SegDis: Segment First, Then Discriminate for Unified Salient and Camouflaged Object Detection

Existing joint learning approaches for salient object detection (SOD) and camouflaged object detection (COD) typically formulate both tasks as binary foreground segmentation, making it difficult to distinguish salient from camouflaged objects when they coexist. We uncover a deeper limitation through bidirectional cross-task transfer: models trained exclusively on SOD generalize surprisingly well to COD, and vice versa. This reveals that strong segmentation performance does not necessarily imply reliable object-type discrimination, suggesting that existing models primarily rely on generic foreground cues rather than task-specific characteristics of saliency and camouflage. Motivated by this observation, we propose Segment First, Then Discriminate (SegDis), a unified framework that explicitly decouples foreground discovery from object-type discrimination. SegDis first identifies task-agnostic foreground regions and then determines whether foreground pixels are salient or camouflaged by conditioning class-specific type queries on the relation between foreground and surrounding background. A subsequent uncertainty–boundary refinement stage resolves ambiguous regions and sharpens object boundaries, yielding a three-class prediction of background, salient objects, and camouflaged objects. On USC12K, SegDis achieves 71.82% FG-mIoU and 80.25% mIoU, outperforming USCNet by 3.36 and 2.22 percentage points, respectively, while reducing the Camouflage-Saliency Confusion Score (CSCS) from 7.49 to 3.27. On Scene-C, where salient and camouflaged objects coexist, SegDis obtains 73.82% IoU-S and 51.60% IoU-C, improving IoU-C by 5.87 percentage points over USCNet. These results demonstrate that decoupling foreground discovery from object-type discrimination improves unified three-class parsing and reduces salient–camouflaged confusion in mixed scenes.