text: 105,234,1435350231437045762,1387514922,Irwan Bello,"Wondering how simple 3D-ResNets perform on video recognition given all the recent architecture craze? In Revisiting 3D ResNets for Video Recognition, we study the impact of improved training and scaling methods on 3D ResNets. <LINK> <LINK> We present revised 3D-ResNet baselines, termed 3D-ResNet-RS, and show that they are on par with more recent works. <LINK> This follows a series of works revisiting “older” architectures and showing that they can be competitive with recent alternatives, when scaled and trained properly. Object Detection: <LINK> Image classification: <LINK> Work led by @Phyyysalis in collaboration with @yeqing133, @YinCui1, @RuiQian3 and Jing Li.",https://arxiv.org/abs/2109.01696,"A recent work from Bello shows that training and scaling strategies may be more significant than model architectures for visual recognition. This short note studies effective training and scaling strategies for video recognition models. We propose a simple scaling strategy for 3D ResNets, in combination with improved training strategies and minor architectural changes. The resulting models, termed 3D ResNet-RS, attain competitive performance of 81.0 on Kinetics-400 and 83.8 on Kinetics-600 without pre-training. When pre-trained on a large Web Video Text dataset, our best model achieves 83.5 and 84.3 on Kinetics-400 and Kinetics-600. The proposed scaling rule is further evaluated in a self-supervised setup using contrastive learning, demonstrating improved performance. Code is available at: this https URL ",Revisiting 3D ResNets for Video Recognition,4,"['Wondering how simple 3D-ResNets perform on video recognition given all the recent architecture craze?\n\nIn Revisiting 3D ResNets for Video Recognition, we study the impact of improved training and scaling methods on 3D ResNets.\n\n<LINK> <LINK>', 'We present revised 3D-ResNet baselines, termed 3D-ResNet-RS, and show that they are on par with more recent works. https://t.co/Fa1nSl9loe', 'This follows a series of works revisiting “older” architectures and showing that they can be competitive with recent alternatives, when scaled and trained properly.\n\nObject Detection:\nhttps://t.co/VrSbG9RuvM\n\nImage classification:\nhttps://t.co/K7hMTtS0eR', 'Work led by @Phyyysalis in collaboration with @yeqing133, @YinCui1, @RuiQian3 and Jing Li.']",21,09,671