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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Graph Attention Based Proposal 3D ConvNets for Action Detection

February 1, 2023

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Authors

Jin Li

Beihang University


Xianglong Liu

Beihang University


Zhuofan Zong

Beihang University


Wanru Zhao

Beihang University


Mingyuan Zhang

Beihang University


Jingkuan Song

University of Electronic Science and Technology


DOI:

10.1609/aaai.v34i04.5893


Abstract:

The recent advances in 3D Convolutional Neural Networks (3D CNNs) have shown promising performance for untrimmed video action detection, employing the popular detection framework that heavily relies on the temporal action proposal generations as the input of the action detector and localization regressor. In practice the proposals usually contain strong intra and inter relations among them, mainly stemming from the temporal and spatial variations in the video actions. However, most of existing 3D CNNs ignore the relations and thus suffer from the redundant proposals degenerating the detection performance and efficiency. To address this problem, we propose graph attention based proposal 3D ConvNets (AGCN-P-3DCNNs) for video action detection. Specifically, our proposed graph attention is composed of intra attention based GCN and inter attention based GCN. We use intra attention to learn the intra long-range dependencies inside each action proposal and update node matrix of Intra Attention based GCN, and use inter attention to learn the inter dependencies between different action proposals as adjacency matrix of Inter Attention based GCN. Afterwards, we fuse intra and inter attention to model intra long-range dependencies and inter dependencies simultaneously. Another contribution is that we propose a simple and effective framewise classifier, which enhances the feature presentation capabilities of backbone model. Experiments on two proposal 3D ConvNets based models (P-C3D and P-ResNet) and two popular action detection benchmarks (THUMOS 2014, ActivityNet v1.3) demonstrate the state-of-the-art performance achieved by our method. Particularly, P-C3D embedded with our module achieves average mAP 3.7% improvement on THUMOS 2014 dataset compared to original model.

Topics: AAAI

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HOW TO CITE:

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song Graph Attention Based Proposal 3D ConvNets for Action Detection Proceedings of the AAAI Conference on Artificial Intelligence (2020) 4626-4633.

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song Graph Attention Based Proposal 3D ConvNets for Action Detection AAAI 2020, 4626-4633.

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song (2020). Graph Attention Based Proposal 3D ConvNets for Action Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 4626-4633.

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song. Graph Attention Based Proposal 3D ConvNets for Action Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.4626-4633.

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song. 2020. Graph Attention Based Proposal 3D ConvNets for Action Detection. "Proceedings of the AAAI Conference on Artificial Intelligence". 4626-4633.

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song. (2020) "Graph Attention Based Proposal 3D ConvNets for Action Detection", Proceedings of the AAAI Conference on Artificial Intelligence, p.4626-4633

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song, "Graph Attention Based Proposal 3D ConvNets for Action Detection", AAAI, p.4626-4633, 2020.

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song. "Graph Attention Based Proposal 3D ConvNets for Action Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.4626-4633.

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song. "Graph Attention Based Proposal 3D ConvNets for Action Detection". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 4626-4633.

Jin Li||Xianglong Liu||Zhuofan Zong||Wanru Zhao||Mingyuan Zhang||Jingkuan Song. Graph Attention Based Proposal 3D ConvNets for Action Detection. AAAI[Internet]. 2020[cited 2023]; 4626-4633.


ISSN: 2374-3468


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