Spaces:
Sleeping
Sleeping
| % Encoding: UTF-8 | |
| % COME15K CMINet | |
| @Article{cascaded_rgbd_sod, | |
| title={RGB-D Saliency Detection via Cascaded Mutual Information Minimization}, | |
| author={Zhang, Jing and Fan, Deng-Ping and Dai, Yuchao and Yu, Xin and Zhong, Yiran and Barnes, Nick and Shao, Ling}, | |
| booktitle={International Conference on Computer Vision (ICCV)}, | |
| year={2021} | |
| } | |
| % A2dele | |
| @Article{piao2020a2dele, | |
| title={A2dele: Adaptive and attentive depth distiller for efficient RGB-D salient object detection}, | |
| author={Piao, Yongri and Rong, Zhengkun and Zhang, Miao and Ren, Weisong and Lu, Huchuan}, | |
| booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, | |
| pages={9060--9069}, | |
| year={2020} | |
| } | |
| % BBS-Net | |
| @Article{fan2020bbs, | |
| title={BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network}, | |
| author={Fan, Deng-Ping and Zhai, Yingjie and Borji, Ali and Yang, Jufeng and Shao, Ling}, | |
| booktitle={European conference on computer vision}, | |
| pages={275--292}, | |
| year={2020}, | |
| organization={Springer} | |
| } | |
| % MobileSal | |
| @article{wu2021mobilesal, | |
| title={MobileSal: Extremely efficient RGB-D salient object detection}, | |
| author={Wu, Yu-Huan and Liu, Yun and Xu, Jun and Bian, Jia-Wang and Gu, Yu-Chao and Cheng, Ming-Ming}, | |
| journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, | |
| year={2021}, | |
| publisher={IEEE} | |
| } | |
| % ATSA | |
| @Article{zhang2020asymmetric, | |
| title={Asymmetric two-stream architecture for accurate RGB-D saliency detection}, | |
| author={Zhang, Miao and Fei, Sun Xiao and Liu, Jie and Xu, Shuang and Piao, Yongri and Lu, Huchuan}, | |
| booktitle={European Conference on Computer Vision}, | |
| pages={374--390}, | |
| year={2020}, | |
| organization={Springer} | |
| } | |
| % CDNet | |
| @article{jin2021cdnet, | |
| title={CDNet: Complementary depth network for RGB-D salient object detection}, | |
| author={Jin, Wen-Da and Xu, Jun and Han, Qi and Zhang, Yi and Cheng, Ming-Ming}, | |
| journal={IEEE Transactions on Image Processing}, | |
| volume={30}, | |
| pages={3376--3390}, | |
| year={2021}, | |
| publisher={IEEE} | |
| } | |
| % CoNet | |
| @Article{ji2020accurate, | |
| title={Accurate RGB-D salient object detection via collaborative learning}, | |
| author={Ji, Wei and Li, Jingjing and Zhang, Miao and Piao, Yongri and Lu, Huchuan}, | |
| booktitle={European Conference on Computer Vision}, | |
| pages={52--69}, | |
| year={2020}, | |
| organization={Springer} | |
| } | |
| % SPNet | |
| @inproceedings{zhou2021specificity, | |
| title={Specificity-preserving rgb-d saliency detection}, | |
| author={Zhou, Tao and Fu, Huazhu and Chen, Geng and Zhou, Yi and Fan, Deng-Ping and Shao, Ling}, | |
| booktitle={Proceedings of the IEEE/CVF international conference on computer vision}, | |
| pages={4681--4691}, | |
| year={2021} | |
| } | |
| % C2DFNet | |
| @article{zhang2022c, | |
| title={C2DFNet: Criss-Cross Dynamic Filter Network for RGB-D Salient Object Detection}, | |
| author={Zhang, Miao and Yao, Shunyu and Hu, Beiqi and Piao, Yongri and Ji, Wei}, | |
| journal={IEEE Transactions on Multimedia}, | |
| year={2022}, | |
| publisher={IEEE} | |
| } | |
| % SPSN | |
| @inproceedings{lee2022spsn, | |
| title={SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection}, | |
| author={Lee, Minhyeok and Park, Chaewon and Cho, Suhwan and Lee, Sangyoun}, | |
| booktitle={European Conference on Computer Vision}, | |
| pages={630--647}, | |
| year={2022}, | |
| organization={Springer} | |
| } | |
| % ConvNeXt | |
| @inproceedings{liu2022convnet, | |
| title={A convnet for the 2020s}, | |
| author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining}, | |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | |
| pages={11976--11986}, | |
| year={2022} | |
| } | |
| % GPT-2 | |
| @article{radford2019language, | |
| title={Language models are unsupervised multitask learners}, | |
| author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others}, | |
| journal={OpenAI blog}, | |
| volume={1}, | |
| number={8}, | |
| pages={9}, | |
| year={2019} | |
| } | |
| % BERT | |
| @article{devlin2018bert, | |
| title={Bert: Pre-training of deep bidirectional transformers for language understanding}, | |
