index_struct_id": "1517f784-78b3-457a-a5aa-a4e1c0c361b5", "docstore": {"docs": {"1517f784-78b3-457a-a5aa-a4e1c0c361b5": {"text": null, "doc_id": "1517f784-78b3-457a-a5aa-a4e1c0c361b5", "embedding": null, "doc_hash": "08a14830cef184731c6b6a0bdd67fa351d923556941aa99027b276bd839a07a4", "extra_info": null, "nodes_dict": {"7296817700772434305": {"text": "ECA-Net: Ef\ufb01cient Channel Attention for Deep Convolutional Neural Networks\n\nQilong Wang1, Banggu Wu1, Pengfei Zhu1, Peihua Li2, Wangmeng Zuo3, Qinghua Hu1,\u2217\n1 Tianjin Key Lab of Machine Learning, College of Intelligence and Computing, Tianjin University, China\n\n2 Dalian University of Technology, China\n\n3 Harbin Institute of Technology, China\n\n0\n2\n0\n2\n\nr\np\nA\n7\n\n]\n\nV\nC\n.\ns\nc\n[\n\n4\nv\n1\n5\n1\n3\n0\n.\n0\n1\n9\n1\n:\nv\ni\nX\nr\na\n\nAbstract\n\nRecently, channel attention mechanism has demon-\nstrated to offer great potential in improving the perfor-\nmance of deep convolutional neural networks (CNNs).\nHowever, most existing methods dedicate to developing\nmore sophisticated attention modules for achieving better\nperformance, which inevitably increase model complexity.\nTo overcome the paradox of performance and complexity\ntrade-off, this paper proposes an Ef\ufb01cient Channel Atten-\ntion (ECA) module, which only involves a handful of param-\neters while bringing clear performance gain. By dissecting\nthe channel attention module in SENet, we empirically show\navoiding dimensionality reduction is important for learning\nchannel attention, and appropriate cross-channel interac-\ntion can preserve performance while signi\ufb01cantly decreas-\ning model complexity. Therefore, we propose a local cross-\nchannel interaction strategy without dimensionality reduc-\ntion, which can be ef\ufb01ciently implemented via 1D convolu-\ntion. Furthermore, we develop a method to adaptively select\nkernel size of 1D convolution, determining coverage of lo-\ncal cross-channel interaction. The proposed ECA module\nis ef\ufb01cient yet effective, e.g., the parameters and computa-\ntions of our modules against backbone of ResNet50 are 80\nvs. 24.37M and 4.7e-4 GFLOPs vs. 3.86 GFLOPs, respec-\ntively, and the performance boost is more than 2% in terms\nof Top-1 accuracy. We extensively evaluate our ECA mod-\nule on image classi\ufb01cation, object detection and", "doc_id": "1148ffdf-77c5-4584-a45b-9e489fa94aad", "embedding": null, "doc_hash": "ab6fe48ea65b293b4de99490ea1840a2c944fa31d7323c2794afd5352d7b0222", "extra_info": null, "index": 0, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 0, "end": 1904}, "image": null}, "5729012705767151825": {"text": "evaluate our ECA mod-\nule on image classi\ufb01cation, object detection and instance\nsegmentation with backbones of ResNets and MobileNetV2.\nThe experimental results show our module is more ef\ufb01cient\nwhile performing favorably against its counterparts.\n\n1. Introduction\n\nDeep convolutional neural networks (CNNs) have been\nwidely used in computer vision community, and have\n\n\u2217Qinghua Hu is the corresponding author.\n\nEmail: {qlwang, wubanggu, huqinghua}@tju.edu.cn. The work was sup-\nported by the National Natural Science Foundation of China (Grant No.\n61806140, 61876127, 61925602, 61971086, U19A2073, 61732011), Ma-\njor Scienti\ufb01c Research Project of Zhejiang Lab (2019DB0ZX01). Q. Wang\nwas supported by National Postdoctoral Program for Innovative Talents.\n\nFigure 1. Comparison of various attention modules\n(i.e.,\nSENet [14], CBAM [33], A2-Nets [4] and ECA-Net) using\nResNets [11] as backbone models in terms of classi\ufb01cation ac-\ncuracy, network parameters and FLOPs, indicated by radiuses of\ncircles. Note that our ECA-Net obtains higher accuracy while hav-\ning less model complexity.\n\nachieved great progress in a broad range of tasks, e.g., im-\nage classi\ufb01cation, object detection and semantic segmenta-\ntion. Starting from the groundbreaking AlexNet [17], many\nresearches are continuously investigated to further improve\nthe performance of deep CNNs [29, 30, 11, 15, 19, 20, 32].\nRecently, incorporation of channel attention into convolu-\ntion blocks has attracted a lot of interests, showing great po-\ntential in performance improvement [14, 33, 13, 4, 9, 18, 7].\nOne of the representative methods is squeeze-and-excitation\nnetworks (SENet) [14], which learns channel attention for\neach convolution block, bringing clear performance gain for\nvarious deep CNN architectures.\n\nFollowing the setting of squeeze (i.e.,\n\nfeature ag-\ngregation) and excitation (i.e., feature recalibration) in\nSENet [14], some researches improve SE block by cap-\nturing more", "doc_id": "1350bac7-f626-4cd8-b23c-2f20f0363daa", "embedding": null, "doc_hash": "47c0e86082c7f3817800f326f6a7d3deaf5952e8e2d21e75162fea2cdb77d9b2", "extra_info": null, "index": 1, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 1844, "end": 3798}, "image": null}, "5973770463335275742": {"text": "[14], some researches improve SE block by cap-\nturing more sophisticated channel-wise dependencies [33,\n4, 9, 7] or by combining with additional spatial atten-\ntion [33, 13, 7]. Although these methods have achieved\n\n \n \n \n \n \n \n\f\nModel\nSENet [14]\nCBAM [33]\nGE-\u03b8\u2212 [13]\nGE-\u03b8 [13]\nGE-\u03b8+ [13]\nA2-Net [4]\nGSoP-Net [9]\nECA-Net (Ours)\n\nNo DR\n\u00d7\n\u00d7\n\u221a\n\u221a\n\n\u00d7\n\u00d7\n\u00d7\n\u221a\n\nCross-channel Interaction\n\u221a\n\u221a\n\n\u00d7\n\u00d7\n\u221a\n\u221a\n\u221a\n\u221a\n\nLightweight\n\u2013\n\u00d7\n\u221a\n\n\u00d7\n\u00d7\n\u00d7\n\u00d7\n\u221a\n\nTable 1. Comparison of existing attention modules in terms of\nwhether no channel dimensionality reduction (No DR), cross-\nchannel interaction and less parameters than SE (indicated by\nlightweight) or not.\n\nmodule (called ECA-Net) introduce very few additional pa-\nrameters and negligible computations, while bringing no-\ntable performance gain. For example, for ResNet-50 with\n24.37M parameters and 3.86 GFLOPs, the additional pa-\nrameters and computations of ECA-Net50 are 80 and 4.7e-\n4 GFLOPs, respectively; meanwhile, ECA-Net50 outper-\nforms ResNet-50 by 2.28% in terms of Top-1 accuracy.\n\nTable 1 summarizes existing attention modules in terms\nof whether channel dimensionality reduction (DR), cross-\nchannel interaction and lightweight model, where we can\nsee that our ECA module learn effective channel attention\nby avoiding channel dimensionality reduction while captur-\ning cross-channel interaction in an extremely lightweight\nway. To evaluate our method, we conduct experiments on\nImageNet-1K [6] and MS COCO [23] in a variety of tasks\nusing different deep CNN architectures.\n\nThe contributions of this paper are summarized as fol-\nlows. (1) We dissect the SE block and empirically demon-\nstrate avoiding dimensionality reduction and appropriate\ncross-channel interaction are important to learn effective\nand ef\ufb01cient channel attention, respectively. (2) Based on\nabove analysis, we make an attempt to develop an", "doc_id": "4a20b5cb-ddbb-4d11-843b-0706888a10bd", "embedding": null, "doc_hash": "9780ad227f798d3977e8d4c3d88f17db859f29acc74d386693678d5da662e124", "extra_info": null, "index": 2, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 3810, "end": 5660}, "image": null}, "1576839141285306667": {"text": "respectively. (2) Based on\nabove analysis, we make an attempt to develop an extremely\nlightweight channel attention module for deep CNNs by\nproposing an Ef\ufb01cient Channel Attention (ECA), which in-\ncreases little model complexity while bringing clear im-\nprovement. (3) The experimental results on ImageNet-1K\nand MS COCO demonstrate our method has lower model\ncomplexity than state-of-the-arts while achieving very com-\npetitive performance.