A Survey on Video Moment Localization

Li, Meng; Nie, Liqiang; Wang, Yunxiao; Wang, Meng; Rui, Yong

doi:10.1145/3556537

Cited by 9 publications

(2 citation statements)

References 114 publications

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“…For activity detection, a tIoU threshold was considered when matching the predicted activities to human annotated ones, following the standard practice 41 , 42 . The tIoU between a predicted activity and a human annotated activity was computed as the intersection of the two events divided by their union.…”

Section: Methodsmentioning

confidence: 99%

Deep learning to quantify care manipulation activities in neonatal intensive care units

Majeedi,

McAdams,

Kaur

et al. 2024

npj Digit. Med.

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Early-life exposure to stress results in significantly increased risk of neurodevelopmental impairments with potential long-term effects into childhood and even adulthood. As a crucial step towards monitoring neonatal stress in neonatal intensive care units (NICUs), our study aims to quantify the duration, frequency, and physiological responses of care manipulation activities, based on bedside videos and physiological signals. Leveraging 289 h of video recordings and physiological data within 330 sessions collected from 27 neonates in 2 NICUs, we develop and evaluate a deep learning method to detect manipulation activities from the video, to estimate their duration and frequency, and to further integrate physiological signals for assessing their responses. With a 13.8% relative error tolerance for activity duration and frequency, our results were statistically equivalent to human annotations. Further, our method proved effective for estimating short-term physiological responses, for detecting activities with marked physiological deviations, and for quantifying the neonatal infant stressor scale scores.

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Section: Methodsmentioning

confidence: 99%

Deep learning to quantify care manipulation activities in neonatal intensive care units

Majeedi,

McAdams,

Kaur

et al. 2024

npj Digit. Med.

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show abstract

“…The user then selects which videos to watch and discards the ones they don't like, resulting in wasted transmission resources. However, if not all the videos are transmitted, the user may 3 experience buffering or a decrease in video quality, which can significantly impact their viewing experience. This issue involves how to recommend videos to the user, whether to transmit all or part of the videos, and how to allocate video resources, among other challenges.…”

Section: Streamingmentioning

confidence: 99%

A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming

Zhou,

Wang,

Liu

et al. 2024

Preprint

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This paper offers an insightful examination of how currently top-trending AI technologies, i.e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology, including video generation, understanding, and streaming. It highlights the innovative use of these technologies in producing highly realistic videos, a significant leap in bridging the gap between real-world dynamics and digital creation. The study also delves into the advanced capabilities of LLMs in video understanding, demonstrating their effectiveness in extracting meaningful information from visual content, thereby enhancing our interaction with videos. In the realm of video streaming, the paper discusses how LLMs contribute to more efficient and user-centric streaming experiences, adapting content delivery to individual viewer preferences. This comprehensive review navigates through the current achievements, ongoing challenges, and future possibilities of applying Generative AI and LLMs to video-related tasks, underscoring the immense potential these technologies hold for advancing the field of video technology related to multimedia, networking, and AI communities.Impact Statement-This paper contributes to the field of video technology by examining the integration of Generative AI and Large Language Models (LLMs) in video generation, understanding, and streaming. Its exploration of these technologies offers a foundational understanding of their potential and limitations in enhancing the realism and interactivity of video content. The exploration of LLMs in video comprehension sets the stage for advancements in accessibility and interaction, promising enhanced educational tools, improved user interfaces, and advanced video analytics applications. Additionally, the paper underscores the role of LLMs in optimizing video streaming services, leading to more personalized and bandwidth-efficient platforms. This could substantially benefit the entertainment sector with adaptive streaming solutions tailored to individual preferences. By identifying key challenges and future research directions, the paper guides ongoing efforts to merge AI with video technology, while raising awareness about potential ethical issues. Its influence extends beyond academia, encouraging responsible AI development and policy-making in video technology, balancing technological advancements with ethical considerations.

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A survey of video-based human action recognition in team sports

Yin,

Sinnott,

Jayaputera

2024

Artif Intell Rev

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Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex actions involving multiple people interacting with objects. Actions in team sports exhibit a different nature compared to other sports, since they tend to occur at a faster pace and involve more human-human interactions. As a result, research has typically not focused on the challenges of HAR in team sports. This paper comprehensively summarises HAR-related research and applications with specific focus on team sports such as football (soccer), basketball and Australian rules football. Key datasets used for HAR-related team sports research are explored. Finally, common challenges and future work are discussed, and possible research directions identified.

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A Survey on Video Moment Localization

Cited by 9 publications

References 114 publications

Deep learning to quantify care manipulation activities in neonatal intensive care units

Deep learning to quantify care manipulation activities in neonatal intensive care units

A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming

A survey of video-based human action recognition in team sports

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