{"ID":2876534,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00578","arxiv_id":"2509.00578","title":"C-DiffDet+: Fusing Global Scene Context with Generative Denoising for High-Fidelity Car Damage Detection","abstract":"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","short_abstract":"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 condi...","url_abs":"https://arxiv.org/abs/2509.00578","url_pdf":"https://arxiv.org/pdf/2509.00578v4","authors":"[\"Abdellah Zakaria Sellam\",\"Ilyes Benaissa\",\"Salah Eddine Bekhouche\",\"Abdenour Hadid\",\"Vito Renó\",\"Cosimo Distante\"]","published":"2025-08-30T18:06:06Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}