Recently, the zero-shot semantic segmentation problem has attracted
increasing attention, and the best performing methods are based on two-stream
networks: one stream for proposal mask generation and the other for segment
classification using a pre-trained visual-language model. However, existing
two-stream methods require passing a great number of (up to a hundred) image
crops into the visuallanguage model, which is highly inefficient. To address
the problem, we propose a network that only needs a single pass through the
visual-language model for each input image. Specifically, we first propose a
novel network adaptation approach, termed patch severance, to restrict the
harmful interference between the patch embeddings in the pre-trained visual
encoder. We then propose classification anchor learning to encourage the
network to spatially focus on more discriminative features for classification.
Extensive experiments demonstrate that the proposed method achieves outstanding
performance, surpassing state-of-theart methods while being 4 to 7 times faster
at inference. We release our code at this https URL

该研究提出了一种零样本语义分割的模型，使用两个流模型解决提案掩模生成和使用预先训练的视觉 - 语言模型的段分类，并采用修补切断和分类锚学习来提高分类准确率和降低运行时间。