The field of few-shot segmentation is moving towards leveraging semantic information and multi-modal interaction to improve performance. Researchers are exploring ways to integrate textual descriptions and visual features to enhance segmentation accuracy. A key direction is the use of language-driven approaches, which utilize inherent target property language descriptions to build robust support strategies. Another area of focus is the development of frameworks that can handle intra-class variations and improve semantic consistency. Notable papers in this area include: CapeNext, which proposes a new framework that integrates hierarchical cross-modal interaction with dual-stream feature refinement, Unbiased Semantic Decoding with Vision Foundation Models for Few-shot Segmentation, which introduces an unbiased semantic decoding strategy integrated with the Segment Anything Model, Multi-Text Guided Few-Shot Semantic Segmentation, which proposes a dual-branch framework that enhances segmentation performance by fusing diverse textual prompts, Beyond Visual Cues: Leveraging General Semantics as Support for Few-Shot Segmentation, which introduces a Language-Driven Attribute Generalization architecture to utilize inherent target property language descriptions.