Review — Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Axial-DeepLab, for Both Image Classification & Segmentation
6 min readFeb 22, 2023
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation,
Axial-DeepLab, by Johns Hopkins University, and Google Research,
2020 ECCV, Over 400 Citations (Sik-Ho Tsang @ Medium)
Image Classification, Panoptic Segmentation, Instance Segmentation, Semantic Segmentation
==== My Other Paper Readings Are Also Over Here ====
- Conventional 2D self-attention which has very high computational complexity is factorized into two 1D self-attentions.
- A position-sensitive self-attention design is proposed.
- Combining both yields the position-sensitive axial-attention layer.
- By stacking the position-sensitive axial-attention layers, Axial-DeepLab models are formed for image classification and dense prediction.
Outline
- Position-Sensitive Axial-Attention Layer
- Axial-DeepLab
- Results
1. Position-Sensitive Self-Attention Layer
1.1. Conventional Self-Attention
- Given an input feature map x with height h, width w, and channels din, the output at position o=(i, j), yo computed by pooling over the projected input as:
- where N is the whole…
