Language Model Guided Interpretable Video Action Reasoning
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910395603615744 |
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| author | Wang, Ning Zhu, Guangming Li, HS Zhang, Liang Shah, Syed Afaq Ali Bennamoun, Mohammed |
| author_facet | Wang, Ning Zhu, Guangming Li, HS Zhang, Liang Shah, Syed Afaq Ali Bennamoun, Mohammed |
| contents | While neural networks have excelled in video action recognition tasks, their black-box nature often obscures the understanding of their decision-making processes. Recent approaches used inherently interpretable models to analyze video actions in a manner akin to human reasoning. These models, however, usually fall short in performance compared to their black-box counterparts. In this work, we present a new framework named Language-guided Interpretable Action Recognition framework (LaIAR). LaIAR leverages knowledge from language models to enhance both the recognition capabilities and the interpretability of video models. In essence, we redefine the problem of understanding video model decisions as a task of aligning video and language models. Using the logical reasoning captured by the language model, we steer the training of the video model. This integrated approach not only improves the video model's adaptability to different domains but also boosts its overall performance. Extensive experiments on two complex video action datasets, Charades & CAD-120, validates the improved performance and interpretability of our LaIAR framework. The code of LaIAR is available at https://github.com/NingWang2049/LaIAR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01591 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Language Model Guided Interpretable Video Action Reasoning Wang, Ning Zhu, Guangming Li, HS Zhang, Liang Shah, Syed Afaq Ali Bennamoun, Mohammed Computer Vision and Pattern Recognition While neural networks have excelled in video action recognition tasks, their black-box nature often obscures the understanding of their decision-making processes. Recent approaches used inherently interpretable models to analyze video actions in a manner akin to human reasoning. These models, however, usually fall short in performance compared to their black-box counterparts. In this work, we present a new framework named Language-guided Interpretable Action Recognition framework (LaIAR). LaIAR leverages knowledge from language models to enhance both the recognition capabilities and the interpretability of video models. In essence, we redefine the problem of understanding video model decisions as a task of aligning video and language models. Using the logical reasoning captured by the language model, we steer the training of the video model. This integrated approach not only improves the video model's adaptability to different domains but also boosts its overall performance. Extensive experiments on two complex video action datasets, Charades & CAD-120, validates the improved performance and interpretability of our LaIAR framework. The code of LaIAR is available at https://github.com/NingWang2049/LaIAR. |
| title | Language Model Guided Interpretable Video Action Reasoning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.01591 |