FOCAL: A Cost-Aware Video Dataset for Active Learning

• URL: http://arxiv.org/abs/2311.10591v1
• Date: Fri, 17 Nov 2023 15:46:09 GMT
• Title: FOCAL: A Cost-Aware Video Dataset for Active Learning
• Authors: Kiran Kokilepersaud, Yash-Yee Logan, Ryan Benkert, Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib, Enrique Corona, Kunjan Singh, Mostafa Parchami
• Abstract summary: annotation-cost refers to the time it takes an annotator to label and quality-assure a given video sequence. We introduce a set of conformal active learning algorithms that take advantage of the sequential structure of video data. We show that the best conformal active learning method is cheaper than the best traditional active learning method by 113 hours.
• Score: 13.886774655927875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning setting. Annotation-cost refers to the time it takes an annotator to label and quality-assure a given video sequence. A practical motivation for active learning research is to minimize annotation-cost by selectively labeling informative samples that will maximize performance within a given budget constraint. However, previous work in video active learning lacks real-time annotation labels for accurately assessing cost minimization and instead operates under the assumption that annotation-cost scales linearly with the amount of data to annotate. This assumption does not take into account a variety of real-world confounding factors that contribute to a nonlinear cost such as the effect of an assistive labeling tool and the variety of interactions within a scene such as occluded objects, weather, and motion of objects. FOCAL addresses this discrepancy by providing real annotation-cost labels for 126 video sequences across 69 unique city scenes with a variety of weather, lighting, and seasonal conditions. We also introduce a set of conformal active learning algorithms that take advantage of the sequential structure of video data in order to achieve a better trade-off between annotation-cost and performance while also reducing floating point operations (FLOPS) overhead by at least 77.67%. We show how these approaches better reflect how annotations on videos are done in practice through a sequence selection framework. We further demonstrate the advantage of these approaches by introducing two performance-cost metrics and show that the best conformal active learning method is cheaper than the best traditional active learning method by 113 hours.

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