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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 1: AAAI-22 Technical Tracks 1

DCAN: Improving Temporal Action Detection via Dual Context Aggregation

February 1, 2023

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Authors

Guo Chen

Nanjing University


Yin-Dong Zheng

Nanjing University, China


Limin Wang

Nanjing University


Tong Lu

Nanjing University


DOI:

10.1609/aaai.v36i1.19900


Abstract:

Temporal action detection aims to locate the boundaries of action in the video. The current method based on boundary matching enumerates and calculates all possible boundary matchings to generate proposals. However, these methods neglect the long-range context aggregation in boundary prediction. At the same time, due to the similar semantics of adjacent matchings, local semantic aggregation of densely-generated matchings cannot improve semantic richness and discrimination. In this paper, we propose the end-to-end proposal generation method named Dual Context Aggregation Network (DCAN) to aggregate context on two levels, namely, boundary level and proposal level, for generating high-quality action proposals, thereby improving the performance of temporal action detection. Specifically, we design the Multi-Path Temporal Context Aggregation (MTCA) to achieve smooth context aggregation on boundary level and precise evaluation of boundaries. For matching evaluation, Coarse-to-fine Matching (CFM) is designed to aggregate context on the proposal level and refine the matching map from coarse to fine. We conduct extensive experiments on ActivityNet v1.3 and THUMOS-14. DCAN obtains an average mAP of 35.39% on ActivityNet v1.3 and reaches mAP 54.14% at IoU@0.5 on THUMOS-14, which demonstrates DCAN can generate high-quality proposals and achieve state-of-the-art performance. We release the code at https://github.com/cg1177/DCAN.

Topics: AAAI

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HOW TO CITE:

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu DCAN: Improving Temporal Action Detection via Dual Context Aggregation Proceedings of the AAAI Conference on Artificial Intelligence (2022) 248-257.

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu DCAN: Improving Temporal Action Detection via Dual Context Aggregation AAAI 2022, 248-257.

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu (2022). DCAN: Improving Temporal Action Detection via Dual Context Aggregation. Proceedings of the AAAI Conference on Artificial Intelligence, 248-257.

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu. DCAN: Improving Temporal Action Detection via Dual Context Aggregation. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.248-257.

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu. 2022. DCAN: Improving Temporal Action Detection via Dual Context Aggregation. "Proceedings of the AAAI Conference on Artificial Intelligence". 248-257.

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu. (2022) "DCAN: Improving Temporal Action Detection via Dual Context Aggregation", Proceedings of the AAAI Conference on Artificial Intelligence, p.248-257

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu, "DCAN: Improving Temporal Action Detection via Dual Context Aggregation", AAAI, p.248-257, 2022.

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu. "DCAN: Improving Temporal Action Detection via Dual Context Aggregation". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.248-257.

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu. "DCAN: Improving Temporal Action Detection via Dual Context Aggregation". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 248-257.

Guo Chen||Yin-Dong Zheng||Limin Wang||Tong Lu. DCAN: Improving Temporal Action Detection via Dual Context Aggregation. AAAI[Internet]. 2022[cited 2023]; 248-257.


ISSN: 2374-3468


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