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Photo Credit MaxViT: Multi-Axis Vision Transformer(1) is a paper jointly produced by Google Research and University of Texas at Austin in 2022. The paper proposes a new attention model, named multi-axis attention, which comprises a blocked local and a dilated global attention module. In addition, the paper introduces MaxViT architecture that combines multi-axis attentions with convolutions, which is highly effective in ImageNet benchmarks and downstream tasks. Multi-Axis Attention Source: [2] ...

Photo Credit Introduction Recall that an one-dimensional Taylor series is an expansion of a real function $f\left(x\right)$ about a point $x=a$ [2]: $$f\left(x\right)=f\left(a\right)+f^{\prime }\left(a\right)(x-a)+\frac{f^{\prime \prime }\left(a\right)}{2!}(x-a{)}^2+..+\frac{f^n\left(a\right)}{n!}(x-a{)}^n+...$$ We can approximate the cross-entropy loss using the Taylor series (a.k.a. Taylor expansion) using $a=1$: $$f\left(x\right)=-log\left(x\right)=0+(-1)\left(1{)}^{-1}\right(x-1)+(-1{)}^2(1{)}^{-2}\frac{(x-1{)}^2}{2}+...=\sum _{j=1}^{\infty }(-1{)}^j\frac{(j-1)!}{j!}(x-1{)}^j=\sum _{j=1}^{\infty }\frac{(1-x{)}^j}{j}$$ We can get the expansion for the focal loss simply by multiplying the cross-entropy loss series by $(1-x{)}^{\gamma }$: ...

credit Introduction At the start of 2022, we have a new pure convolution architecture (ConvNext)[1] that challenges the transformer architectures as a generic vision backbone. The new Visual Attention Network (VAN)[2] is yet another pure and simplistic convolution architecture that its creators claim to have achieved SOTA results with fewer parameters. Source: [2] What ConvNext tries to achieve is modernizing a standard ConvNet (ResNet) without introducing any attention-based modules. VAN still has attention-based modules, but the attention weights are obtained from a large kernel convolution instead of a self-attention block. To overcome the high computation costs brought by a large kernel convolution, it is decomposed into three components: a spatial local convolution (depth-wise convolution), a spatial long-range convolution (depth-wise dilation convolution), and a channel convolution (1x1 point-wise convolution). ...

credit Introduction Hierarchical Transformers (e.g., Swin Transformers[1]) has made Transformers highly competitive as a generic vision backbone and in a wide variety of vision tasks. A new paper from Facebook AI Research — “A ConvNet for the 2020s”[2] — gradually and systematically “modernizes” a standard ResNet[3] toward the design of a vision Transformer. The result is a family of pure ConvNet models dubbed ConvNeXt that compete favorably with Transformers in terms of accuracy and scalability. ...

Photo Credit In Part 1, we introduced the XCiT architecture and reviewed the implementation of the Cross-Covariance Attention(XCA) block. In this Part 2, we’ll review the implementation of the Local Patch Interaction(LPI) block and the Class Attention layer. from [1] Local Patch Interaction(LPI) Because there is no explicit communication between patches(tokens) in XCA, a layer consisting of two depth-wise 3×3 convolutional layers with Batch Normalization with GELU non-linearity is added to enable explicit communication. ...