Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations

Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of annotated data, where meticulous pixel-level annotation is both labor-intensive and costly, primarily due to the intricate object-background boundaries. Addressing the core question, ”Can COS be effectively achieved in a zero-shot manner without manual annotations for any camouflaged object?”, we propose an affirmative solution. We analyze the learned attention patterns for camouflaged objects and introduce a robust zero-shot COS framework. Our findings reveal that while transformer models for salient object segmentation (SOS) prioritize global features in their attention mechanisms, camouflaged object segmentation exhibits both global and local attention biases. Based on these findings, we design a framework that adapts with the inherent local pattern bias of COS while incorporating global attention patterns and a broad semantic feature space derived from SOS. This enables efficient zero-shot transfer for COS. Specifically, We incorporate an Masked Image Modeling (MIM) based image encoder optimized for Parameter-Efficient Fine-Tuning (PEFT), a Multimodal Large Language Model (M-LLM), and a Multi-scale Fine-grained Alignment (MFA) mechanism. The MIM encoder captures essential local features, while the PEFT module learns global and semantic representations from SOS datasets. To further enhance semantic granularity, we leverage the M-LLM to generate caption embeddings conditioned on visual cues, which are meticulously aligned with multi-scale visual features via MFA. This alignment enables precise interpretation of complex semantic contexts. Moreover, we introduce a learnable codebook to represent the M-LLM during inference, significantly reducing computational demands while maintaining performance. Our framework demonstrates its versatility and efficacy through rigorous experimentation, achieving state-of-the-art performance in zero-shot COS with $F_{\beta }^{w}$ scores of 72.9% on CAMO and 71.7% on COD10K. By removing the M-LLM during inference, we achieve an inference speed comparable to that of traditional end-to-end models, reaching 18.1 FPS. Additionally, our method excels in polyp segmentation, and underwater scene segmentation, outperforming challenging baselines in both zero-shot and supervised settings, thereby highlighting its potential for broad applicability in diverse segmentation tasks.