CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Prompting
This work addresses finer-grained semantic segmentation for natural scene and remote sensing imagery, particularly in flood disaster scenarios, but appears incremental as it builds on existing language-guided methods.
The paper tackles the problem of enhancing image semantic segmentation by integrating a novel Chain-of-Thought language prompting framework, resulting in improved performance as validated by qualitative and quantitative analyses, though no concrete numbers are provided.
Natural scene analysis and remote sensing imagery offer immense potential for advancements in large-scale language-guided context-aware data utilization. This potential is particularly significant for enhancing performance in downstream tasks such as object detection and segmentation with designed language prompting. In light of this, we introduce the CPSeg, Chain-of-Thought Language Prompting for Finer-grained Semantic Segmentation), an innovative framework designed to augment image segmentation performance by integrating a novel "Chain-of-Thought" process that harnesses textual information associated with images. This groundbreaking approach has been applied to a flood disaster scenario. CPSeg encodes prompt texts derived from various sentences to formulate a coherent chain-of-thought. We propose a new vision-language dataset, FloodPrompt, which includes images, semantic masks, and corresponding text information. This not only strengthens the semantic understanding of the scenario but also aids in the key task of semantic segmentation through an interplay of pixel and text matching maps. Our qualitative and quantitative analyses validate the effectiveness of CPSeg.