Context-Aware Feature-Fusion for Co-occurring Object Detection in Autonomous Driving
Proposes a novel framework called Context-Centric Feature Fusion (CCFF) to handle co-occurring object detection in autonomous driving, using Local Context Fusion Module (LCFM) and Global Context Attention Module (GCAM). Achieves Category-level Consistency Strategy (CCS) of 0.973 and 0.969 on Cityscapes and BDD100K, respectively, with a 14.1% improvement in small object detection AP_S and successful recovery of rare classes like 'Train'. The framework processes images in real-time with only 0.2 FPS overhead.
[2606.12628] Context-Aware Feature-Fusion for Co-occurring Object Detection in Autonomous Driving
[Submitted on 10 Jun 2026]
Title:Context-Aware Feature-Fusion for Co-occurring Object Detection in Autonomous Driving
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Abstract:Object detection in autonomous driving requires precise localization and an inherent understanding of the relational context between co-occurring objects. In extremely complex heterogeneous environments rare classes, small-scale objects, and frequently appearing objects are difficult for standard object detection frameworks to handle. In this paper, we propose a novel framework called Context-Centric Feature Fusion (CCFF), which utilizes two attention-based modules, Local Context Fusion Module (LCFM) uses the RoI-to-RoI self-attention mechanism to resolve spatial interactions, mainly considering small and partially obscured objects, while Global Context Attention Module (GCAM) converts the co-occurrence of objects priors by pooling top-K RoI features into a global context attention token, avoiding the computational overhead of pixel-level global pooling. This fusion of local and object-centric global features yields contextualized embeddings that enhance classification results and co-occurring objects detection. Our method is evaluated on two datasets, Cityscapes and BDD100K which demonstrate significant improvement on relational consistency, achieving a Category-level Consistency Strategy (CCS) of 0.973 and 0.969, respectively. Furthermore, our approach produces substantial gains in small object detection (AP_S: 14.1%) and successfully recovers rare classes such as "Train" that are typically lost in large distributions. Our efficiency report shows that the framework processes images in real time with a 0.2 FPS overhead. The code is available at this https URL.
Comments: 8 pages, 3 figures, CVPR 2026 Precognition Workshop
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.12628 [cs.CV]
(or arXiv:2606.12628v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.12628
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Binay Singh [view email] [v1] Wed, 10 Jun 2026 19:33:16 UTC (1,409 KB)
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