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.

通过将视觉与文本对齐能力作为先验表示，本研究提出了两种训练无关的先验信息生成策略，以更可靠的指导并增强模型的泛化能力。在 PASCAL-5i 和 COCO-20i 数据集上的实验证明我们的方法在少样本分割任务中取得了明显的改进，并达到了新的最先进性能。