Cluster-Consensus Bayes for Open-Vocabulary Camouflaged Object Segmentation

Open-vocabulary camouflaged object segmentation requires recognizing visually subtle objects from unseen categories. Existing methods typically process each test image independently, overlooking the latent consensus among related samples. We introduce Cluster-Consensus Bayes (CCB), a lightweight, plug-and-play empirical Bayes module that turns this consensus into a source of semantic evidence. CCB groups unlabeled test images by visual similarity and estimates cluster-level class priors from their predicted categories. It then adaptively fuses these priors with individual vision-language predictions according to prediction confidence. This allows uncertain samples to draw on shared evidence while confident samples retain their individual information, without assigning a single label to an entire cluster. CCB supports iterative offline refinement across complementary encoder views and online inference through confidence-weighted centroid memory. Both modes refine predictions at test time without gradient updates, accommodating collection-based and streaming inference. Experiments across five base models and two additional single-target datasets demonstrate consistent recognition gains. On OVCamo, CLIP-only CCB improves OVCoser’s accuracy from 68.8% to 73.2%, with a 6.2% average relative gain across six class-aware segmentation metrics. An enhanced system combining auxiliary DINO feature fusion with multi-view CCB achieves a 14.3% average relative gain over original OVCoser.