AP-TransNet: a polarized transformer based aerial human action recognition framework

Dhiman, Chhavi; Varshney, Anunay; Vyapak, Ved

doi:10.1007/s00138-024-01535-1

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2024

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Multimed Tools Appl

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Insights into aerial intelligence: assessing CNN-based algorithms for human action recognition and object detection in diverse environments

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Mewada

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Multimed Tools Appl

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Performance Comparison of Five Different Neural Networks for Human Activity Recognition

Zeng

2024

2024 9th International Conference on Electronic Technology and Information Science (ICETIS)

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An integrated framework for multi-granular explanation of video summarization

Tsigos,

Apostolidis,

Mezaris

2024

Front. Signal Process.

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In this paper, we propose an integrated framework for multi-granular explanation of video summarization. This framework integrates methods for producing explanations both at the fragment level (indicating which video fragments influenced the most the decisions of the summarizer) and the more fine-grained visual object level (highlighting which visual objects were the most influential for the summarizer). To build this framework, we extend our previous work on this field, by investigating the use of a model-agnostic, perturbation-based approach for fragment-level explanation of the video summarization results, and introducing a new method that combines the results of video panoptic segmentation with an adaptation of a perturbation-based explanation approach to produce object-level explanations. The performance of the developed framework is evaluated using a state-of-the-art summarization method and two datasets for benchmarking video summarization. The findings of the conducted quantitative and qualitative evaluations demonstrate the ability of our framework to spot the most and least influential fragments and visual objects of the video for the summarizer, and to provide a comprehensive set of visual-based explanations about the output of the summarization process.

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AP-TransNet: a polarized transformer based aerial human action recognition framework

Cited by 3 publications

References 50 publications

Insights into aerial intelligence: assessing CNN-based algorithms for human action recognition and object detection in diverse environments

Insights into aerial intelligence: assessing CNN-based algorithms for human action recognition and object detection in diverse environments

Performance Comparison of Five Different Neural Networks for Human Activity Recognition

An integrated framework for multi-granular explanation of video summarization

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