CoPRS: Learning Positional Prior from Chain-of-Thought for Reasoning Segmentation
This addresses the need for more interpretable and precise reasoning segmentation in computer vision, though it appears incremental as it builds on existing chain-of-thought and segmentation methods.
The paper tackles the problem of limited interpretability and semantic detail in reasoning segmentation by introducing CoPRS, a model that uses a differentiable heatmap as a positional prior to bridge language reasoning to segmentation, achieving state-of-the-art or matching performance on RefCOCO series and ReasonSeg benchmarks.
Existing works on reasoning segmentation either connect hidden features from a language model directly to a mask decoder or represent positions in text, which limits interpretability and semantic detail. To solve this, we present CoPRS, a Multi-modal Chain-of-Thought (MCoT)-based positional perception model that bridges language reasoning to segmentation through a differentiable and interpretable positional prior instantiated as a heatmap. By making the reasoning process clear via MCoT and expressing it as a dense, differentiable heatmap, this interface enhances interpretability and diagnostic analysis and yields more concentrated evidence on the target. A learnable concentration token aggregates features of the image and reasoning text to generate this positional prior, which is decoded to precise masks through a lightweight decoder, providing a direct connection between reasoning and segmentation. Across the RefCOCO series and ReasonSeg, CoPRS matches or surpasses the best reported metrics on each standard split under comparable protocols, with performance at or above prior state of the art across both validation and test partitions. Extensive experiments reveal that the quality of the heatmap strongly influences the resulting mask quality, supporting a consistent association between the reasoning output and downstream mask generation. Collectively, these findings support the utility of this paradigm in bridging reasoning and segmentation and show advantages in concentration driven by reasoning and predicting masks more precisely. Code, checkpoints and logs are released at https://github.com/ZhenyuLU-Heliodore/CoPRS.git.