Lianghui Zhu, Xinggang Wang, Jiapei Feng, Tianheng Cheng, Yingyue Li, Bo Jiang, Dingwen Zhang, Junwei Han
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引用次数: 0
Abstract
Contrastive language and image pre-training (CLIP) achieves great success in various computer vision tasks and also presents an opportune avenue for enhancing weakly-supervised image understanding with its large-scale pre-trained knowledge. As an effective way to reduce the reliance on pixel-level human-annotated labels, weakly-supervised semantic segmentation (WSSS) aims to refine the class activation map (CAM) and produce high-quality pseudo masks. Weakly-supervised semantic segmentation (WSSS) aims to refine the class activation map (CAM) as pseudo masks, but heavily relies on inductive biases like hand-crafted priors and digital image processing methods. For the vision-language pre-trained model, i.e. CLIP, we propose a novel text-to-pixel matching paradigm for WSSS. However, directly applying CLIP to WSSS is challenging due to three critical problems: (1) the task gap between contrastive pre-training and WSSS CAM refinement, (2) lacking text-to-pixel modeling to fully utilize the pre-trained knowledge, and (3) the insufficient details owning to the \(\frac{1}{16}\) down-sampling resolution of ViT. Thus, we propose WeakCLIP to address the problems and leverage the pre-trained knowledge from CLIP to WSSS. Specifically, we first address the task gap by proposing a pyramid adapter and learnable prompts to extract WSSS-specific representation. We then design a co-attention matching module to model text-to-pixel relationships. Finally, the pyramid adapter and text-guided decoder are introduced to gather multi-level information and integrate it with text guidance hierarchically. WeakCLIP provides an effective and parameter-efficient way to transfer CLIP knowledge to refine CAM. Extensive experiments demonstrate that WeakCLIP achieves the state-of-the-art WSSS performance on standard benchmarks, i.e., 74.0% mIoU on the val set of PASCAL VOC 2012 and 46.1% mIoU on the val set of COCO 2014. The source code and model checkpoints are released at https://github.com/hustvl/WeakCLIP.
期刊介绍:
The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs.
Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision.
Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community.
Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas.
In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives.
The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research.
Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.