\n\n2. Related Work\n\nAttention mechanism has proven to be a potential means\nto enhance deep CNNs. SE-Net [14] presents for the \ufb01rst\ntime an effective mechanism to learn channel attention and\nachieves promising performance. Subsequently, develop-\nment of attention modules can be roughly divided into two\n(1) enhancement of feature aggregation; (2)\ndirections:\ncombination of channel and spatial attentions. Speci\ufb01cally,\nCBAM [33] employs both average and max pooling to ag-\n\nFigure 2. Diagram of our ef\ufb01cient channel attention (ECA) mod-\nule. Given the aggregated features obtained by global average\npooling (GAP), ECA generates channel weights by performing a\nfast 1D convolution of size k, where k is adaptively determined\nvia a mapping of channel dimension C.\n\nhigher accuracy, they often bring higher model complex-\nity and suffer from heavier computational burden. Different\nfrom the aforementioned methods that achieve better per-\nformance at the cost of higher model complexity, this paper\nfocuses instead on a question: Can one learn effective chan-\nnel attention in a more ef\ufb01cient way?\n\nTo answer this question, we \ufb01rst revisit the channel at-\ntention module in SENet. Speci\ufb01cally, given the input fea-\ntures, SE block \ufb01rst employs a global average pooling for\neach channel independently, then two fully-connected (FC)\nlayers with non-linearity followed by a Sigmoid function\nare used to generate channel weights. The two FC layers\nare designed to capture non-linear cross-channel interac-\ntion, which involve dimensionality reduction for controlling\nmodel complexity. Although this strategy is widely used in\nsubsequent channel attention modules [33, 13, 9], our em-\npirical studies show dimensionality reduction brings side", "doc_id": "274fbd51-65ad-4b3e-bf31-ac08ac39627e", "embedding": null, "doc_hash": "c242e116e4b3c50513b9112b0d11539a1626825e36b2c561634eaeab959c095a", "extra_info": null, "index": 3, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 5647, "end": 7821}, "image": null}, "5672338402144338994": {"text": "13, 9], our em-\npirical studies show dimensionality reduction brings side ef-\nfect on channel attention prediction, and it is inef\ufb01cient and\nunnecessary to capture dependencies across all channels.\n\nTherefore, this paper proposes an Ef\ufb01cient Channel At-\ntention (ECA) module for deep CNNs, which avoids dimen-\nsionality reduction and captures cross-channel interaction in\nan ef\ufb01cient way. As illustrated in Figure 2, after channel-\nwise global average pooling without dimensionality reduc-\ntion, our ECA captures local cross-channel interaction by\nconsidering every channel and its k neighbors. Such method\nis proven to guarantee both ef\ufb01ciency and effectiveness.\nNote that our ECA can be ef\ufb01ciently implemented by fast\n1D convolution of size k, where kernel size k represents the\ncoverage of local cross-channel interaction, i.e., how many\nneighbors participate in attention prediction of one channel.\nTo avoid manual tuning of k via cross-validation, we de-\nvelop a method to adaptively determine k, where coverage\nof interaction (i.e., kernel size k) is proportional to channel\ndimension. As shown in Figure 1 and Table 3, as opposed\nto the backbone models [11], deep CNNs with our ECA\n\nGAP\u00d7: element-wise productAdaptive Selection of Kernel Size:5k=\uf07311C\uf0b4\uf0b4\uf06311C\uf0b4\uf0b4\uf0c4()kC\uf079=WHCWHC\uf063\uff5e\f\ngregate features. GSoP [9] introduces a second-order pool-\ning for more effective feature aggregation. GE [13] explores\nspatial extension using a depth-wise convolution [5] to ag-\ngregate features. CBAM [33] and scSE [27] compute spa-\ntial attention using a 2D convolution of kernel size k \u00d7 k,\nthen combine it with channel attention. Sharing similar phi-\nlosophy with Non-Local (NL) neural networks [32], GC-\nNet [2] develops a simpli\ufb01ed NL network and integrates\nwith the SE block, resulting in a lightweight module to\nmodel long-range dependency. Double Attention Networks\n(A2-Nets) [4] introduces a novel relation function for NL\nblocks for image or video recognition. Dual", "doc_id": "06faaa57-ad2f-4dc7-afd1-23ed61afa3da", "embedding": null, "doc_hash": "05073a7f8fbf507c1ccfe97fcf9c324e78364a112228a23497ac3423a568741b", "extra_info": null, "index": 4, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 7822, "end": 9781}, "image": null}, "5087516292114383165": {"text": "introduces a novel relation function for NL\nblocks for image or video recognition. Dual Attention Net-\nwork (DAN) [7] simultaneously considers NL-based chan-\nnel and spatial attentions for semantic segmentation. How-\never, most above NL-based attention modules can only be\nused in a single or a few convolution blocks due to their high\nmodel complexity. Obviously, all of the above methods fo-\ncus on developing sophisticated attention modules for better\nperformance. Different from them, our ECA aims at learn-\ning effective channel attention with low model complexity.\nOur work is also related to ef\ufb01cient convolutions, which\nare designed for lightweight CNNs. Two widely used ef\ufb01-\ncient convolutions are group convolutions [37, 34, 16] and\ndepth-wise separable convolutions [5, 28, 38, 24]. As given\nin Table 2, although these ef\ufb01cient convolutions involve less\nparameters, they show little effectiveness in attention mod-\nule. Our ECA module aims at capturing local cross-channel\ninteraction, which shares some similarities with channel lo-\ncal convolutions [36] and channel-wise convolutions [8];\ndifferent from them, our method investigates a 1D convolu-\ntion with adaptive kernel size to replace FC layers in chan-\nnel attention module. Comparing with group and depth-\nwise separable convolutions, our method achieves better\nperformance with lower model complexity.\n\n3. Proposed Method\n\nIn this section, we \ufb01rst revisit the channel attention mod-\nule in SENet [14] (i.e., SE block). Then, we make a em-\npirical diagnosis of SE block by analyzing effects of di-\nmensionality reduction and cross-channel interaction. This\nmotivates us to propose our ECA module. In addition, we\ndevelop a method to adaptively determine parameter of our\nECA, and \ufb01nally show how to adopt it for deep CNNs.\n\n3.1. Revisiting Channel Attention in SE Block\n\nLet\n\nthe output of one convolution block be X \u2208\nRW \u00d7H\u00d7C, where W , H and C are width, height and chan-\nnel dimension (i.e., number of \ufb01lters). Accordingly, the\nweights of channels in SE block can be computed as\n\n\u03c9 = \u03c3(f{W1,W2}(g(X", "doc_id": "96aeb1db-2290-43c9-95b0-99adf6f9ca63", "embedding": null, "doc_hash": "84cce20cd2d87fcf08d08ce8ae98b9986168ce6350142e0003add8ec21a404ad", "extra_info": null, "index": 5, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 9771, "end": 11842}, "image": null}, "5711748660893466706": {"text": "as\n\n\u03c9 = \u03c3(f{W1,W2}(g(X ))),\n\n(1)\n\nwhere g(X ) = 1\ni=1,j=1 Xij is channel-wise global\nW H\naverage pooling (GAP) and \u03c3 is a Sigmoid function. Let\n\n(cid:80)W,H\n\nMethods\nVanilla\nSE\nSE-Var1\nSE-Var2\nSE-Var3\nSE-GC1\nSE-GC2\nSE-GC3\nECA-NS\nECA (Ours)\n\nAttention\nN/A\n\u03c3(f{W1,W2}(y))\n\u03c3(y)\n\u03c3(w (cid:12) y)\n\u03c3(Wy)\n\u03c3(GC16(y))\n\u03c3(GCC/16(y))\n\u03c3(GCC/8(y))\n\u03c3(\u03c9) with Eq. (7)\n\u03c3(C1Dk(y))\n\n#.Param.