Paper Info
Title
GroupViT: Semantic Segmentation Emerges from Text Supervision
Abstract
Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision from pixel-level recognition labels. Instead, in this paper, we propose to bring back the grouping mechanism into deep networks, which allows semantic segments to emerge automatically with only text supervision. We propose a hierarchical Grouping Vision Transformer (GroupViT), which goes beyond the regular grid structure representation and learns to group image regions into progressively larger arbitrary-shaped segments. We train GroupViT jointly with a text encoder on a large-scale image-text dataset via contrastive losses. With only text supervision and without any pixel-level annotations, GroupViT learns to group together semantic regions and successfully transfers to the task of semantic segmentation in a zero-shot manner, i.e., without any further fine-tuning. It achieves a zero-shot accuracy of 52.3% mIoU on the PASCAL VOC 2012 and 22.4% mIoU on PASCAL Context datasets, and performs competitively to state-of-the-art transfer-learning methods requiring greater levels of supervision. We open-source our code at https://github.com/NVlabs/GroupViT.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.01760
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Vision + language, Representation learning, Segmentation,grouping and shape analysis, Self-& semi-& meta- & unsupervised learning
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Jiarui Xu192.48
Shalini Gupta229920.42
Sifei Liu322717.54
Wonmin Byeon4678.42
Thomas M. Breuel52362219.10
Jan Kautz63615198.77
Xiaolong Wang771339.04