Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation
This work addresses the challenge of few-shot segmentation for unseen classes, which is an incremental improvement over existing methods.
The paper tackled the problem of coarse and poorly generalizing prior representations in few-shot segmentation by replacing visual prior with CLIP's visual-text alignment, achieving substantial improvement and new state-of-the-art performance on PASCAL-5{i} and COCO-20{i} datasets.
Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature 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 training-free 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-5{i} and COCO-20{i} datasets show that our method obtains a clearly substantial improvement and reaches the new state-of-the-art performance.