\n0\n2 \u00d7 C2/r\n0\nC\nC2\nC2/16\n16 \u00d7 C\n8 \u00d7 C\nk \u00d7 C\nk = 3\n\nTop-1\n75.20\n76.71\n76.00\n77.07\n77.42\n76.95\n76.98\n76.96\n77.35\n77.43\n\nTop-5\n92.25\n93.38\n92.90\n93.31\n93.64\n93.47\n93.31\n93.38\n93.61\n93.65\n\nTable 2. Comparison of various channel attention modules using\nResNet-50 as backbone model on ImageNet. #.Param. indicates\nnumber of parameters of the channel attention module; (cid:12) indicates\nelement-wise product; GC and C1D indicate group convolutions\nand 1D convolution, respectively; k is kernel size of C1D.\n\ny = g(X ), f{W1,W2} takes the form\n\nf{W1,W2}(y) = W2ReLU(W1y),\n\n(2)\n\nr ) and ( C\n\nwhere ReLU indicates the Recti\ufb01ed Linear Unit [25]. To\navoid high model complexity, sizes of W1 and W2 are\nset to C \u00d7 ( C\nr ) \u00d7 C, respectively. We can see\nthat f{W1,W2} involves all parameters of channel attention\nblock. While dimensionality reduction in Eq. (2) can reduce\nmodel", "doc_id": "c92348af-a427-4ac5-8377-a2d69a12fdce", "embedding": null, "doc_hash": "0a28b272c0188dac0a703f6c101260ff7739bc582b60ec579d8227f32438245f", "extra_info": null, "index": 6, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 11898, "end": 13133}, "image": null}, "2755801811331838464": {"text": "While dimensionality reduction in Eq. (2) can reduce\nmodel complexity, it destroys the direct correspondence be-\ntween channel and its weight. For example, one single FC\nlayer predicts weight of each channel using a linear com-\nbination of all channels. But Eq. (2) \ufb01rst projects chan-\nnel features into a low-dimensional space and then maps\nthem back, making correspondence between channel and\nits weight be indirect.\n\n3.2. Ef\ufb01cient Channel Attention (ECA) Module\n\nAfter revisiting SE block, we conduct empirical com-\nparisons for analyzing effects of channel dimensionality re-\nduction and cross-channel interaction on channel attention\nlearning. According to these analyses, we propose our ef\ufb01-\ncient channel attention (ECA) module.\n\n3.2.1 Avoiding Dimensionality Reduction\n\nAs discussed above, dimensionality reduction in Eq. (2)\nmakes correspondence between channel and its weight be\nindirect. To verify its effect, we compare the original SE\nblock with its three variants (i.e., SE-Var1, SE-Var2 and SE-\nVar3), all of which do not perform dimensionality reduc-\ntion. As presented in Table 2, SE-Var1 with no parameter\nis still superior to the original network, indicating channel\nattention has ability to improve performance of deep CNNs.\nMeanwhile, SE-Var2 learns the weight of each channel in-\ndependently, which is slightly superior to SE block while\ninvolving less parameters. It may suggest that channel and\nits weight needs a direct correspondence while avoiding di-\n\n\f\nmensionality reduction is more important than considera-\ntion of nonlinear channel dependencies. Additionally, SE-\nVar3 employing one single FC layer performs better than\ntwo FC layers with dimensionality reduction in SE block.\nAll of above results clearly demonstrate avoiding dimen-\nsionality reduction is helpful to learn effective channel at-\ntention. Therefore, we develop our ECA module without\nchannel dimensionality reduction.\n\nbring no gain over SE-Var2, indicating it is not an effective\nscheme to capture local cross-channel interaction. The rea-\nson may be that SE-GC completely discards dependences\namong different groups.\n\nIn this paper, we explore another method to capture local\ncross-channel interaction, aiming at guaranteeing both ef-\n\ufb01ciency and effectiveness.", "doc_id": "9ff4e5f1-d3d3-47d5-b6de-1177fd7a4df6", "embedding": null, "doc_hash": "a0fdee5826342d21d8943fe1315f25b317cddb8f4d7680215bf7dc91ee888e7e", "extra_info": null, "index": 7, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 13103, "end": 15365}, "image": null}, "109593825169184541": {"text": "aiming at guaranteeing both ef-\n\ufb01ciency and effectiveness. Speci\ufb01cally, we employ a band\nmatrix Wk to learn channel attention, and Wk has\n\n3.2.2 Local Cross-Channel Interaction\n\nGiven the aggregated feature y \u2208 RC without dimensional-\nity reduction, channel attention can be learned by\n\n\u03c9 = \u03c3(Wy),\n\n(3)\n\nwhere W is a C \u00d7 C parameter matrix. In particular, for\nSE-Var2 and SE-Var3 we have\n\nW =\n\n\uf8f1\n\n\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f2\n\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f3\n\nWvar2 =\n\nWvar3 =\n\n\uf8ee\n\n\uf8ef\n\uf8f0\n\n\uf8ee\n\n\uf8ef\n\uf8f0\n\nw1,1\n...\n0\nw1,1\n...\n\n0\n\u00b7 \u00b7 \u00b7\n...\n. . .\n. . . wC,C\n\u00b7 \u00b7 \u00b7 w1,C\n...\n. . .\nw1,C . . . wC,C\n\n\uf8f9\n\n\uf8fa\n\uf8fb ,\n\n(4)\n\n\uf8f9\n\n\uf8fa\n\uf8fb ,\n\nwhere Wvar2 for SE-Var2 is a diagonal matrix, involving C\nparameters; Wvar3 for SE-Var3 is a full matrix, involving\nC\u00d7C parameters. As shown in Eq. (4), the key difference is\nthat SE-Var3 considers cross-channel interaction while SE-\nVar2 does not, and consequently SE-Var3 achieves better\nperformance. This result indicates that cross-channel inter-\naction is bene\ufb01cial to learn channel attention. However, SE-\nVar3 requires a mass of parameters, leading to high model\ncomplexity, especially for large channel numbers.\n\nA possible compromise between SE-Var2 and SE-Var3\n\nis extension of Wvar2 to a block diagonal matrix, i.e.,\n\nWG =\n\n\uf8ee\n\n\uf8ef\n\uf8f0\n\nW1\n...\n0\n\nG \u00b7 \u00b7 \u00b7\n0\n...\n. . .\n. . . WG\nG\n\n\uf8f9\n\n\uf8fa\n\uf8fb", "doc_id": "dd64a7bf-dc79-4812-a039-09787ef0e24c", "embedding": null, "doc_hash": "61148a065ffec1484d36ea887cf8b9bb40f9995e3f451e764312d561104b3011", "extra_info": null, "index": 8, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 15365, "end": 16621}, "image": null}, "7261673482884363735": {"text": ". . WG\nG\n\n\uf8f9\n\n\uf8fa\n\uf8fb ,\n\n(5)\n\nwhere Eq. (5) divides channel into G groups each of which\nincludes C/G channels, and learns channel attention in each\ngroup independently, which captures cross-channel interac-\ntion in a local manner. Accordingly, it involves C 2/G pa-\nrameters. From perspective of convolution, SE-Var2, SE-\nVar3 and Eq. (5) can be regarded as a depth-wise separa-\nble convolution, a FC layer and group convolutions, respec-\ntively. Here, SE block with group convolutions (SE-GC) is\nindicated by \u03c3(GCG(y)) = \u03c3(WGy). However, as shown\nin [24], excessive group convolutions will increase memory\naccess cost and so decrease computational ef\ufb01ciency. Fur-\nthermore, as shown in Table 2, SE-GC with varying groups\n\n.\n\n\uf8f9\n\n\uf8fa\n\uf8fa\n\uf8fa\n\uf8fb\n\n\uf8ee\n\n\uf8ef\n\uf8ef\n\uf8ef\n\uf8f0\n\n0\n0\n...\n\n\u00b7 \u00b7 \u00b7\n\u00b7 \u00b7 \u00b7\n...\n\nw1,1\n0\n...\n0\n\nw1,k\n\u00b7 \u00b7 \u00b7\n...\n0\n\n\u00b7 \u00b7 \u00b7\nw2,2\n...\n\u00b7 \u00b7 \u00b7\n\n0\nw2,k+1\n...\n0\n\n0\n0\n. . .\n\u00b7 \u00b7 \u00b7 wC,C\u2212k+1\n\n\u00b7 \u00b7 \u00b7\n\u00b7 \u00b7 \u00b7\n...\n\u00b7 \u00b7 \u00b7 wC,C\n(6)\nClearly, Wk in Eq. (6) involves k \u00d7 C parameters, which\nis usually less than those of Eq. (5). Furthermore, Eq. (6)\navoids complete independence among different groups in\nEq. (5). As compared in Table 2, the method in Eq. (6)\n(namely ECA-NS) outperforms SE-GC of Eq. (5). As for\nEq. (6), the weight of yi is calculated by only considering\ninteraction between yi and its k neighbors, i.e.,\n\n\u03c9i = \u03c3\n\n(cid:18) k\n\n(cid:88)\n\n(cid:19)\n\ni yj\nwj\n\ni\n\n, yj\n\ni \u2208", "doc_id": "d4b98c90-5589-44eb-9189-6efdd34950a4", "embedding": null, "doc_hash": "32163bff861bcb266bc3d6c2eecbe99b868652597b879cd45ce065ff951eb9d3", "extra_info": null, "index": 9, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 16660, "end": 18009}, "image": null}, "4010341747361088511": {"text": "yj\nwj\n\ni\n\n, yj\n\ni \u2208 \u2126k\ni ,\n\nj=1\n\n(7)\n\nwhere \u2126k\n\ni indicates the set of k adjacent channels of yi.