Daoan Zhang, Chenming Li, Haoquan Li, Wenjian Huang, Lingyun Huang, JianGuo Zhang
Unsupervised image semantic segmentation(UISS) aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the critical properties from the view of feature alignments and feature uniformity for UISS models. We also make a comparison between UISS and image-wise representation learning. Based on the analysis, we argue that the existing MI-based methods in UISS suffer from representation collapse. By this, we proposed a robust network called Semantic Attention Network(SAN), in which a new module Semantic Attention(SEAT) is proposed to generate pixel-wise and semantic features dynamically. Experimental results on multiple semantic segmentation benchmarks show that our unsupervised segmentation framework specializes in catching semantic representations, which outperforms all the unpretrained and even several pretrained methods.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Domain Adaptation | DomainNet | H-Score | 52 | SAN |
| Semantic Segmentation | COCO-Stuff-3 | Pixel Accuracy | 80.3 | SAN |
| Semantic Segmentation | COCO-Stuff-27 | Clustering [Accuracy] | 52 | SAN |
| Universal Domain Adaptation | DomainNet | H-Score | 52 | SAN |
| Unsupervised Semantic Segmentation | COCO-Stuff-3 | Pixel Accuracy | 80.3 | SAN |
| Unsupervised Semantic Segmentation | COCO-Stuff-27 | Clustering [Accuracy] | 52 | SAN |
| 10-shot image generation | COCO-Stuff-3 | Pixel Accuracy | 80.3 | SAN |
| 10-shot image generation | COCO-Stuff-27 | Clustering [Accuracy] | 52 | SAN |