Consensus-Aware Multi-Source Fusion for Reference-Guided Camouflaged Object Detection

arXiv:2609.38747v1 Announce Type: new Abstract: Reference-guided camouflaged object detection aims to segment a target whose visual appearance closely resembles its surroundings by exploiting auxiliary reference samples. The task remains difficult because reference samples contain inconsistent target cues, while generic visual representations are not inherently aligned with the target specified by the references. To handle these problems, we present a consensus-aware multi-source fusion framework. Reference-Conditioned Dual-Backbone Fusion (RCDF) couples trainable PVTv2 query features with frozen DINOv3 representations and uses reference-conditioned correlation to select foundation-model evidence before multi-scale fusion. The framework also aggregates multiple references through cross-reference consensus aggregation and injects reference information at semantic depths matched to the query features. Extensive experiments demonstrate the effectiveness of the proposed method. The results further show that reference consensus, target-conditioned foundation features, and hierarchical decoding provide complementary improvements under the evaluation protocol. The source code will be made publicly available upon acceptance.

Related

• FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning
• Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion
• DRPFNet: Dual-domain Residual Progressive Fusion Network for RGB-Thermal Object Detection

Source: arXiv cs.CV | 2026-10-01