Papers › DiffusionVID: Denoising Object Boxes with Spatio-temporal Conditioning for Video...
DiffusionVID: Denoising Object Boxes with Spatio-temporal Conditioning for Video Object Detection
Si-Dong Roh, Ki-Seok Chung
Several existing still image object detectors suffer from image deterioration in videos, such as motion blur, camera defocus, and partial occlusion. We present DiffusionVID, a diffusion model-based video object detector, that exploits spatio-temporal conditioning. Inspired by the diffusion model, DiffusionVID refines random noise boxes to obtain the original object boxes in a video sequence. To effectively refine the box from the degraded images in the videos, we used three novel approaches: cascade refinement, dynamic core-set conditioning, and local batch refinement. The cascade refinement architecture effectively collects information from object regions, whereas the dynamic core-set conditioning further improves the denoising quality using adaptive conditional guidance based on the spatio-temporal core-set. Local batch refinement significantly improves the refinement speed by exploiting GPU parallelism. On the standard and widely used ImageNet-VID benchmark, our DiffusionVID with the ResNet-101 and Swin-Base backbones achieves 86.9 mAP @ 46.6 FPS and 92.4 mAP @ 27.0 FPS, respectively, which is state-of-the-art performance. To the best of the authors’ knowledge, this is the first video object detector based on a diffusion model. The code and models are available at https://github.com/sdroh1027/DiffusionVID.
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Object Detection | ImageNet VID | DiffusionVID (Swin-B) | MAP | 92.5 | #2 of 33 | Archive leaderboard | report |
| Video Object Detection | ImageNet VID | DiffusionVID (ResNet-101) | MAP | 87.1 | #11 of 33 | 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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