Weimin Tan, Siyuan Chen, Bo Yan
Diffusion models have demonstrated excellent performance in image generation. Although various few-shot semantic segmentation (FSS) models with different network structures have been proposed, performance improvement has reached a bottleneck. This paper presents the first work to leverage the diffusion model for FSS task, called DifFSS. DifFSS, a novel FSS paradigm, can further improve the performance of the state-of-the-art FSS models by a large margin without modifying their network structure. Specifically, we utilize the powerful generation ability of diffusion models to generate diverse auxiliary support images by using the semantic mask, scribble or soft HED boundary of the support image as control conditions. This generation process simulates the variety within the class of the query image, such as color, texture variation, lighting, $etc$. As a result, FSS models can refer to more diverse support images, yielding more robust representations, thereby achieving a consistent improvement in segmentation performance. Extensive experiments on three publicly available datasets based on existing advanced FSS models demonstrate the effectiveness of the diffusion model for FSS task. Furthermore, we explore in detail the impact of different input settings of the diffusion model on segmentation performance. Hopefully, this completely new paradigm will bring inspiration to the study of FSS task integrated with AI-generated content. Code is available at https://github.com/TrinitialChan/DifFSS
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Few-Shot Learning | FSS-1000 (1-shot) | Mean IoU | 88.4 | DCAMA (DifFSS, ResNet-50) |
| Few-Shot Learning | FSS-1000 (1-shot) | Mean IoU | 86.2 | HSNet (DifFSS, ResNet-50) |
| Few-Shot Learning | PASCAL-5i (1-Shot) | Mean IoU | 70.2 | HDMNet (DifFSS, ResNet-50) |
| Few-Shot Learning | PASCAL-5i (1-Shot) | Mean IoU | 69.3 | BAM (DifFSS, ResNet-50) |
| Few-Shot Learning | PASCAL-5i (1-Shot) | Mean IoU | 66.2 | CyCTR (DifFSS, ResNet-50) |
| Few-Shot Learning | COCO-20i (1-shot) | Mean IoU | 46.7 | HDMNet (DifFSS, ResNet-50) |
| Few-Shot Learning | COCO-20i (1-shot) | Mean IoU | 43.6 | BAM (DifFSS, ResNet-50) |
| Few-Shot Semantic Segmentation | FSS-1000 (1-shot) | Mean IoU | 88.4 | DCAMA (DifFSS, ResNet-50) |
| Few-Shot Semantic Segmentation | FSS-1000 (1-shot) | Mean IoU | 86.2 | HSNet (DifFSS, ResNet-50) |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | Mean IoU | 70.2 | HDMNet (DifFSS, ResNet-50) |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | Mean IoU | 69.3 | BAM (DifFSS, ResNet-50) |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | Mean IoU | 66.2 | CyCTR (DifFSS, ResNet-50) |
| Few-Shot Semantic Segmentation | COCO-20i (1-shot) | Mean IoU | 46.7 | HDMNet (DifFSS, ResNet-50) |
| Few-Shot Semantic Segmentation | COCO-20i (1-shot) | Mean IoU | 43.6 | BAM (DifFSS, ResNet-50) |
| Meta-Learning | FSS-1000 (1-shot) | Mean IoU | 88.4 | DCAMA (DifFSS, ResNet-50) |
| Meta-Learning | FSS-1000 (1-shot) | Mean IoU | 86.2 | HSNet (DifFSS, ResNet-50) |
| Meta-Learning | PASCAL-5i (1-Shot) | Mean IoU | 70.2 | HDMNet (DifFSS, ResNet-50) |
| Meta-Learning | PASCAL-5i (1-Shot) | Mean IoU | 69.3 | BAM (DifFSS, ResNet-50) |
| Meta-Learning | PASCAL-5i (1-Shot) | Mean IoU | 66.2 | CyCTR (DifFSS, ResNet-50) |
| Meta-Learning | COCO-20i (1-shot) | Mean IoU | 46.7 | HDMNet (DifFSS, ResNet-50) |
| Meta-Learning | COCO-20i (1-shot) | Mean IoU | 43.6 | BAM (DifFSS, ResNet-50) |