| author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, | |
| journal={arXiv preprint arXiv:1810.04805}, | |
| year={2018} | |
| } | |
| % UNet | |
| @inproceedings{ronneberger2015u, | |
| title={U-net: Convolutional networks for biomedical image segmentation}, | |
| author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas}, | |
| booktitle={International Conference on Medical image computing and computer-assisted intervention}, | |
| pages={234--241}, | |
| year={2015}, | |
| organization={Springer} | |
| } | |
| % MobileNetV2 | |
| @inproceedings{sandler2018mobilenetv2, | |
| title={Mobilenetv2: Inverted residuals and linear bottlenecks}, | |
| author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh}, | |
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, | |
| pages={4510--4520}, | |
| year={2018} | |
| } | |
| % Xception | |
| @inproceedings{chollet2017xception, | |
| title={Xception: Deep learning with depthwise separable convolutions}, | |
| author={Chollet, Fran{\c{c}}ois}, | |
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, | |
| pages={1251--1258}, | |
| year={2017} | |
| } | |
| % MobileNets | |
| @article{howard2017mobilenets, | |
| title={Mobilenets: Efficient convolutional neural networks for mobile vision applications}, | |
| author={Howard, Andrew G and Zhu, Menglong and Chen, Bo and Kalenichenko, Dmitry and Wang, Weijun and Weyand, Tobias and Andreetto, Marco and Adam, Hartwig}, | |
| journal={arXiv preprint arXiv:1704.04861}, | |
| year={2017} | |
| } | |
| % ResNet | |
| @inproceedings{he2016deep, | |
| title={Deep residual learning for image recognition}, | |
| author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, | |
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, | |
| pages={770--778}, | |
| year={2016} | |
| } | |
| % ResNeXt | |
| @inproceedings{xie2017aggregated, | |
| title={Aggregated residual transformations for deep neural networks}, | |
| author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming}, | |
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, | |
| pages={1492--1500}, | |
| year={2017} | |
| } | |
| % MultiMAE | |
| @article{bachmann2022multimae, | |
| title={MultiMAE: Multi-modal Multi-task Masked Autoencoders}, | |
| author={Bachmann, Roman and Mizrahi, David and Atanov, Andrei and Zamir, Amir}, | |
| journal={arXiv preprint arXiv:2204.01678}, | |
| year={2022} | |
| } | |
| % MAE | |
| @inproceedings{he2022masked, | |
| title={Masked autoencoders are scalable vision learners}, | |
| author={He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll{\'a}r, Piotr and Girshick, Ross}, | |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | |
| pages={16000--16009}, | |
| year={2022} | |
| } | |
| % VisionTransformer, ViT | |
| @article{dosovitskiy2020image, | |
| title={An image is worth 16x16 words: Transformers for image recognition at scale}, | |
| author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others}, | |
| journal={arXiv preprint arXiv:2010.11929}, | |
| year={2020} | |
| } | |
| % DANet | |
| @Article{zhao2020single, | |
| title={A single stream network for robust and real-time RGB-D salient object detection}, | |
| author={Zhao, Xiaoqi and Zhang, Lihe and Pang, Youwei and Lu, Huchuan and Zhang, Lei}, | |
| booktitle={European Conference on Computer Vision}, | |
| pages={646--662}, | |
| year={2020}, | |
| organization={Springer} | |
| } | |
| % DCF | |
| @Article{Ji_2021_DCF, | |
| author = {Ji, Wei and Li, Jingjing and Yu, Shuang and Zhang, Miao and Piao, Yongri and Yao, Shunyu and Bi, Qi and Ma, Kai and Zheng, Yefeng and Lu, Huchuan and Cheng, Li}, | |
| title = {Calibrated RGB-D Salient Object Detection}, | |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| year = {2021}, | |
| pages = {9471-9481} | |
| } | |
| % MVSalNet | |
| @inproceedings{zhou2022mvsalnet, | |
| title={MVSalNet: Multi-view Augmentation for RGB-D Salient Object Detection}, | |
| author={Zhou, Jiayuan and Wang, Lijun and Lu, Huchuan and Huang, Kaining and Shi, Xinchu and Liu, Bocong}, | |
| booktitle={European Conference on Computer Vision}, | |
| pages={270--287}, | |
| year={2022}, | |
| organization={Springer} | |
| } | |
| % DSA2F | |
| @Article{Sun2021DeepRS, | |
| title={Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion}, | |
| author={P. Sun and Wenhu Zhang and Huanyu Wang and Songyuan Li and Xi Li}, | |
