C-DiffDet+: Fusing Global Scene Context with Generative Denoising for
  High-Fidelity Object Detection
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
68b7d632295f15ff609114a9
Abdellah Zakaria Sellam
admin_assigned
2025-09-03T08:51:53.813Z
673dc4e50f53d42fdab5fc64
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/EUZs07mRzVNrrCK0F8nPY.png
abdellah zakaria sellam
user
ldb0071
68b7d632295f15ff609114aa
Ilyes Benaissa
admin_assigned
2025-09-03T08:52:00.298Z
67297ce68d9327bd247a8274
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/hyNoQRX61fOqw428wsHZJ.png
Ilyes benaissa
user
xkruvox
68b7d632295f15ff609114ab
Salah Eddine Bekhouche
claimed_verified
2025-09-03T08:26:07.358Z
63e7493fdb40d9e67feaccdd
https://cdn-avatars.huggingface.co/v1/production/uploads/63e7493fdb40d9e67feaccdd/9d1iTreC4GlxSiWvLT0dP.jpeg
Salah Eddine Bekhouche
user
Bekhouche
68b7d632295f15ff609114ac
Abdenour Hadid
68b7d632295f15ff609114ad
Vito Renó
68b7d632295f15ff609114ae
Cosimo Distante
C-DiffDet+: Fusing Global Scene Context with Generative Denoising for
  High-Fidelity Object Detection
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
Context-Aware Fusion enhances DiffusionDet by integrating global scene context with local features using cross-attention, improving performance on fine-grained object detection tasks.
DiffusionDet conditional denoising diffusion Context-Aware Fusion cross-attention mechanisms global scene context local proposal features dedicated encoder generative detection paradigm CarDD benchmark