Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, Diana Marculescu

Abstract:

Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during
training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-trained vision-language models, e.g., CLIP, to classify
masked regions. We identify the performance bottleneck of this paradigm to be
the pre-trained CLIP model, since it does not perform well on masked images.
To address this, we propose to finetune CLIP on a collection of masked image
regions and their corresponding text descriptions. We collect training data by
mining an existing image-caption dataset (e.g., COCO Captions), using CLIP to
match masked image regions to nouns in the image captions. Compared with the
more precise and manually annotated segmentation labels with fixed classes (e.g.,
COCO-Stuff), we find our noisy but diverse dataset can better retain CLIP’s generalization ability. Along with finetuning the entire model, we utilize the “blank”
areas in masked images using a method we dub mask prompt tuning. Experiments
demonstrate mask prompt tuning brings significant improvement without modifying any weights of CLIP, and it can further improve a fully finetuned model. In
particular, when trained on COCO and evaluated on ADE20K-150, our best model
achieves 29.6% mIoU, which is +8.5% higher than the previous state-of-the-art.
For the first time, open-vocabulary generalist models match the performance of
supervised specialist models in 2017 without dataset specific adaptations. Project
page: https://jeff-liangf.github.io/projects/ovseg