Progressive Task-Conditioned Correction for Semantic Segmentation

Hierarchical segmentation decoders repeatedly combine encoder and decoder representations, but their discrepancy provides candidate refinement content rather than a task update by itself. We formulate decoding as progressive task-conditioned correction and introduce DiCoSeg. In the encoder, Consistency-Guided Directional Aggregation (CGDA) adapts multi-directional state aggregation while retaining the pretrained equal-path computation as its initialization. In the decoder, each Corrective Fusion Block (CFB) estimates where a correction is supported and its signed task-space direction, transports supported corrections while preserving their support, and uses the resulting Corrective State to modulate discrepancy-derived features. A Persistent Task State carries this corrective context across decoder stages, turning hierarchical decoding into an evolving correction process rather than a sequence of isolated fusions. Across medical-image segmentation and natural-image scene parsing, DiCoSeg consistently improves segmentation quality, while controlled analyses support the intended separation between observable discrepancy and corrective action. DiCoSeg therefore separates two operations often coupled implicitly in hierarchical fusion: identifying representational discrepancy and deciding how that discrepancy should update the task representation.