Referring Image Segmentation (RIS) - the problem of identifying objects in images through natural language sentences - is a challenging task currently mostly solved through supervised learning. However, while collecting referred annotation masks is a time-consuming process, the few existing weakly-supervised and zero-shot approaches fall significantly short in performance compared to fully-supervised learning ones. To bridge the performance gap without mask annotations, we propose a novel weakly-supervised framework that tackles RIS by decomposing it into three steps: obtaining instance masks for the object mentioned in the referencing instruction (segment), using zero-shot learning to select a potentially correct mask for the given instruction (select), and bootstrapping a model which allows for fixing the mistakes of zero-shot selection (correct). In our experiments, using only the first two steps (zero-shot segment and select) outperforms other zero-shot baselines by as much as 16.5%, while our full method improves upon this much stronger baseline and sets the new state-of-the-art for weakly-supervised RIS, reducing the gap between the weakly-supervised and fully-supervised methods in some cases from around 33% to as little as 7%. Code is available at https://github.com/fgirbal/segment-select-correct.
@article{arxiv.2310.13479,
title = {Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation},
author = {Francisco Eiras and Kemal Oksuz and Adel Bibi and Philip H. S. Torr and Puneet K. Dokania},
journal= {arXiv preprint arXiv:2310.13479},
year = {2024}
}
Comments
Accepted to ECCV'24 Workshop Proceedings (Instance-Level Recognition Workshop)