Emergent Open-Vocabulary Semantic Segmentation from Off-the-Shelf Vision-Language Models

Abstract

From image-text pairs large-scale vision-language models (VLMs) learn to implicitly associate image regions with words which prove effective for tasks like visual question answering. However leveraging the learned association for open-vocabulary semantic segmentation remains a challenge. In this paper we propose a simple yet extremely effective training-free technique Plug-and-Play Open-Vocabulary Semantic Segmentation (PnP-OVSS) for this task. PnP-OVSS leverages a VLM with direct text-to-image cross-attention and an image-text matching loss. To balance between over-segmentation and under-segmentation we introduce Salience Dropout; by iteratively dropping patches that the model is most attentive to we are able to better resolve the entire extent of the segmentation mask. PnP-OVSS does not require any neural network training and performs hyperparameter tuning without the need for any segmentation annotations even for a validation set. PnP-OVSS demonstrates substantial improvements over comparable baselines (+29.4% mIoU on Pascal VOC +13.2% mIoU on Pascal Context +14.0% mIoU on MS COCO +2.4% mIoU on COCO Stuff) and even outperforms most baselines that conduct additional network training on top of pretrained VLMs. Our codebase is at https://github.com/letitiabanana/PnP-OVSS.