Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well

• URL: http://arxiv.org/abs/2502.14471v2
• Date: Mon, 19 May 2025 04:42:52 GMT
• Title: Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
• Authors: Chengyu Fang, Chunming He, Longxiang Tang, Yuelin Zhang, Chenyang Zhu, Yuqi Shen, Chubin Chen, Guoxia Xu, Xiu Li,
• Abstract summary: MultiCOS is a novel framework that effectively leverages diverse data modalities to improve segmentation performance.<n>BFSer outperforms existing multimodal baselines with both real and pseudo-modal data.
• Score: 23.460400679372714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Camouflaged Object Segmentation (COS) remains challenging because camouflaged objects exhibit only subtle visual differences from their backgrounds and single-modality RGB methods provide limited cues, leading researchers to explore multimodal data to improve segmentation accuracy. In this work, we presenet MultiCOS, a novel framework that effectively leverages diverse data modalities to improve segmentation performance. MultiCOS comprises two modules: Bi-space Fusion Segmentor (BFSer), which employs a state space and a latent space fusion mechanism to integrate cross-modal features within a shared representation and employs a fusion-feedback mechanism to refine context-specific features, and Cross-modal Knowledge Learner (CKLer), which leverages external multimodal datasets to generate pseudo-modal inputs and establish cross-modal semantic associations, transferring knowledge to COS models when real multimodal pairs are missing. When real multimodal COS data are unavailable, CKLer yields additional segmentation gains using only non-COS multimodal sources. Experiments on standard COS benchmarks show that BFSer outperforms existing multimodal baselines with both real and pseudo-modal data. Code will be released at \href{https://github.com/cnyvfang/MultiCOS}{GitHub}.

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