Transcription of A ConvNet for the 2020s
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A ConvNet for the 2020s
A ConvNet for the 2020s Zhuang Liu1,2 * Hanzi Mao1 Chao-Yuan Wu1 Christoph Feichtenhofer1 Trevor Darrell2 Saining Xie1 . 1 2. Facebook AI Research (FAIR) UC Berkeley Code: Abstract [ ] 2 Mar 2022. The Roaring 20s of visual recognition began with the introduction of vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classifica- tion model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers ( , Swin Transformers) that reintroduced sev- eral ConvNet priors, making Transformers practically viable Diameter 4 8 16 256 GFLOPs as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather Figure 1.
suited to a wide variety of computer vision applications. The most important one is translation equivariance, which is a de-sirable property for tasks like objection detection. ConvNets are also inherently efficient due to the fact that when used in a sliding-window manner, the computations are shared [59].
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