C-DiffDet+: Fusing Global Scene Context with Generative Denoising for
  High-Fidelity Object Detection
Authors: name: Abdellah Zakaria Sellam, name: Ilyes Benaissa, name: Salah Eddine Bekhouche, name: Abdenour Hadid, name: Vito Renó, name: Cosimo Distante

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