Non-Equilibrium Diffusion Pseudo-labelling and Relative Preference Optimisation for Unsupervised Camouflaged Object Detection

Unsupervised Camouflaged Object Detection (UCOD) is challenged by extreme visual entanglement between objects and surroundings. Existing methods struggle to integrate the heterogeneous cues of intra-object multi-camouflage, yielding fragmented pseudo-labels plagued by structural omissions and foreground-background confusion, which ultimately compels models to passively overfit initial errors rather than recover the complete object structure. To address these problems, we propose DES-UCOD, a novel Discover-Explore-Specialise framework for Unsupervised Camouflaged Object Detection that shifts from passive label fitting to active object discovery. Firstly, to resolve feature interference under multi-camouflage conditions and provide comprehensive candidate supervision, we propose the Non-Equilibrium Diffusion Pseudo-labelling (NEDP) strategy, which captures transient structural dynamics prior to global equilibrium to theoretically unify fragmented camouflage cues and discover robust structural anchors to generate high-quality pseudo-labels. Secondly, to mitigate the negative impact of pseudo-label noise and significantly alleviate confirmation bias, we propose the Stochastic Relative Preference Optimisation (S-RPO) strategy, an active reward-driven sampling mechanism that compels the model to explore decision boundaries highly robust against diverse visual variations during training. Finally, to enhance the model's capacity for handling diverse camouflage characteristics, we propose a Spatial-Cue Mixture-of-Experts (SC-MoE) decoder, which specialises in routing spatially heterogeneous features at each location, ensuring that distinct experts fulfil their respective roles to generate accurate predictions. Extensive experiments demonstrate that our model consistently outperforms various UCOD methods across multiple datasets to achieve state-of-the-art performance.