Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation
Ji-Jia Wu Andy Chia-Hao Chang Chieh-Yu Chuang Chun-Pei Chen Yu-Lun Liu Min-Hung Chen Hou-Ning Hu Yung-Yu Chuang Yen-Yu Lin
This paper addresses text-supervised semantic segmentation, aiming to learn a
model capable of segmenting arbitrary visual concepts within images by using
only image-text pairs without dense annotations. Existing methods have
demonstrated that contrastive learning on image-text pairs effectively aligns
visual segments with the meanings of texts. We notice that there is a
discrepancy between text alignment and semantic segmentation: A text often
consists of multiple semantic concepts, whereas semantic segmentation strives
to create semantically homogeneous segments. To address this issue, we propose
a novel framework, Image-Text Co-Decomposition (CoDe), where the paired image
and text are jointly decomposed into a set of image regions and a set of word
segments, respectively, and contrastive learning is developed to enforce
region-word alignment. To work with a vision-language model, we present a
prompt learning mechanism that derives an extra representation to highlight an
image segment or a word segment of interest, with which more effective features
can be extracted from that segment. Comprehensive experimental results
demonstrate that our method performs favorably against existing text-supervised
semantic segmentation methods on six benchmark datasets.