A Sliding Window Scheme for Online Temporal Action Localization

Kim, Young Hwi; Kang, Hyolim; Kim, Seon Joo

doi:10.1007/978-3-031-19830-4_37

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…Apart from the semi-supervised setting, Kang et al [208] developed the temporal action localization task in an online setting, constraining the algorithm to process streaming videos without accessing future frames. Subsequently, Kim et al [209] improved the online setting by integrating the anchor mechanism.…”

Section: Further Discussion and Promising Directionsmentioning

confidence: 99%

Temporal Action Localization in the Deep Learning Era: A Survey

Wang,

Zhao,

Yang

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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The temporal action localization research aims to discover action instances from untrimmed videos, representing a fundamental step in the field of intelligent video understanding. With the advent of deep learning, backbone networks have been instrumental in providing representative spatiotemporal features, while the end-to-end learning paradigm has enabled the development of high-quality models through data-driven training. Both supervised and weakly supervised learning approaches have contributed to the rapid progress of temporal action localization, resulting in a multitude of methods and a large body of literature, making a comprehensive survey a pressing necessity. This paper presents a thorough analysis of existing action localization works, offering a well-organized taxonomy that highlights the strengths and weaknesses of each strategy. In the realm of supervised learning, in addition to the anchor mechanism, we introduce a novel classification mechanism to categorize and summarize existing works. Similarly, for weakly supervised learning, we extend the traditional pre-classification and post-classification mechanisms by providing a fresh perspective on enhancement strategies. Furthermore, we shed light on the bottleneck of confidence estimation, a critical yet overlooked aspect of current works. By conducting detailed analyses, this survey serves as a valuable resource for researchers, providing beneficial guidance to newcomers and inspiring seasoned researchers alike.

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Section: Further Discussion and Promising Directionsmentioning

confidence: 99%