\nA more ef\ufb01cient way is to make all channels share the\n\nsame learning parameters, i.e.,\n\n\u03c9i = \u03c3\n\n(cid:18) k\n\n(cid:88)\n\n(cid:19)\n\nwjyj\ni\n\n, yj\n\ni \u2208 \u2126k\ni .\n\nj=1\n\n(8)\n\nNote that such strategy can be readily implemented by a fast\n1D convolution with kernel size of k, i.e.,\n\n\u03c9 = \u03c3(C1Dk(y)),\n\n(9)\n\nwhere C1D indicates 1D convolution. Here, the method in\nEq. (9) is called by ef\ufb01cient channel attention (ECA) mod-\nule, which only involves k parameters. As presented in Ta-\nble 2, our ECA module with k = 3 achieves similar results\nwith SE-var3 while having much lower model complexity,\nwhich guarantees both ef\ufb01ciency and effectiveness by ap-\npropriately capturing local cross-channel interaction.\n\n3.2.3 Coverage of Local Cross-Channel Interaction\n\nSince our ECA module (9) aims at appropriately capturing\nlocal cross-channel interaction, so the coverage of interac-\ntion (i.e., kernel size k of 1D convolution) needs to be de-\ntermined. The optimized coverage of interaction could be\ntuned manually for convolution blocks with different chan-\nnel numbers in various CNN architectures. However, man-\nual tuning via cross-validation will cost a lot of comput-\ning resources. Group convolutions have been successfully\n\n\f\napplying our ECA to deep CNNs, we replace SE block by\nour ECA module following the same con\ufb01guration in [14].\nThe resulting networks are named by ECA-Net. Figure 3\ngives PyTorch code of our ECA.\n\n4. Experiments\n\nIn this section, we evaluate the proposed method\non large-scale image classi\ufb01cation, object detection and\n[6] and MS\ninstance segmentation using ImageNet\nCOCO [23], respectively. Speci\ufb01cally, we \ufb01rst assess the\neffect of kernel", "doc_id": "eb07662e-746e-4a86-9777-7f7336a16474", "embedding": null, "doc_hash": "905cb220371e2fe37e754f584046b438c60a306dd9a798e803a5f7da28cb0399", "extra_info": null, "index": 10, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 18007, "end": 19757}, "image": null}, "2806378935951439626": {"text": "we \ufb01rst assess the\neffect of kernel size on our ECA module, and compare with\nstate-of-the-art counterparts on ImageNet. Then, we ver-\nify the effectiveness of our ECA-Net on MS COCO using\nFaster R-CNN [26], Mask R-CNN [10] and RetinaNet [22].\n\nFigure 3. PyTorch code of our ECA module.\n\n4.1. Implementation Details\n\nadopted to improve CNN architectures [37, 34, 16], where\nhigh-dimensional (low-dimensional) channels involve long\nrange (short range) convolutions given the \ufb01xed number of\ngroups. Sharing the similar philosophy, it is reasonable that\nthe coverage of interaction (i.e., kernel size k of 1D con-\nvolution) is proportional to channel dimension C. In other\nwords, there may exist a mapping \u03c6 between k and C:\n\nC = \u03c6(k).\n\n(10)\n\nThe simplest mapping is a linear function, i.e., \u03c6(k) =\n\u03b3 \u2217 k \u2212 b. However, the relations characterized by linear\nfunction are too limited. On the other hand, it is well known\nthat channel dimension C (i.e., number of \ufb01lters) usually is\nset to power of 2. Therefore, we introduce a possible solu-\ntion by extending the linear function \u03c6(k) = \u03b3 \u2217 k \u2212 b to a\nnon-linear one, i.e.,\n\nC = \u03c6(k) = 2(\u03b3\u2217k\u2212b).\n\n(11)\n\nThen, given channel dimension C, kernel size k can be\nadaptively determined by\n\nk = \u03c8(C) =\n\n(cid:12)\n(cid:12)\n(cid:12)\n(cid:12)\n\nlog2(C)\n\u03b3\n\n+\n\nb\n\u03b3\n\n(cid:12)\n(cid:12)\n(cid:12)\n(cid:12)odd\n\n,\n\n(12)\n\nwhere |t|odd indicates the nearest odd number of t. In this\npaper, we set \u03b3 and b to 2 and 1 throughout all the experi-\nments, respectively. Clearly, through the mapping \u03c8, high-\ndimensional channels have longer range interaction while\nlow-dimensional ones undergo shorter range interaction by\nusing a non-linear mapping.\n\n3.3. ECA Module for Deep CNNs\n\nFigure 2", "doc_id": "94ee4d22-e1f3-4878-94f9-c36f9c1a50db", "embedding": null, "doc_hash": "4b66fa06b2a6238db0e7603ebcb490722205541376e09050fdf3975c13d984bd", "extra_info": null, "index": 11, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 19745, "end": 21451}, "image": null}, "6439704467523944253": {"text": "mapping.\n\n3.3. ECA Module for Deep CNNs\n\nFigure 2 illustrates the overview of our ECA module. Af-\nter aggregating convolution features using GAP without di-\nmensionality reduction, ECA module \ufb01rst adaptively deter-\nmines kernel size k, and then performs 1D convolution fol-\nlowed by a Sigmoid function to learn channel attention. For\n\nTo evaluate our ECA-Net on ImageNet classi\ufb01cation, we\nemploy four widely used CNNs as backbone models, in-\ncluding ResNet-50 [11], ResNet-101 [11], ResNet-512 [11]\nand MobileNetV2 [28]. For training ResNets with our\nECA, we adopt exactly the same data augmentation and\nhyper-parameter settings in [11, 14]. Speci\ufb01cally, the input\nimages are randomly cropped to 224\u00d7224 with random hor-\nizontal \ufb02ipping. The parameters of networks are optimized\nby stochastic gradient descent (SGD) with weight decay of\n1e-4, momentum of 0.9 and mini-batch size of 256. All\nmodels are trained within 100 epochs by setting the initial\nlearning rate to 0.1, which is decreased by a factor of 10\nper 30 epochs. For training MobileNetV2 with our ECA,\nwe follow the settings in [28], where networks are trained\nwithin 400 epochs using SGD with weight decay of 4e-5,\nmomentum of 0.9 and mini-batch size of 96. The initial\nlearning rate is set to 0.045, and is decreased by a linear\ndecay rate of 0.98. For testing on the validation set, the\nshorter side of an input image is \ufb01rst resized to 256 and a\ncenter crop of 224 \u00d7 224 is used for evaluation. All models\nare implemented by PyTorch toolkit1.\n\nWe further evaluate our method on MS COCO using\nFaster R-CNN [26], Mask R-CNN [10] and RetinaNet [22],\nwhere ResNet-50 and ResNet-101 along with FPN [21] are\nused as backbone models. We implement all detectors by\nusing MMDetection toolkit [3] and employ the default set-\ntings. Speci\ufb01cally, the shorter side of input images are re-\nsized to 800, then all models are optimized using SGD with\nweight decay of 1e-4, momentum of", "doc_id": "9a928222-e0cf-46df-a8c7-aa6327a1c6be", "embedding": null, "doc_hash": "1ec07b684ba4fa13e4db666598a03976638b69cc795f48a022de406ecf2be239", "extra_info": null, "index": 12, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 21439, "end": 23373}, "image": null}, "8119335398431058798": {"text": "optimized using SGD with\nweight decay of 1e-4, momentum of 0.9 and mini-batch size\nof 8 (4 GPUs with 2 images per GPU). The learning rate\nis initialized to 0.01 and is decreased by a factor of 10 af-\nter 8 and 11 epochs, respectively. We train all detectors\nwithin 12 epochs on train2017 of COCO and report the re-\nsults on val2017 for comparison. All programs run on a\nPC equipped with four RTX 2080Ti GPUs and an Intel(R)\n\n1https://github.com/BangguWu/ECANet\n\n\f\nMethod\nResNet [11]\nSENet [14]\nCBAM [33]\nA2-Nets [4]\u2020\nGCNet [2]\nGSoP-Net1 [9]\nAA-Net [1]\u2020,\u2666\nECA-Net (Ours)\nResNet [11]\nSENet [14]\nCBAM [33]\nAA-Net [1]\u2020,\u2666\nECA-Net (Ours)\nResNet [11]\nSENet [14]\nECA-Net (Ours)\nMobileNetV2 [28]\nSENet\nECA-Net (Ours)\n\nBackbone Models\n\nResNet-50\n\nResNet-101\n\nResNet-152\n\nMobileNetV2\n\nFLOPs\n#.Param.