DiffusionDet: Diffusion Model for Object Detection
Abstract
We propose DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. During the training stage, object boxes diffuse from ground-truth boxes to random distribution, and the model learns to reverse this noising process. In inference, the model refines a set of randomly generated boxes to the output results in a progressive way. Our work possesses an appealing property of flexibility, which enables the dynamic number of boxes and iterative evaluation. The extensive experiments on the standard benchmarks show that DiffusionDet achieves favorable performance compared to previous well-established detectors. For example, DiffusionDet achieves 5.3 AP and 4.8 AP gains when evaluated with more boxes and iteration steps, under a zero-shot transfer setting from COCO to CrowdHuman. Our code is available at https://github.com/ShoufaChen/DiffusionDet.
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DiffMIC is the first diffusion-based model proposed for general medical image classification.
“In this paper, we propose the first diffusion-based model (named DiffMIC) to address general medical image classification by eliminating unexpected noise and perturbations in medical images and robustly capturing semantic representation.”
From DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification (2023) · cites this paper as [4]
Cites
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“Diffusion models have recently demonstrated remarkable results in fields including computer vision [4,21,33,35,39,68,71,75,76,82,104,108], nature language processing [3,30,52], audio processing [41,48,50,70, 91,100,103], graph-related topics [42], interdisciplinary applications…”
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Dif-Fusion: Toward High Color Fidelity in Infrared and Visible Image Fusion With Diffusion Models
Yue, J., Fang, L., Xia, S., Deng, Y., & Ma, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3322046 · Cited by 304 · see citing works
DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification
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DiffMIC-v2: Medical Image Classification via Improved Diffusion Network
Yang, Y., Fu, H., Aviles-Rivero, A. I., Xing, Z., & Zhu, L. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3530399 · Cited by 33 · see citing works
Multimodal Promptable Token Merging for Diffusion Models
Hong, C.-Y., & Liu, T.-L. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i16.33894
- Authors
- Chen, S., Sun, P., Song, Y., & Luo, P.
- Affiliations
- University of Hong Kong (HK)
- Tencent (China) (CN)
- Shanghai Artificial Intelligence Laboratory (CN)
- Venue
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2023-10-01
- Pages
- 19773-19786
- OpenAlex
- W4390873752
- Cited by
- 657
- References
- 21
- Citations by year
- 2026: 139 · 2025: 253 · 2024: 214 · 2023: 51
- FWCI
- 70.17
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- object detection, diffusion models, denoising diffusion, progressive refinement, zero-shot transfer, CrowdHuman, COCO
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