A Memory-Assisted Knowledge Transferring Framework with Curriculum Anticipation for Weakly Supervised Online Activity Detection

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Computer Vision Pub Date : 2024-10-28 DOI:10.1007/s11263-024-02279-1
Tianshan Liu, Kin-Man Lam, Bing-Kun Bao
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Abstract

As a crucial topic of high-level video understanding, weakly supervised online activity detection (WS-OAD) involves identifying the ongoing behaviors moment-to-moment in streaming videos, trained with solely cheap video-level annotations. It is essentially a challenging task, which requires addressing the entangled issues of the weakly supervised settings and online constraints. In this paper, we tackle the WS-OAD task from the knowledge-distillation (KD) perspective, which trains an online student detector to distill dual-level knowledge from a weakly supervised offline teacher model. To guarantee the completeness of knowledge transfer, we improve the vanilla KD framework from two aspects. First, we introduce an external memory bank to maintain the long-term activity prototypes, which serves as a bridge to align the activity semantics learned from the offline teacher and online student models. Second, to compensate the missing contexts of unseen near future, we leverage a curriculum learning paradigm to gradually train the online student detector to anticipate the future activity semantics. By dynamically scheduling the provided auxiliary future states, the online detector progressively distills contextual information from the offline model in an easy-to-hard course. Extensive experimental results on three public data sets demonstrate the superiority of our proposed method over the competing methods.

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用于弱监督在线活动检测的具有课程预测功能的记忆辅助知识转移框架
作为高级视频理解的一个重要课题,弱监督在线活动检测(WS-OAD)是指仅利用廉价的视频级注释进行训练,识别流媒体视频中每时每刻正在发生的行为。从本质上讲,这是一项具有挑战性的任务,需要解决弱监督设置和在线约束之间的纠缠不清的问题。在本文中,我们从知识提炼(KD)的角度来解决 WS-OAD 任务,即训练一个在线学生检测器,从弱监督离线教师模型中提炼出双层知识。为了保证知识转移的完整性,我们从两个方面改进了虚无的 KD 框架。首先,我们引入了一个外部记忆库来维护长期的活动原型,作为一座桥梁,将从离线教师模型和在线学生模型中学到的活动语义统一起来。其次,为了弥补近期未见语境的缺失,我们利用课程学习范式来逐步训练在线学生检测器,以预测未来的活动语义。通过动态调度所提供的辅助未来状态,在线检测器在由易到难的过程中逐步从离线模型中提炼出上下文信息。在三个公共数据集上的广泛实验结果表明,我们提出的方法优于其他竞争方法。
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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
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.
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