\n3.86G\n24.37M\n3.87G\n26.77M\n3.87G\n26.77M\n6.50G\n33.00M\n3.87G\n28.08M\n6.18G\n28.05M\n4.15G\n25.80M\n3.86G\n24.37M\n7.34G\n42.49M\n7.35G\n47.01M\n7.35G\n47.01M\n8.05G\n45.40M\n42.49M\n7.35G\n57.40M 10.82G\n63.68M\n10.85G\n57.40M 10.83G\n3.34M\n3.40M\n3.34M\n\nTraining\n1024 FPS\n759 FPS\n472 FPS\nN/A\nN/A\n596 FPS\nN/A\n785 FPS\n386 FPS\n367 FPS\n270 FPS\nN/A\n380 FPS\n281 FPS\n268 FPS\n279 FPS\n319.4M 711 FPS\n671 FPS\n320.1M\n676", "doc_id": "924463ff-a97d-4bf9-aa51-3539063fbb64", "embedding": null, "doc_hash": "c582f79f6fb08c5216b5a2a1dac53dff26a2f527045ed3e8508cfa90d009794d", "extra_info": null, "index": 13, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 23367, "end": 24541}, "image": null}, "8088901492638068067": {"text": "711 FPS\n671 FPS\n320.1M\n676 FPS\n319.9M\n\nInference\n1855 FPS\n1620 FPS\n1213 FPS\nN/A\nN/A\n1383 FPS\nN/A\n1805 FPS\n1174 FPS\n1044 FPS\n635 FPS\nN/A\n1089 FPS\n815 FPS\n761 FPS\n785 FPS\n2086 FPS\n2000 FPS\n2010 FPS\n\nTop-1\n75.20\n76.71\n77.34\n77.00\n77.70\n77.68\n77.70\n77.48\n76.83\n77.62\n78.49\n78.70\n78.65\n77.58\n78.43\n78.92\n71.64\n72.42\n72.56\n\nTop-5\n92.52\n93.38\n93.69\n93.50\n93.66\n93.98\n93.80\n93.68\n93.48\n93.93\n94.31\n94.40\n94.34\n93.66\n94.27\n94.55\n90.20\n90.67\n90.81\n\nTable 3. Comparison of different attention methods on ImageNet in terms of network parameters (#.Param.), \ufb02oating point operations per\nsecond (FLOPs), training or inference speed (frame per second, FPS), and Top-1/Top-5 accuracy (in %). \u2020: Since the source code and\nmodels of A2-Nets and AA-Net are publicly unavailable, we do not compare their running time. \u2666: AA-Net is trained with Inception data\naugmentation and different setting of learning rates.\n\nXeon Silver 4112 CPU@2.60GHz.\n\n4.2. Image Classi\ufb01cation on ImageNet-1K\n\nHere, we \ufb01rst assess the effect of kernel size on our ECA\nmodule and verify the effectiveness of our method to adap-\ntively determine kernel size, then we compare with state-\nof-the-art counterparts and CNN models using ResNet-50,\nResNet-101, ResNet-152 and MobileNetV2.\n\n4.2.1 Effect of Kernel Size (k) on ECA Module\n\nAs shown in Eq. (9), our ECA module involves a parame-\nter k, i.e., kernel size of 1D convolution. In this part, we\nevaluate its effect on our ECA module and validate the ef-\nfectiveness of our method for adaptive selection of", "doc_id": "766386c4-2117-4104-bf6b-7a16b754606d", "embedding": null, "doc_hash": "0a6d304a0da7f86e6098c2d44d3573784490fe23226fa292c2fec711c75f90ab", "extra_info": null, "index": 14, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 24568, "end": 26079}, "image": null}, "3297019322072755872": {"text": "our ECA module and validate the ef-\nfectiveness of our method for adaptive selection of kernel\nsize. To this end, we employ ResNet-50 and ResNet-101\nas backbone models, and train them with our ECA module\nby setting k be from 3 to 9. The results are illustrated in\nFigure 4, from it we have the following observations.\n\nFirstly, when k is \ufb01xed in all convolution blocks, ECA\nmodule obtains the best results at k = 9 and k = 5 for\nResNet-50 and ResNet-101, respectively. Since ResNet-\n101 has more intermediate layers that dominate perfor-\nmance of ResNet-101, it may prefer to small kernel size.\nBesides, these results show that different deep CNNs have\nvarious optimal k, and k has a clear effect on performance\nof ECA-Net. Furthermore, accuracy \ufb02uctuations (\u223c0.5%)\nof ResNet-101 are larger than those (\u223c0.15%) of ResNet-\n50, and we conjecture the reason is that the deeper net-\n\nFigure 4. Results of our ECA module with various numbers of k\nusing ResNet-50 and ResNet-101 as backbone models. Here, we\nalso give the results of ECA module with adaptive selection of\nkernel size and compare with SENet as baseline.\n\nworks are more sensitive to the \ufb01xed kernel size than the\nshallower ones. Additionally, kernel size that is adaptively\ndetermined by Eq. (12) usually outperforms the \ufb01xed ones,\nwhile it can avoid manual tuning of parameter k via cross-\nvalidation. Above results demonstrate the effectiveness of\nour adaptive kernel size selection in attaining better and sta-\nble results. Finally, ECA module with various numbers of\nk consistently outperform SE block, verifying that avoid-\ning dimensionality reduction and local cross-channel inter-\naction have positive effects on learning channel attention.\n\n3579Number of k76.877.077.277.477.677.878.078.278.478.678.8Top-1 accuracySENet50SENet101ECA-Net50ECA-Net101ECA50-AdaptiveECA101-Adaptive\f\n4.2.2 Comparisons Using Different Deep CNNs\n\nResNet-50 We compare our ECA module with several\nstate-of-the-art attention methods using ResNet-50 on Im-\nageNet, including SENet [14], CBAM [33],", "doc_id": "58effe83-c714-4ff3-b324-0f857a59198e", "embedding": null, "doc_hash": "766582050b4ea478fb2cde241a1410cc09edbeed2853c01830acbb6abd4291ca", "extra_info": null, "index": 15, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 26030, "end": 28069}, "image": null}, "4055971687185696973": {"text": "on Im-\nageNet, including SENet [14], CBAM [33], A2-Nets [4],\nAA-Net [1], GSoP-Net1 [9] and GCNet [2]. The evalua-\ntion metrics include both ef\ufb01ciency (i.e., network param-\neters, \ufb02oating point operations per second (FLOPs) and\ntraining/inference speed) and effectiveness (i.e., Top-1/Top-\n5 accuracy). For comparison, we duplicate the results of\nResNet and SENet from [14], and report the results of other\ncompared methods in their original papers. To test train-\ning/inference speed of various models, we employ publicly\navailable models of the compared CNNs, and run them on\nthe same computing platform. The results are given in Ta-\nble 3, where we can see that our ECA-Net shares almost the\nsame model complexity (i.e., network parameters, FLOPs\nand speed) with the original ResNet-50, while achieving\n2.28% gains in Top-1 accuracy. Comparing with state-of-\nthe-art counterparts (i.e., SENet, CBAM, A2-Nets, AA-Net,\nGSoP-Net1 and GCNet), ECA-Net obtains better or com-\npetitive results while bene\ufb01ting lower model complexity.\nResNet-101 Using ResNet-101 as backbone model, we\ncompare our ECA-Net with SENet [14], CBAM [33] and\nAA-Net [1]. From Table 3 we can see that ECA-Net out-\nperforms the original ResNet-101 by 1.8% with almost\nthe same model complexity. Sharing the same tendency\non ResNet-50, ECA-Net is superior to SENet and CBAM\nwhile it is very competitive to AA-Net with lower model\ncomplexity. Note that AA-Net is trained with Inception data\naugmentation and different setting of learning rates.\nResNet-152 Using ResNet-152 as backbone model, we\ncompare our ECA-Net with SENet [14]. From Table 3 we\ncan see that ECA-Net improves the original ResNet-152\nover about 1.3% in terms of Top-1 accuracy with almost\nthe same model complexity. Comparing with SENet, ECA-\nNet achieves 0.5% gain in terms of Top-1 with lower model\ncomplexity. The results with respect to ResNet-50,", "doc_id": "185785ad-4b6b-47b8-8851-ec84704b730a", "embedding": null, "doc_hash": "430a39aeca86d4aa755a091d343cccf214ad440e9563a52c1a9a5ce070e8f460", "extra_info": null, "index": 16, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 28103, "end": 29989}, "image": null}, "6576751604826204792": {"text": "Top-1 with lower model\ncomplexity. The results with respect to ResNet-50, ResNet-\n101 and ResNet-152 demonstrate the effectiveness of our\nECA module on the widely used ResNet architectures.\nMobileNetV2 Besides ResNet architectures, we also verify\nthe effectiveness of our ECA module on lightweight CNN\narchitectures. To this end, we employ MobileNetV2 [28]\nas backbone model and compare our ECA module with SE\nblock. In particular, we integrate SE block and ECA mod-\nule in convolution layer before residual connection lying in\neach \u2019bottleneck\u2019 of MobileNetV2, and parameter r of SE\nblock is set to 8. All models are trained using exactly the\nsame settings. The results in Table 3 show our ECA-Net im-\nproves the original MobileNetV2 and SENet by about 0.9%\nand 0.14% in terms of Top-1 accuracy, respectively. Fur-\nthermore, our ECA-Net has smaller model size and faster\ntraining/inference speed than SENet. Above results verify\nthe ef\ufb01ciency and effectiveness of our ECA module again.