Camouflage-aware Image-Text Retrieval via Expert Collaboration

• URL: http://arxiv.org/abs/2604.01251v1
• Date: Wed, 01 Apr 2026 01:52:08 GMT
• Title: Camouflage-aware Image-Text Retrieval via Expert Collaboration
• Abstract summary: We formulate a new task dubbed camouflage-aware image-text retrieval'' (CA-ITR)<n>We first construct a dedicated camouflage image-text retrieval dataset (CamoIT)
• Score: 22.74075378281881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Camouflaged scene understanding (CSU) has attracted significant attention due to its broad practical implications. However, in this field, robust image-text cross-modal alignment remains under-explored, hindering deeper understanding of camouflaged scenarios and their related applications. To this end, we focus on the typical image-text retrieval task, and formulate a new task dubbed ``camouflage-aware image-text retrieval'' (CA-ITR). We first construct a dedicated camouflage image-text retrieval dataset (CamoIT), comprising $\sim$10.5K samples with multi-granularity textual annotations. Benchmark results conducted on CamoIT reveal the underlying challenges of CA-ITR for existing cutting-edge retrieval techniques, which are mainly caused by objects' camouflage properties as well as those complex image contents. As a solution, we propose a camouflage-expert collaborative network (CECNet), which features a dual-branch visual encoder: one branch captures holistic image representations, while the other incorporates a dedicated model to inject representations of camouflaged objects. A novel confidence-conditioned graph attention (C\textsuperscript{2}GA) mechanism is incorporated to exploit the complementarity across branches. Comparative experiments show that CECNet achieves $\sim$29% overall CA-ITR accuracy boost, surpassing seven representative retrieval models. The dataset and code will be available at https://github.com/jiangyao-scu/CA-ITR.

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