[人工智能]Visual Attention Network

paper
code

Abstract

demerits of self-attention (SA)

• treat images as 1D sequence and neglect 2D structure
• quadractic complexity is expensive for HR images
• achieve spatial adaptability but ignore channel adaptability

demrit of multiple layer perceptron (MLP)

• sensitive to input size and only process fixed-size images
• consider global information but ignore local structure

contributions

• propose large kernel attention (LKA)
  local structure information, long-range dependence, adaptability in channel dimension
• present visual attention network (VAN) as backbone based on LKA
  SOTA performance, less parameters and FLOPS

Results of different models on ImageNet-1K validation set. Left: Comparing the performance of recent models DeiT, PVT, Swin Transformer, ConvNeXt, Focal Transformer and our VAN. All these models have a similar amount of parameters. Right: Comparing the performance of recent models and our VAN while keeping the computational cost similar.

Method

large kernel attention (LKA)

large-kernel convolution brings a huge amount of computational overhead and parameters
solution decompose a large kernel convolution

Decomposition diagram of large-kernel convolution. A standard convolution can be decomposed into three parts: a depth-wise convolution (DW-Conv), a depth-wise dilation convolution (DW-D-Conv), and a pointwise convolution ($1\times 1$ Conv). The colored grids represent the location of convolution kernel and the yellow grid means the center point. The diagram shows that a $13\times 13$ convolution is decomposed into a $5\times 5$ depth-wise convolution, a $5\times 5$ depth-wise dilation convolution with dilation rate 3, and a pointwise convolution. Note: zero paddings are omitted in the above figure.

a $K\times K$ large kernel convolution divided into 3 components

• a spatial local convolution: $(2d?1)\times (2d?1)$ depth-wise conv $?$ local contextual information
• a spatial long-range convolution: $?\frac{K}{d}?\times ?\frac{K}{d}?$ depth-wise dilated
  conv $?$ large receptive field
• a channel convolution: $1\times 1$ conv $?$ adaptility in channel

where, $K$ is kernel size, $d$ is dilation

Desirable properties belonging to convolution, self-attention and LKA.

write LKA module as
$$\begin{array}{rl}Attention& =Con{v}_{1\times 1}(DW\text{-}D\text{-}Conv(DW\text{-}Conv(F)))\\ Output& =Attention?F\end{array}$$

where, $F\in {\mathbb{R}}^{C\times H\times W}$ is input features, $Attention\in {\mathbb{R}}^{C\times H\times W}$ is attention map

The structure of different modules: (a) the proposed Large Kernel Attention (LKA); (b) non-attention module; ? the self-attention module; (d) a stage of our Visual Attention Network (VAN). “CFF” means convolutional feed-forward network. Residual connection is omitted in (d). The difference between (a) and (b) is the element-wise multiply. It is worth noting that ? is designed for 1D sequences.

computational complexity

assumed input and output with same size ${\mathbb{R}}^{C\times H\times W}$
$$\begin{array}{rl}\mathrm{P}\mathrm{a}\mathrm{r}\mathrm{a}\mathrm{m}& =?\frac{K}{d}?\times ?\frac{K}{d}?\times C+(2d?1)\times (2d?1)\times C+C\times C\\ \mathrm{F}\mathrm{L}\mathrm{O}\mathrm{P}\mathrm{s}& =(?\frac{K}{d}?\times ?\frac{K}{d}?\times C+(2d?1)\times (2d?1)\times C+C\times C)\times H\times W\end{array}$$

where, $K$ is kernel size with default $K=21$, $d$ is dilation with default $d=3$

Comparison of parameters of different manners for a $21\times 21$ convolution. X, Y and Our donate standard convolution, MobileNet decomposition and our decomposition, respectively. The input and output feature have the same size $H\times W\times C$. Note: Bias is omitted for simplifying format.

architecture variants

The detailed setting for different versions of the VAN. “e.r.” represents expansion ratio in the feed-forward network.

Experiment

image classification

dataset ImageNet-1K, with augmentation
optimizer AdamW: batchsize=1024, 310 epochs, momentum=0.9, weigh decy=5e-2, init lr=5e-4, warm-up, cosine decay

Compare with the state-of-the-art methods on ImageNet validation set. Params means parameter. GFLOPs donates floating point operations. Top-1 Acc represents Top-1 accuracy.

object detection and instance segmentation

framework RetinaNet, Mask R-CNN, Cascade Mask R-CNN, Sparse R-CNN
dataset COCO 2017

Object detection on COCO 2017 dataset. #P means parameter. RetinaNet $1\times$ donates models are based on RetinaNet and we train them for 12 epochs.

Object detection and instance segmentation on COCO 2017 dataset. #P means parameter. Mask R-CNN $1\times$ donates models are based on Mask R-CNN and we train them for 12 epochs. $A{P}^b$ and $A{P}^m$ refer to bounding box AP and mask AP respectively.

Comparison with the state-of-the-art vision backbones on COCO 2017 benchmark. All models are trained for 36 epochs. We calculate FLOPs with input size of $1280\times 800$.

semantic segmentation

framework Semantic FPN, UperNet
dataset ADE20K

Results of semantic segmentation on ADE20K validation set. The upper and lower part are obtained under two different training/validation schemes. We calculate FLOPs with input size $512\times 512$ for Semantic FPN and $2048\times 512$ for UperNet.

ablation studies

architecture components

Ablation study of different modules in LKA. Results show that each part is critical. Acc(%) means Top-1 accuracy on ImageNet validation set.

key findings

• local structural information, long-range dependence, adaptability in channel dimension are all critial
• attention mechanism help network achieve adaptive property

kernel size and dilation

Ablation study of different kernel size in LKA. Acc(%) means Top-1 accuracy on ImageNet validation set.

key findings

• decomposing a $21\times 21$ convolution work better than decomposing a $7\times 7$ convolution
  $?$ large kernel is critical for visual tasks
• decomposing a larger $35\times 35$ convolution, gain is not obvious comparing with decomposing a $21\times 21$ convolution

visualization

Visualization results. All images come from different categories in ImageNet validation set. CAM is produced by using Grad-CAM. We compare different CAMs produced by Swin-T, ConvNeXt-T and VAN-Base.

key findings

• activation area is more accurate
• show obvious advantages when object is dominant in images $?$ ability to capture long-range dependence