\n\nCNN Models\nResNet-200\nInception-v3\nResNeXt-101\nDenseNet-264 (k=32)\nDenseNet-161 (k=48)\nECA-Net50 (Ours)\nECA-Net101 (Ours)\n\nFLOPs\n#.Param.\n74.45M 14.10G\n5.36G\n25.90M\n7.53G\n46.66M\n5.52G\n31.79M\n7.34G\n27.35M\n3.86G\n24.37M\n7.35G\n42.49M\n\nTop-1\n78.20\n77.45\n78.80\n77.85\n77.65\n77.48\n78.65\n\nTop-5\n94.00\n93.56\n94.40\n93.78\n93.80\n93.68\n94.34\n\nTable 4. Comparisons with state-of-the-art CNNs on ImageNet.\n\n4.2.3 Comparisons with Other CNN Models\n\nAt the end of this part, we compare our ECA-Net50 and\nECA-Net101 with other state-of-the-art CNN models, in-\ncluding ResNet-200 [12], Inception-v3 [31], ResNeXt [34],\nDenseNet [15]. These CNN models have deeper and", "doc_id": "d2bebe1c-4775-4565-bced-8c8369b32e71", "embedding": null, "doc_hash": "c644f3f6ca97bd910902be64175ca5d4061e5c32eee14c0cfc9e20ec467c6538", "extra_info": null, "index": 17, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 29967, "end": 31601}, "image": null}, "4590524478080288654": {"text": "[34],\nDenseNet [15]. These CNN models have deeper and wider\narchitectures, and their results all are copied from the orig-\ninal papers. As presented in Table 4, ECA-Net101 outper-\nforms ResNet-200, indicating that our ECA-Net can im-\nprove the performance of deep CNNs using much less com-\nputational cost. Meanwhile, our ECA-Net101 is very com-\npetitive to ResNeXt-101, while the latter one employs more\nconvolution \ufb01lters and expensive group convolutions. In ad-\ndition, ECA-Net50 is comparable to DenseNet-264 (k=32),\nDenseNet-161 (k=48) and Inception-v3, but it has lower\nmodel complexity. All above results demonstrate that our\nECA-Net performs favorably against state-of-the-art CNNs\nwhile bene\ufb01ting much lower model complexity. Note that\nour ECA also has great potential to further improve the per-\nformance of the compared CNN models.\n\n4.3. Object Detection on MS COCO\n\nIn this subsection, we evaluate our ECA-Net on object\ndetection task using Faster R-CNN [26], Mask R-CNN [10]\nand RetinaNet [22]. We mainly compare ECA-Net with\nResNet and SENet. All CNN models are pre-trained on Im-\nageNet, then are transferred to MS COCO by \ufb01ne-tuning.\n\n4.3.1 Comparisons Using Faster R-CNN\n\nUsing Faster R-CNN as the basic detector, we employ\nResNets of 50 and 101 layers along with FPN [21] as back-\nbone models. As shown in Table 5, integration of either\nSE block or our ECA module can improve performance of\nobject detection by a clear margin. Meanwhile, our ECA\noutperforms SE block by 0.3% and 0.7% in terms of AP\nusing ResNet-50 and ResNet-101, respectively.\n\n4.3.2 Comparisons Using Mask R-CNN\n\nWe further exploit Mask R-CNN to verify the effectiveness\nof our ECA-Net on object detection task. As shown in Ta-\nble 5, our ECA module is superior to the original ResNet\nby 1.8% and 1.9% in terms of AP under the settings of\n\n\f\nDetectors\n\nFaster R-CNN\n\nMask R-CNN\n\nRetinaNet\n\nMethods\nResNet-50\n+ SE", "doc_id": "0a759a04-09b3-4c33-97b2-56a401d2bd71", "embedding": null, "doc_hash": "872cae262fe7abcc99b9e2999b2f760b627e8d8e75d5e72f8eccde1ebe98e777", "extra_info": null, "index": 18, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 31620, "end": 33519}, "image": null}, "8764395071577595972": {"text": "R-CNN\n\nRetinaNet\n\nMethods\nResNet-50\n+ SE block\n+ ECA (Ours)\nResNet-101\n+ SE block\n+ ECA (Ours)\nResNet-50\n+ SE block\n+ 1 NL\n+ GC block\n+ ECA (Ours)\nResNet-101\n+ SE block\n+ ECA (Ours)\nResNet-50\n+ SE block\n+ ECA (Ours)\nResNet-101\n+ SE block\n+ ECA (Ours)\n\n#.Param.\n41.53 M\n44.02 M\n41.53 M\n60.52 M\n65.24 M\n60.52 M\n44.18 M\n46.67 M\n46.50 M\n46.90 M\n44.18 M\n63.17 M\n67.89 M\n63.17 M\n37.74 M\n40.23 M\n37.74 M\n56.74 M\n61.45 M\n56.74 M\n\nGFLOPs\n207.07\n207.18\n207.18\n283.14\n283.33\n283.32\n275.58\n275.69\n288.70\n279.60\n275.69\n351.65\n351.84\n351.83\n239.32\n239.43\n239.43\n315.39\n315.58\n315.57\n\nAP\n36.4\n37.7\n38.0\n38.7\n39.6\n40.3\n37.2\n38.7\n38.0\n39.4\n39.0\n39.4\n40.7\n41.3\n35.6\n37.1\n37.3\n37.7\n38.7\n39.1\n\nAP50 AP75", "doc_id": "0d64733b-b9e7-49ae-8e29-ff5740abc0cd", "embedding": null, "doc_hash": "3d6d15331b0f736afd7b5a5b9e1a4d076ea14a47fec2df142168e3f6e170dca8", "extra_info": null, "index": 19, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 33527, "end": 34210}, "image": null}, "3965071324088411256": {"text": "AP75 APS\n21.8\n39.2\n58.2\n22.9\n40.9\n60.1\n23.4\n40.9\n60.6\n22.7\n41.9\n60.6\n23.7\n43.1\n62.0\n24.5\n44.0\n62.9\n22.2\n40.3\n58.9\n23.4\n42.1\n60.9\nN/A\n41.0\n59.8\n42.4\n61.6\nN/A\n24.2\n42.1\n61.3\n23.0\n43.3\n60.9\n23.9\n44.3\n62.5\n25.1\n44.8\n63.1\n20.0\n38.2\n55.5\n39.9\n21.2\n57.2\n21.9\n57.7\n39.6\n21.1\n40.4\n57.5\n22.1\n41.6\n59.1\n22.8\n41.8\n59.9\n\nAPM APL\n46.2\n40.0\n48.2\n41.9\n42.1\n48.0\n50.4\n43.2\n51.4\n44.0\n44.7\n51.3\n48.0\n40.7\n50.0\n42.7\nN/A\nN/A\nN/A\nN/A\n42.8\n49.9\n51.4\n43.7\n52.8\n45.2\n52.9\n45.8\n46.8\n39.6\n49.3\n40.7\n41.3\n48.9\n49.5\n42.2\n50.9\n43.1\n43.4\n50.6\n\nTable 5. Object detection results of different methods on COCO val2017.\n\n50 and 101 layers, respectively. Meanwhile, ECA module\nachieves 0.3% and 0.6% gains over SE block using ResNet-\n50 and ResNet-101 as backbone models, respectively. Us-\ning ResNet-50, ECA is superior to one NL [32], and is com-\nparable to GC block [2] using lower model complexity.\n\n4.3.3 Comparisons Using RetinaNet\n\nAdditionally, we verify the effectiveness of our ECA-Net\non object detection using one-stage detector, i.e., RetinaNet.\nAs compared in Table 5, our ECA-Net outperforms the orig-\ninal ResNet by 1.8% and 1.4% in terms of", "doc_id": "fbfef278-f9cf-48f3-bebb-9244ff360dd2", "embedding": null, "doc_hash": "1948aaaffadb0db61f990a201e78d3f310668ec4d1df085fbf044494c21551c9", "extra_info": null, "index": 20, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 34246, "end": 35367}, "image": null}, "4486406959406981854": {"text": "the orig-\ninal ResNet by 1.8% and 1.4% in terms of AP for the net-\nworks of 50 and 101 layers, respectively. Meanwhile, ECA-\nNet improves SE-Net over 0.2% and 0.4% for ResNet-50\nand ResNet-101, respectively. In summary, the results in\nTable 5 demonstrate that our ECA-Net can well generalize\nto object detection task. Speci\ufb01cally, ECA module brings\nclear improvement over the original ResNet, while outper-\nforming SE block using lower model complexity. In par-\nticular, our ECA module achieves more gains for small ob-\njects, which are usually more dif\ufb01cult to be detected.\n\n4.4. Instance Segmentation on MS COCO\n\nThen, we give instance segmentation results of our ECA\nmodule using Mask R-CNN on MS COCO. As compared in\nTable 6, ECA module achieves notable gain over the origi-\nnal ResNet while performing better than SE block with less\nmodel complexity. For ResNet-50 as backbone, ECA with\nlower model complexity is superior one NL [32], and is\ncomparable to GC block [2]. These results verify our ECA\nmodule has good generalization ability for various tasks.\n\nMethods\nResNet-50\n+ SE block\n+ 1 NL\n+ GC block\n+ ECA (Ours)\nResNet-101\n+ SE block\n+ ECA (Ours)\n\nAP\n34.1\n35.4\n34.7\n35.7\n35.6\n35.9\n36.8\n37.4\n\nAP50 AP75 APS\n16.1\n36.2\n55.5\n37.8\n17.1\n57.4\nN/A\n36.6\n56.7\n58.4\nN/A\n37.6\n17.6\n37.7\n58.1\n16.8\n38.4\n57.7\n17.2\n39.2\n59.3\n18.1\n39.8\n59.9\n\nAPM APL\n50.0\n36.7\n51.8\n38.6\nN/A\nN/A\nN/A\nN/A\n51.8\n39.0\n53.6\n39.1\n53.6\n40.3\n54.1\n41.1\n\nTable 6. Instance segmentation results of different methods using\nMask R-CNN on COCO val2017.