Qiang Qi, Zhenyu Qiu, Yan Yan, Yang Lu, Hanzi Wang
{"title":"IMC-Det:用于视频物体检测的跨模态对比学习","authors":"Qiang Qi, Zhenyu Qiu, Yan Yan, Yang Lu, Hanzi Wang","doi":"10.1007/s11263-024-02201-9","DOIUrl":null,"url":null,"abstract":"<p>Video object detection is an important yet challenging task in the computer vision field. One limitation of off-the-shelf video object detection methods is that they only explore information from the visual modality, without considering the semantic knowledge of the textual modality due to the large inter-modality discrepancies, resulting in limited detection performance. In this paper, we propose a novel intra–inter modality contrastive learning network for high-performance video object detection (IMC-Det), which includes three substantial improvements over existing methods. First, we design an intra-modality contrastive learning module to pull close similar features while pushing apart dissimilar ones, enabling our IMC-Det to learn more discriminative feature representations. Second, we develop a graph relational feature aggregation module to effectively model the structural relations between features by leveraging cross-graph learning and residual graph convolution, which is conducive to performing more effective feature aggregation in the spatio-temporal domain. Third, we present an inter-modality contrastive learning module to enforce the visual features belonging to same classes to be compactly gathered around the corresponding textual semantic representations, endowing our IMC-Det with better object classification capability. We conduct extensive experiments on the challenging ImageNet VID dataset, and the experimental results demonstrate that our IMC-Det performs favorably against existing state-of-the-art methods. More remarkably, our IMC-Det achieves 85.5% mAP and 86.7% mAP with ResNet-101 and ResNeXt-101, respectively.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"26 1","pages":""},"PeriodicalIF":11.6000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IMC-Det: Intra–Inter Modality Contrastive Learning for Video Object Detection\",\"authors\":\"Qiang Qi, Zhenyu Qiu, Yan Yan, Yang Lu, Hanzi Wang\",\"doi\":\"10.1007/s11263-024-02201-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Video object detection is an important yet challenging task in the computer vision field. One limitation of off-the-shelf video object detection methods is that they only explore information from the visual modality, without considering the semantic knowledge of the textual modality due to the large inter-modality discrepancies, resulting in limited detection performance. In this paper, we propose a novel intra–inter modality contrastive learning network for high-performance video object detection (IMC-Det), which includes three substantial improvements over existing methods. First, we design an intra-modality contrastive learning module to pull close similar features while pushing apart dissimilar ones, enabling our IMC-Det to learn more discriminative feature representations. Second, we develop a graph relational feature aggregation module to effectively model the structural relations between features by leveraging cross-graph learning and residual graph convolution, which is conducive to performing more effective feature aggregation in the spatio-temporal domain. Third, we present an inter-modality contrastive learning module to enforce the visual features belonging to same classes to be compactly gathered around the corresponding textual semantic representations, endowing our IMC-Det with better object classification capability. We conduct extensive experiments on the challenging ImageNet VID dataset, and the experimental results demonstrate that our IMC-Det performs favorably against existing state-of-the-art methods. More remarkably, our IMC-Det achieves 85.5% mAP and 86.7% mAP with ResNet-101 and ResNeXt-101, respectively.</p>\",\"PeriodicalId\":13752,\"journal\":{\"name\":\"International Journal of Computer Vision\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":11.6000,\"publicationDate\":\"2024-08-23\",\"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/s11263-024-02201-9\",\"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/s11263-024-02201-9","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}
IMC-Det: Intra–Inter Modality Contrastive Learning for Video Object Detection
Video object detection is an important yet challenging task in the computer vision field. One limitation of off-the-shelf video object detection methods is that they only explore information from the visual modality, without considering the semantic knowledge of the textual modality due to the large inter-modality discrepancies, resulting in limited detection performance. In this paper, we propose a novel intra–inter modality contrastive learning network for high-performance video object detection (IMC-Det), which includes three substantial improvements over existing methods. First, we design an intra-modality contrastive learning module to pull close similar features while pushing apart dissimilar ones, enabling our IMC-Det to learn more discriminative feature representations. Second, we develop a graph relational feature aggregation module to effectively model the structural relations between features by leveraging cross-graph learning and residual graph convolution, which is conducive to performing more effective feature aggregation in the spatio-temporal domain. Third, we present an inter-modality contrastive learning module to enforce the visual features belonging to same classes to be compactly gathered around the corresponding textual semantic representations, endowing our IMC-Det with better object classification capability. We conduct extensive experiments on the challenging ImageNet VID dataset, and the experimental results demonstrate that our IMC-Det performs favorably against existing state-of-the-art methods. More remarkably, our IMC-Det achieves 85.5% mAP and 86.7% mAP with ResNet-101 and ResNeXt-101, respectively.
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.