  Recently, pre-trained vision-language models have been increasingly used to
tackle the challenging zero-shot segmentation task. Typical solutions follow
the paradigm of first generating mask proposals and then adopting CLIP to
classify them. To maintain the CLIP's zero-shot transferability, previous
practices favour to freeze CLIP during training. However, in the paper, we
reveal that CLIP is insensitive to different mask proposals and tends to
produce similar predictions for various mask proposals of the same image. This
insensitivity results in numerous false positives when classifying mask
proposals. This issue mainly relates to the fact that CLIP is trained with
image-level supervision. To alleviate this issue, we propose a simple yet
effective method, named Mask-aware Fine-tuning (MAFT). Specifically,
Image-Proposals CLIP Encoder (IP-CLIP Encoder) is proposed to handle arbitrary
numbers of image and mask proposals simultaneously. Then, mask-aware loss and
self-distillation loss are designed to fine-tune IP-CLIP Encoder, ensuring CLIP
is responsive to different mask proposals while not sacrificing
transferability. In this way, mask-aware representations can be easily learned
to make the true positives stand out. Notably, our solution can seamlessly plug
into most existing methods without introducing any new parameters during the
fine-tuning process. We conduct extensive experiments on the popular zero-shot
benchmarks. With MAFT, the performance of the state-of-the-art methods is
promoted by a large margin: 50.4% (+ 8.2%) on COCO, 81.8% (+ 3.2%) on
Pascal-VOC, and 8.7% (+4.3%) on ADE20K in terms of mIoU for unseen classes. The
code is available at https://github.com/jiaosiyu1999/MAFT.git.
