Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation

Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level fea-ture maps from the frozen visual encoder to compute the pixel- wise similarity as a key prior guidance for the decoder. However, such a prior representation suffers from coarse granularity and poor generalization to new classes since these high-level feature maps have obvious category bias. In this work, we propose to replace the visual prior representation with the visual-text alignment capacity to capture more reliable guidance and enhance the model generalization. Specifically, we design two kinds of trainingfree prior information generation strategy that attempts to utilize the semantic alignment capability of the Contrastive Language-Image Pre-training model (CLIP) to locate the target class. Besides, to acquire more accurate prior guidance, we build a high-order relationship of attention maps and utilize it to refine the initial prior information. Experiments on both the PASCAL-5i and COCO-20i datasets show that our method obtains a clearly substantial improvement and reaches the new state-of-the-art performance. The code is available on the project website11https://github.com/vangjin/PI-CLIP.