Papers › DiffusionInst: Diffusion Model for Instance Segmentation
DiffusionInst: Diffusion Model for Instance Segmentation
Zhangxuan Gu, Haoxing Chen, Zhuoer Xu, Jun Lan, Changhua Meng, Weiqiang Wang
Diffusion frameworks have achieved comparable performance with previous state-of-the-art image generation models. Researchers are curious about its variants in discriminative tasks because of its powerful noise-to-image denoising pipeline. This paper proposes DiffusionInst, a novel framework that represents instances as instance-aware filters and formulates instance segmentation as a noise-to-filter denoising process. The model is trained to reverse the noisy groundtruth without any inductive bias from RPN. During inference, it takes a randomly generated filter as input and outputs mask in one-step or multi-step denoising. Extensive experimental results on COCO and LVIS show that DiffusionInst achieves competitive performance compared to existing instance segmentation models with various backbones, such as ResNet and Swin Transformers. We hope our work could serve as a strong baseline, which could inspire designing more efficient diffusion frameworks for challenging discriminative tasks. Our code is available in https://github.com/chenhaoxing/DiffusionInst.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instance Segmentation | COCO test-dev | DiffusionInst-SwinL | mask AP | 48.3 | #30 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | DiffusionInst-SwinB | mask AP | 47.6 | #32 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | DiffusionInst-ResNet101 | mask AP | 41.5 | #61 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | DiffusionInst-ResNet50 | mask AP | 37.1 | #94 of 112 | Archive leaderboard | report |
| Instance Segmentation | LVIS v1.0 val | DiffusionInst-SwinL | mask AP | 38.6 | #9 of 25 | Archive leaderboard | report |
| Instance Segmentation | LVIS v1.0 val | DiffusionInst-SwinB | mask AP | 36 | #12 of 25 | Archive leaderboard | report |
| Instance Segmentation | LVIS v1.0 val | DiffusionInst-ResNet101 | mask AP | 27 | #22 of 25 | Archive leaderboard | report |
| Instance Segmentation | LVIS v1.0 val | DiffusionInst-ResNet50 | mask AP | 22.3 | #25 of 25 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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