Huimin Ma, Sheng Yi, Shijie Chen, Jiansheng Chen, Yu Wang
{"title":"基于少量注释像素和点云的驾驶场景弱监督语义分割","authors":"Huimin Ma, Sheng Yi, Shijie Chen, Jiansheng Chen, Yu Wang","doi":"10.1007/s11263-024-02275-5","DOIUrl":null,"url":null,"abstract":"<p>Previous weakly supervised semantic segmentation (WSSS) methods mainly begin with the segmentation seeds from the CAM method. Because of the high complexity of driving scene images, their framework performs not well on driving scene datasets. In this paper, we propose a new kind of WSSS annotations on the complex driving scene dataset, with only one or several labeled points per category. This annotation is more lightweight than image-level annotation and provides critical localization information for prototypes. We propose a framework to address the WSSS task under this annotation, which generates prototype feature vectors from labeled points and then produces 2D pseudo labels. Besides, we found the point cloud data is useful for distinguishing different objects. Our framework could extract rich semantic information from unlabeled point cloud data and generate instance masks, which does not require extra annotation resources. We combine the pseudo labels and the instance masks to modify the incorrect regions and thus obtain more accurate supervision for training the semantic segmentation network. We evaluated this framework on the KITTI dataset. Experiments show that the proposed method achieves state-of-the-art performance.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"2022 1","pages":""},"PeriodicalIF":11.6000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Few Annotated Pixels and Point Cloud Based Weakly Supervised Semantic Segmentation of Driving Scenes\",\"authors\":\"Huimin Ma, Sheng Yi, Shijie Chen, Jiansheng Chen, Yu Wang\",\"doi\":\"10.1007/s11263-024-02275-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Previous weakly supervised semantic segmentation (WSSS) methods mainly begin with the segmentation seeds from the CAM method. Because of the high complexity of driving scene images, their framework performs not well on driving scene datasets. In this paper, we propose a new kind of WSSS annotations on the complex driving scene dataset, with only one or several labeled points per category. This annotation is more lightweight than image-level annotation and provides critical localization information for prototypes. We propose a framework to address the WSSS task under this annotation, which generates prototype feature vectors from labeled points and then produces 2D pseudo labels. Besides, we found the point cloud data is useful for distinguishing different objects. Our framework could extract rich semantic information from unlabeled point cloud data and generate instance masks, which does not require extra annotation resources. We combine the pseudo labels and the instance masks to modify the incorrect regions and thus obtain more accurate supervision for training the semantic segmentation network. We evaluated this framework on the KITTI dataset. Experiments show that the proposed method achieves state-of-the-art performance.</p>\",\"PeriodicalId\":13752,\"journal\":{\"name\":\"International Journal of Computer Vision\",\"volume\":\"2022 1\",\"pages\":\"\"},\"PeriodicalIF\":11.6000,\"publicationDate\":\"2024-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computer Vision\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11263-024-02275-5\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Vision","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11263-024-02275-5","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Few Annotated Pixels and Point Cloud Based Weakly Supervised Semantic Segmentation of Driving Scenes
Previous weakly supervised semantic segmentation (WSSS) methods mainly begin with the segmentation seeds from the CAM method. Because of the high complexity of driving scene images, their framework performs not well on driving scene datasets. In this paper, we propose a new kind of WSSS annotations on the complex driving scene dataset, with only one or several labeled points per category. This annotation is more lightweight than image-level annotation and provides critical localization information for prototypes. We propose a framework to address the WSSS task under this annotation, which generates prototype feature vectors from labeled points and then produces 2D pseudo labels. Besides, we found the point cloud data is useful for distinguishing different objects. Our framework could extract rich semantic information from unlabeled point cloud data and generate instance masks, which does not require extra annotation resources. We combine the pseudo labels and the instance masks to modify the incorrect regions and thus obtain more accurate supervision for training the semantic segmentation network. We evaluated this framework on the KITTI dataset. Experiments show that the proposed method achieves state-of-the-art performance.
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.