[RIS]SLViT: Scale-Wise Language-Guided Vision Transformer forReferring Image Segmentation

1. BaseInfo

2. Creative Q->A

1. 视觉特征提取和跨模态融合分别考虑, 视觉-语言对齐不足。-> Language-Guided Multi-Scale Fusion Attention (LMFA)
2. 两个模块是顺序存在的，信息交互不充分。-> An Uncertain Region Cross-Scale Enhancement module (URCE)
   

3. Concrete

3.1. Model

类似LAVT在 encoder 里做融合

3.1.1. Input

图片+文本

3.1.2. Backbone

ViT + BERT
2，3，4 特征图下采样得到：stride of 2 and kernel size of 3 × 3 + batch normalization layer

• Language-Guided Multi-Scale Fusion Attention
  • Conv 5 x 5 初步局部特征
  • 多尺度卷积 1 x $k_i$ 同时存在三个具有不同内核大小的卷积分支，以捕捉不同感受野的局部特征，在模拟丰富的局部视觉信息时具有空间归纳偏向。卷积核大小分别为 7，11，21。
  • Cross Attention
  • Gate 门控
  • 两个分支融合后的 Conv 1 x 1
    代码中的 GatedCrossModalAttention 就和 LAVT 中的 PWAM 一样。

• Uncertain Region Cross-Scale Enhancement
  
  多头自注意力

3.1.3. Neck

3.1.4. Decoder

Hamburger [Geng et al., 2021] ：Hamburger function 、1×1 convolution 、 an upsampling function
ImageNet-22K from the SegNeXt [Guo et al., 2022]

3.1.5. Loss

CE

3.2. Training

BERT，12层，维度 768
In convolutional branches of LMFA, we use k1 = 7, k2 = 11, k3 = 21 kernel sizes for our convolutions. The rest of weights in our model are randomly initialized.
AdamW optimizer weight decay 0.01.
The learning rate is initialed as 3e-5 and scheduled by polynomial learning rate decay with a power of 0.9.
Epoch 60, Batch Size 16.
Image Size 480 x 480.

3.2.1. Resource

3.2.2 Dataset

3.3. Eval

verall intersectionover-union (oIoU),
mean intersection-over-union (mIoU),
precision at the 0.5, 0.7, and 0.9 threshold values.

3.4. Ablation

4. Reference

[Geng et al., 2021] Zhengyang Geng, Meng-Hao Guo, Hongxu Chen, Xia Li, Ke Wei, and Zhouchen Lin. Is attention better than matrix decomposition? arXiv preprint arXiv:2109.04553, 2021.
[Guo et al., 2022] Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, Zhengning Liu, Ming-Ming Cheng, and Shi-Min Hu. Segnext: Rethinking convolutional attention design for semantic segmentation. arXiv preprint arXiv:2209.08575, 2022.