Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

TL;DR

This paper introduces QA-CLIMS, a novel weakly supervised semantic segmentation method that leverages vision-language models and question-answering techniques to improve object localization and reduce background false-activations.

Contribution

It proposes a new framework combining question-answer prompting and contrastive learning to enhance CAM quality using open vocabulary textual information.

Findings

Achieves state-of-the-art results on PASCAL VOC 2012.

Outperforms existing methods on MS COCO dataset.

Reduces false-activation of background regions.

Abstract

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model's ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt Engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets. Code is available at: https://github.com/CVI-SZU/QA-CLIMS