\n\n5. Conclusion\n\nIn this paper, we focus on learning", "doc_id": "72e29a39-e340-4a97-bf7f-26ded43ab75c", "embedding": null, "doc_hash": "d3c7b10281333f0b37de9db851b14fe5822b0b2719e34cb8ccada580bb5aea2d", "extra_info": null, "index": 21, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 35331, "end": 36897}, "image": null}, "6063798155724132220": {"text": "val2017.\n\n5. Conclusion\n\nIn this paper, we focus on learning effective channel at-\ntention for deep CNNs with low model complexity. To\nthis end, we propose an ef\ufb01cient channel attention (ECA)\nmodule, which generates channel attention through a fast\n1D convolution, whose kernel size can be adaptively de-\ntermined by a non-linear mapping of channel dimension.\nExperimental results demonstrate our ECA is an extremely\nlightweight plug-and-play block to improve the perfor-\nmance of various deep CNN architectures, including the\nwidely used ResNets and lightweight MobileNetV2. More-\nover, our ECA-Net exhibits good generalization ability in\nobject detection and instance segmentation tasks. In future,\nwe will apply our ECA module to more CNN architectures\n(e.g., ResNeXt and Inception [31]) and further investigate\nincorporation of ECA with spatial attention module.\n\n\f\nReferences\n\n[1] Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens,\nand Quoc V. Le. Attention augmented convolutional net-\nworks. arXiv:1904.09925, 2019.\n\n[2] Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, and Han\nHu. Gcnet: Non-local networks meet squeeze-excitation net-\nworks and beyond. In ICCV Workshops, 2019.\n\n[3] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu\nXiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei\nLiu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu,\nTianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu,\nYue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli\nOuyang, Chen Change Loy, and Dahua Lin. 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Channel locality block: A variant of squeeze-and-\n\nexcitation. arXiv, 1901.01493, 2019.\n\n[19] Peihua Li, Jiangtao Xie, Qilong Wang, and Wangmeng Zuo.\nIs second-order information helpful for large-scale visual\nrecognition? In ICCV, 2017.\n\n[20] Yanghao Li, Naiyan Wang, Jiaying Liu, and Xiaodi Hou.\nFactorized bilinear models for image recognition. In ICCV,\n2017.\n\n[21] Tsung-Yi Lin, Piotr Doll\u00b4ar, Ross B. Girshick, Kaiming He,\nBharath Hariharan, and Serge J. Belongie. Feature pyramid\nIn CVPR, pages 936\u2013944,\nnetworks for object detection.\n2017.\n\n[22] Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He,\nand Piotr Doll\u00b4ar. Focal loss for dense object detection. In\nICCV, 2017.\n\n[23] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays,\nPietro Perona, Deva Ramanan, Piotr Doll\u00b4ar, and C Lawrence\nZitnick. Microsoft COCO: Common objects in context. In\nECCV, 2014.\n\n[24] Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun.\nShuf\ufb02enet V2: Practical guidelines for ef\ufb01cient CNN archi-\ntecture design. In ECCV, 2018.\n\n[25] Vinod Nair and Geoffrey E. Hinton. Recti\ufb01ed linear units\nimprove restricted boltzmann machines. In ICML, 2010.\n[26] Shaoqing", "doc_id": "1c9493d0-92ba-4d9c-8c09-7261f0c99fda", "embedding": null, "doc_hash": "44539fd3eee42248a0c561107f86595e7c7b993bd965626abd062eaae248ec6a", "extra_info": null, "index": 24, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 40127, "end": 41630}, "image": null}, "2521010997772552918": {"text": "machines. In ICML, 2010.\n[26] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun.\nFaster R-CNN: Towards real-time object detection with re-\nIEEE Trans. Pattern Anal. Mach.\ngion proposal networks.\nIntell., 39(6):1137\u20131149, 2017.\n\n[27] Abhijit Guha Roy, Nassir Navab, and Christian Wachinger.\nRecalibrating fully convolutional networks with spatial and\nchannel \u201dsqueeze and excitation\u201d blocks. IEEE Trans. Med.\nImaging, 38(2):540\u2013549, 2019.\n\n[28] Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey\nZhmoginov, and Liang-Chieh Chen. Mobilenetv2: Inverted\nresiduals and linear bottlenecks. In CVPR, 2018.\n\n[29] K. Simonyan and A. Zisserman. Very deep convolutional\nnetworks for large-scale image recognition. In ICLR, 2015.\n[30] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet,\nScott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent\nVanhoucke, and Andrew Rabinovich. Going deeper with\nconvolutions. In CVPR, 2015.\n\n[31] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe,\nJonathon Shlens, and Zbigniew Wojna. Rethinking the in-\nception architecture for computer vision. In CVPR, 2016.\n\n[32] Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaim-\ning He. Non-local neural networks. In CVPR, 2018.\n[33] Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So\nKweon. CBAM: Convolutional block attention module. In\nECCV, 2018.\n\n[34] Saining Xie, Ross B. Girshick, Piotr Doll\u00b4ar, Zhuowen Tu,\nand Kaiming He. Aggregated residual transformations for\ndeep neural networks. In CVPR, 2017.\n\n[35] Matthew D. Zeiler and Rob Fergus. Visualizing and under-\nstanding convolutional networks. In ECCV, pages", "doc_id": "56ba3f0b-59da-4574-9614-a140ea967c6e", "embedding": null, "doc_hash": "d38a41579ed26e9be3dd6a67919a92a265d8a092f89cc89cc42169bed61830a3", "extra_info": null, "index": 25, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 41630, "end": 43231}, "image": null}, "2434882091087989807": {"text": "Visualizing and under-\nstanding convolutional networks. In ECCV, pages 818\u2013833,\n2014.\n\n[36] Dong-Qing Zhang. Clcnet: Improving the ef\ufb01ciency of con-\nvolutional neural network using channel local convolutions.\nIn CVPR, 2018.\n\n\f\nMethod\nResNet [11]\nSENet [14]\nCBAM [33]\nECA-Net (Ours)\nResNet [11]\nSENet [14]\nCBAM [33]\nECA-Net (Ours)\n\nCNNs\n\nR-18\n\nR-34\n\n#.Param.\n11.148M\n11.231M\n11.234M\n11.148M\n20.788M\n20.938M\n20.943M\n20.788M\n\nGFLOPs\n1.699\n1.700\n1.700\n1.700\n3.427\n3.428\n3.428\n3.428\n\nTop-1\n70.40\n70.59\n70.73\n70.78\n73.31\n73.87\n74.01\n74.21\n\nTop-5\n89.45\n89.78\n89.91\n89.92\n91.40\n91.65\n91.76\n91.83\n\nTable 7. Comparison of different methods using ResNet-18 (R-18)\nand ResNet-34 (R-34) on ImageNet in terms of network parame-\nters (#.Param.), \ufb02oating point operations per second (FLOPs), and\nTop-1/Top-5 accuracy (in %).\n\n[37] Ting Zhang, Guo-Jun Qi, Bin Xiao, and Jingdong Wang. In-\n\nterleaved group convolutions. In ICCV, 2017.\n\n[38] Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun.\nShuf\ufb02enet: An extremely ef\ufb01cient convolutional neural net-\nwork for mobile devices. In CVPR, 2018.\n\nAppendix I: Comparison of Different Methods\nusing ResNet-18 and ResNet-34 on ImageNet\n\nHere, we compare different attention methods using\nResNet-18 and ResNet-34 on ImageNet. The results are\nlisted in Table 7, where the results of ResNet, SENet and\nCBAM are duplicated from [33], and we train ECA-Net us-\ning the settings of hyper-parameters with", "doc_id": "170b21d4-8312-4a01-ae8b-3dc8a56661e4", "embedding": null, "doc_hash": "be23c532059f163ae94a56b4275958434a5b27a94434525a21cc0413ac09672d", "extra_info": null, "index": 26, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 43203, "end": 44628}, "image": null}, "7438774493274053670": {"text": "and we train ECA-Net us-\ning the settings of hyper-parameters with [33]. From Ta-\nble 7, we can see that our ECA-Net improves the original\nResNet-18 and ResNet-34 over 0.38% abd 0.9% in Top-1\naccuracy, respectively. Comparing with SENet and CBAM,\nour ECA-Net achieves better performance using less model\ncomplexity, showing the effectiveness of the proposed ECA\nmodule.