Day2Dark:超越无声日光的伪监督活动识别

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
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引用次数: 0

摘要

本文致力于识别黑暗中和白天的活动。我们首先确定,最先进的活动识别器在白天是有效的,但在黑暗中却不可信。主要原因是可供学习的有标签的黑暗视频有限,以及测试时颜色对比度较低的分布变化。为了弥补标记暗视频的不足,我们引入了一种伪监督学习方案,利用容易获得的非标记和任务无关的暗视频来改进暗光下的活动识别器。由于较低的色彩对比度会导致视觉信息损失,我们进一步建议将互补的活动信息纳入音频中,因为音频不受光照影响。由于音频和视觉特征的有用性因光照度而异,因此我们推出了 "暗度适应 "视听识别器。在 EPIC-Kitchens、Kinetics-Sound 和 Charades 上进行的实验表明,我们的建议优于图像增强、域自适应和其他视听融合方法,甚至可以提高对遮挡物造成的局部黑暗的鲁棒性。项目页面:https://xiaobai1217.github.io/Day2Dark/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Day2Dark: Pseudo-Supervised Activity Recognition Beyond Silent Daylight

Day2Dark: Pseudo-Supervised Activity Recognition Beyond Silent Daylight

This paper strives to recognize activities in the dark, as well as in the day. We first establish that state-of-the-art activity recognizers are effective during the day, but not trustworthy in the dark. The main causes are the limited availability of labeled dark videos to learn from, as well as the distribution shift towards the lower color contrast at test-time. To compensate for the lack of labeled dark videos, we introduce a pseudo-supervised learning scheme, which utilizes easy to obtain unlabeled and task-irrelevant dark videos to improve an activity recognizer in low light. As the lower color contrast results in visual information loss, we further propose to incorporate the complementary activity information within audio, which is invariant to illumination. Since the usefulness of audio and visual features differs depending on the amount of illumination, we introduce our ‘darkness-adaptive’ audio-visual recognizer. Experiments on EPIC-Kitchens, Kinetics-Sound, and Charades demonstrate our proposals are superior to image enhancement, domain adaptation and alternative audio-visual fusion methods, and can even improve robustness to local darkness caused by occlusions. Project page: https://xiaobai1217.github.io/Day2Dark/.

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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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