| journal={IEEE Conf. Comput. Vis. Pattern Recog.}, | |
| year={2021} | |
| } | |
| % FRDT | |
| @Article{zhang2020feature, | |
| title={Feature reintegration over differential treatment: A top-down and adaptive fusion network for RGB-D salient object detection}, | |
| author={Zhang, Miao and Zhang, Yu and Piao, Yongri and Hu, Beiqi and Lu, Huchuan}, | |
| booktitle={Proceedings of the 28th ACM international conference on multimedia}, | |
| pages={4107--4115}, | |
| year={2020} | |
| } | |
| % HAINet | |
| @article{li2021hierarchical, | |
| title={Hierarchical alternate interaction network for RGB-D salient object detection}, | |
| author={Li, Gongyang and Liu, Zhi and Chen, Minyu and Bai, Zhen and Lin, Weisi and Ling, Haibin}, | |
| journal={IEEE Transactions on Image Processing}, | |
| volume={30}, | |
| pages={3528--3542}, | |
| year={2021}, | |
| publisher={IEEE} | |
| } | |
| % JLDCF | |
| @Article{fu2020jl, | |
| title={JL-DCF: Joint learning and densely-cooperative fusion framework for RGB-D salient object detection}, | |
| author={Fu, Keren and Fan, Deng-Ping and Ji, Ge-Peng and Zhao, Qijun}, | |
| booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, | |
| pages={3052--3062}, | |
| year={2020} | |
| } | |
| % SSLSOD | |
| @inproceedings{zhao2022self, | |
| title={Self-supervised pretraining for rgb-d salient object detection}, | |
| author={Zhao, Xiaoqi and Pang, Youwei and Zhang, Lihe and Lu, Huchuan and Ruan, Xiang}, | |
| booktitle={AAAI Conference on Artificial Intelligence}, | |
| volume={3}, | |
| year={2022} | |
| } | |
| % DFTR | |
| @article{zhudftr, | |
| title={DFTR: Depth-supervised Fusion Transformer for Salient Object Detection}, | |
| author={Zhu, Heqin and Sun, Xu and Li, Yuexiang and Ma, Kai and Zhou, S Kevin and Zheng, Yefeng} | |
| } | |
| % PGAR | |
| @Article{chen2020progressively, | |
| title={Progressively guided alternate refinement network for RGB-D salient object detection}, | |
| author={Chen, Shuhan and Fu, Yun}, | |
| booktitle={European Conference on Computer Vision}, | |
| pages={520--538}, | |
| year={2020}, | |
| organization={Springer} | |
| } | |
| % DCMF | |
| @article{wang2022learning, | |
| title={Learning Discriminative Cross-Modality Features for RGB-D Saliency Detection}, | |
| author={Wang, Fengyun and Pan, Jinshan and Xu, Shoukun and Tang, Jinhui}, | |
| journal={IEEE Transactions on Image Processing}, | |
| volume={31}, | |
| pages={1285--1297}, | |
| year={2022}, | |
| publisher={IEEE} | |
| } | |
| % RD3D | |
| @Article{chen2021rgb, | |
| title={RGB-D salient object detection via 3D convolutional neural networks}, | |
| author={Chen, Qian and Liu, Ze and Zhang, Yi and Fu, Keren and Zhao, Qijun and Du, Hongwei}, | |
| booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, | |
| volume={35}, | |
| number={2}, | |
| pages={1063--1071}, | |
| year={2021} | |
| } | |
| % ReDWeb-S | |
| % S2MA | |
| @Article{liu2020learning, | |
| title={Learning selective self-mutual attention for RGB-D saliency detection}, | |
| author={Liu, Nian and Zhang, Ni and Han, Junwei}, | |
| booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, | |
| pages={13756--13765}, | |
| year={2020} | |
| } | |
| % SSF | |
| @Article{zhang2020select, | |
| title={Select, supplement and focus for RGB-D saliency detection}, | |
| author={Zhang, Miao and Ren, Weisong and Piao, Yongri and Rong, Zhengkun and Lu, Huchuan}, | |
| booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, | |
| pages={3472--3481}, | |
| year={2020} | |
| } | |
| % UCNet | |
| @Article{zhang2020uc, | |
| title={UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders}, | |
| author={Zhang, Jing and Fan, Deng-Ping and Dai, Yuchao and Anwar, Saeed and Saleh, Fatemeh Sadat and Zhang, Tong and Barnes, Nick}, | |
| booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, | |
| pages={8582--8591}, | |
| year={2020} | |
| } | |
| % TriTransNet | |
| @inproceedings{liu2021tritransnet, | |
| title={TriTransNet: RGB-D salient object detection with a triplet transformer embedding network}, | |
| author={Liu, Zhengyi and Wang, Yuan and Tu, Zhengzheng and Xiao, Yun and Tang, Bin}, | |
| booktitle={Proceedings of the 29th ACM international conference on multimedia}, | |
| pages={4481--4490}, | |
| year={2021} | |
| } | |
| @Comment{jabref-meta: databaseType:bibtex;} | |