Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision

TL;DR

Unpair-Seg introduces a weakly-supervised framework for open-vocabulary segmentation that learns from unpaired image-mask and image-text data, reducing annotation costs and improving performance.

Contribution

It proposes a novel method to perform open-vocabulary segmentation using unpaired supervision, addressing noise issues with a vision-language model and multi-scale matching.

Findings

Achieves 14.6% mIoU on ADE-847 dataset.

Achieves 19.5% mIoU on PASCAL Context-459 dataset.

Narrowing the gap with fully-supervised methods.

Abstract

Current state-of-the-art open-vocabulary segmentation methods typically rely on image-mask-text triplet annotations for supervision. However, acquiring such detailed annotations is labour-intensive and poses scalability challenges in complex real-world scenarios. While existing weakly-supervised approaches leverage image-text pairs to reduce the expansive annotation cost, the lack of mask supervision makes it difficult for the model to locate multiple instances and accurately group pixels with similar semantics, significantly hampering versatility and performance. In this paper, we introduce Unpair-Seg, a novel weakly-supervised open-vocabulary segmentation framework that learns from unpaired image-mask and image-text pairs, which can be independently and efficiently collected. Unpair-Seg initially predicts a set of binary masks and generates pseudo labels by identifying confident pairs of masks and text entities. We then train a feature adapter to align region embeddings with text embeddings based on these pseudo labels, achieving open-vocabulary segmentation. However, the inherent noise in the mask-entity correspondence poses a challenge to obtaining reliable pairs. To address this, we employ a vision-language large model to re-caption the input images and extract precise entities, and we design a multi-scale matching strategy to reduce noisy mask-entity pairs. Our Unpair-Seg framework demonstrates impressive performance, achieving 14.6\% and 19.5\% mIoU on the ADE-847 and PASCAL Context-459 datasets, significantly narrowing the gap between fully-supervised and weakly-supervised methods.