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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.00578 |
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| _version_ | 1866909870583709696 |
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| author | Sellam, Abdellah Zakaria Benaissa, Ilyes Bekhouche, Salah Eddine Hadid, Abdenour Renó, Vito Distante, Cosimo |
| author_facet | Sellam, Abdellah Zakaria Benaissa, Ilyes Bekhouche, Salah Eddine Hadid, Abdenour Renó, Vito Distante, Cosimo |
| contents | Fine-grained object detection in challenging visual domains, such as vehicle damage assessment, presents a formidable challenge even for human experts to resolve reliably. While DiffusionDet has advanced the state-of-the-art through conditional denoising diffusion, its performance remains limited by local feature conditioning in context-dependent scenarios. We address this fundamental limitation by introducing Context-Aware Fusion (CAF), which leverages cross-attention mechanisms to integrate global scene context with local proposal features directly. The global context is generated using a separate dedicated encoder that captures comprehensive environmental information, enabling each object proposal to attend to scene-level understanding. Our framework significantly enhances the generative detection paradigm by enabling each object proposal to attend to comprehensive environmental information. Experimental results demonstrate an improvement over state-of-the-art models on the CarDD benchmark, establishing new performance benchmarks for context-aware object detection in fine-grained domains |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | C-DiffDet+: Fusing Global Scene Context with Generative Denoising for High-Fidelity Car Damage Detection Sellam, Abdellah Zakaria Benaissa, Ilyes Bekhouche, Salah Eddine Hadid, Abdenour Renó, Vito Distante, Cosimo Computer Vision and Pattern Recognition Fine-grained object detection in challenging visual domains, such as vehicle damage assessment, presents a formidable challenge even for human experts to resolve reliably. While DiffusionDet has advanced the state-of-the-art through conditional denoising diffusion, its performance remains limited by local feature conditioning in context-dependent scenarios. We address this fundamental limitation by introducing Context-Aware Fusion (CAF), which leverages cross-attention mechanisms to integrate global scene context with local proposal features directly. The global context is generated using a separate dedicated encoder that captures comprehensive environmental information, enabling each object proposal to attend to scene-level understanding. Our framework significantly enhances the generative detection paradigm by enabling each object proposal to attend to comprehensive environmental information. Experimental results demonstrate an improvement over state-of-the-art models on the CarDD benchmark, establishing new performance benchmarks for context-aware object detection in fine-grained domains |
| title | C-DiffDet+: Fusing Global Scene Context with Generative Denoising for High-Fidelity Car Damage Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.00578 |