\n\nAppendix II: Stacking More 1D Convolutions\nin ECA Module\n\nIntuitively, more 1D convolutions stacked in ECA mod-\nule may bring further improvement, due to increase of mod-\neling capability. Actually, we found that one extra 1D con-\nvolution brings trivial gains (\u223c0.1%) at the cost of slightly\nincreasing complexity, but more 1D convolutions degrade\nperformance, which may be caused by that more 1D con-\nvolutions make gradient backpropagation more dif\ufb01cult.\nTherefore, our \ufb01nal ECA module contains only one 1D con-\nvolution.\n\nAppendix\nLearned by ECA Modules and SE Blocks\n\nIII: Visualization of Weights\n\nFigure 5. Example images of four random sampled classes on Im-\nageNet, including hammerhead shark, ambulance, medicine chest\nand butternut squash.\n\nby ECA modules and compare with SE blocks. Here,\nwe employ ResNet-50 as backbone model, and illus-\ntrate weights of different convolution blocks. Speci\ufb01cally,\nwe randomly sample four classes from ImageNet dataset,\nwhich are hammerhead shark, ambulance, medicine chest\nand butternut squash, respectively. Some example images\nare illustrated in Figure 5. After training the networks, for\nall images of each class collected from validation set of\nImageNet, we compute the channel weights of convolution\nblocks on average. Figure 6 visualizes the channel weights\nof conv i j, which indicates j-th convolution block in i-\nth stage. Besides the visualization results of four random\nsampled classes, we also give the distribution of the aver-\nage weights across 1K classes as reference. The channel\nweights learned by ECA modules and SE blocks are illus-\ntrated in bottom and top of each row, respectively.\n\nFrom Figure 6 we have the following observations.\nFirstly, for both ECA modules and SE blocks, the distri-\nbutions of channel", "doc_id": "dbcbbe86-0ac1-4c76-a5a8-0aed3cc3e2b8", "embedding": null, "doc_hash": "b977716689f1e6bd6389a4e2f8365cbc549690bc1992be49b567936763eff2bf", "extra_info": null, "index": 27, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 44635, "end": 46784}, "image": null}, "5335432363228342882": {"text": "for both ECA modules and SE blocks, the distri-\nbutions of channel weights for different classes are very\nsimilar at the earlier layers (i.e., ones from conv 2 1 to\nconv 3 4), which may be by reason of that the earlier layers\naim at capturing the basic elements (e.g., boundaries and\ncorners) [35]. These features are almost similar for differ-\nent classes. Such phenomenon also was described in the ex-\ntended version of [14]2. Secondly, for the channel weights\nof different classes learned by SE blocks, most of them tend\nto be the same (i.e., 0.5) in conv 4 2 \u223c conv 4 5 while the\ndifferences among various classes are not obvious. On the\ncontrary, the weights learned by ECA modules are clearly\ndifferent across various channels and classes. Since convo-\nlution blocks in 4-th stage prefer to learn semantic infor-\nmation, so the weights learned by ECA modules can better\ndistinguish different classes. Finally, convolution blocks in\nthe \ufb01nal stage (i.e., conv 5 1, conv 5 2 and conv 5 3) cap-\nture high-level semantic features and they are more class-\n\nTo further analyze the effect of our ECA module on\nlearning channel attention, we visualize the weights learned\n\n2https://arxiv.org/abs/1709.01507\n\nhammer sharkambulancemedicine chestbutternut squash\f\n(a) conv 2 1\n\n(b) conv 2 2\n\n(c) conv 2 3\n\n(d) conv 3 1\n\n(e) conv 3 2\n\n(f) conv 3 3\n\n(g) conv 3 4\n\n(h) conv 4 1\n\n(i) conv 4 2\n\n(j) conv 4 3\n\n(k) conv 4 4\n\n(l) conv 4 5\n\n(m) conv 4 6\n\n(n) conv 5 1\n\n(o) conv 5 2\n\n(p) conv 5 3\n\nFigure 6. Visualization the channel weights of conv i j, where i indicate i-th stage and j is j-th convolution block in i-th stage. The\nchannel weights learned by ECA modules and SE blocks are illustrated in bottom and top of each row, respectively. Better view with\nzooming in.\n\n050100150200250Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead", "doc_id": "1bb7d702-9374-4351-bcad-cf69301f102b", "embedding": null, "doc_hash": "761f4cd71d2104dc9de745e9d25ef2e266dbf47cc404e2d0514fee78f2944085", "extra_info": null, "index": 28, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 46786, "end": 48626}, "image": null}, "2174270469571096791": {"text": "sharkambulancemedicine chestbutternut squashall050100150200250Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall050100150200250Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall050100150200250Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall050100150200250Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall050100150200250Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0100200300400500Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0100200300400500Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0100200300400500Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0100200300400500Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0100200300400500Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0100200300400500Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0100200300400500Channel", "doc_id": "2a67f95d-85a9-4daf-8d48-d982e6bf475f", "embedding": null, "doc_hash": "5cfba50e5ae941b25206400fe358649d5c43f95adb4ca06d91ba285fbbdcbd52", "extra_info": null, "index": 29, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 48683, "end": 50134}, "image": null}, "2824740344406387108": {"text": "chestbutternut squashall0100200300400500Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0100200300400500Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine", "doc_id": "36ec9ea6-5348-413a-a567-c7af43187ce7", "embedding": null, "doc_hash": "33ef4491156ef7f504839b94ee508758ac67517c6a3384c0f1123310b73ee127", "extra_info": null, "index": 30, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 50089, "end": 51610}, "image": null}, "691420121717569527": {"text": "sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall02004006008001000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0500100015002000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0500100015002000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0500100015002000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0500100015002000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0500100015002000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall0500100015002000Channel Index0.00.10.20.30.40.50.60.70.80.91.0Weighthammerhead sharkambulancemedicine chestbutternut squashall\f\nspeci\ufb01c. Obviously, the weights learned by ECA modules\nare more class-speci\ufb01c than ones learned by SE blocks.\nAbove results clearly demonstrate that the weights learned\nby our ECA modules have better discriminative ability.\n\n\f", "doc_id": "3ef48f1f-8b6f-4408-89db-ca05771b2110", "embedding": null, "doc_hash": "3a9c4e0a990772a4e15acd7926e1122a157af56df04e3cf01baeb0b654512663", "extra_info": null, "index": 31, "child_indices": [], "ref_doc_id": "e5a98e47-29cd-4adb-b502-cdb64cdb2e4f", "node_info": {"start": 51634, "end": 52919}, "image": null}}, "id_map": {"bb285881-1d0a-4fe2-aabd-a7d00c63d104": 7296817700772434305, "986a5a6d-6fd7-4a4e-9015-b49c67106f30": 5729012705767151825, "c1807ac0-ec6b-4d99-bfc4-93315e3a44c8": 5973770463335275742, "c60a9b1e-1bd3-4ef0-a9fb-c1fa740ecf95": 1576839141285306667, "77d656f7-77df-4a87-bee5-f6ca8b580eae": 5672338402144338994, "a2c71ab6-9a8c-4fb5-8d11-a4696a6240c8": 5087516292114383165, "9466eaa1-1a21-4b24-8e6e-c1af1484f3ae": 5711748660893466706, 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