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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 5: AAAI-22 Technical Tracks 5

Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains

February 1, 2023

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Authors

Hua Hua

Australian National University


Dongxu Li

The Australian National University


Ruiqi Li

Australian National University


Peng Zhang

Australian National University


Jochen Renz

Australian National University


Anthony Cohn

University of Leeds


DOI:

10.1609/aaai.v36i5.20513


Abstract:

In order to be trusted by humans, Artificial Intelligence agents should be able to describe rationales behind their decisions. One such application is human action recognition in critical or sensitive scenarios, where trustworthy and explainable action recognizers are expected. For example, reliable pedestrian action recognition is essential for self-driving cars and explanations for real-time decision making are critical for investigations if an accident happens. In this regard, learning-based approaches, despite their popularity and accuracy, are disadvantageous due to their limited interpretability. This paper presents a novel neuro-symbolic approach that recognizes actions from videos with human-understandable explanations. Specifically, we first propose to represent videos symbolically by qualitative spatial relations between objects called qualitative spatial object relation chains. We further develop a neural saliency estimator to capture the correlation between such object relation chains and the occurrence of actions. Given an unseen video, this neural saliency estimator is able to tell which object relation chains are more important for the action recognized. We evaluate our approach on two real-life video datasets, with respect to recognition accuracy and the quality of generated action explanations. Experiments show that our approach achieves superior performance on both aspects to previous symbolic approaches, thus facilitating trustworthy intelligent decision making. Our approach can be used to augment state-of-the-art learning approaches with explainabilities.

Topics: AAAI

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HOW TO CITE:

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains Proceedings of the AAAI Conference on Artificial Intelligence (2022) 5710-5718.

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains AAAI 2022, 5710-5718.

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn (2022). Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains. Proceedings of the AAAI Conference on Artificial Intelligence, 5710-5718.

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn. Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.5710-5718.

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn. 2022. Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains. "Proceedings of the AAAI Conference on Artificial Intelligence". 5710-5718.

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn. (2022) "Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains", Proceedings of the AAAI Conference on Artificial Intelligence, p.5710-5718

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn, "Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains", AAAI, p.5710-5718, 2022.

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn. "Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.5710-5718.

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn. "Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 5710-5718.

Hua Hua||Dongxu Li||Ruiqi Li||Peng Zhang||Jochen Renz||Anthony Cohn. Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains. AAAI[Internet]. 2022[cited 2023]; 5710-5718.


ISSN: 2374-3468


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