Papers › Graph-Based High-Order Relation Modeling for Long-Term Action Recognition

Graph-Based High-Order Relation Modeling for Long-Term Action Recognition

19 Jun 2021CVPR 2021 1archive 2025-07-28

Jiaming Zhou, Kun-Yu Lin, Haoxin Li, Wei-Shi Zheng

Long-term actions involve many important visual concepts, e.g., objects, motions, and sub-actions, and there are various relations among these concepts, which we call basic relations. These basic relations will jointly affect each other during the temporal evolution of long-term actions, which forms the high-order relations that are essential for long-term action recognition. In this paper, we propose a Graph-based High-order Relation Modeling (GHRM) module to exploit the high-order relations in the long-term actions for long-term action recognition. In GHRM, each basic relation in the long-term actions will be modeled by a graph, where each node represents a segment in a long video. Moreover, when modeling each basic relation, the information from all the other basic relations will be incorporated by GHRM, and thus the high-order relations in the long-term actions can be well exploited. To better exploit the high-order relations along the time dimension, we design a GHRM-layer consisting of a Temporal-GHRM branch and a Semantic-GHRM branch, which aims to model the local temporal high-order relations and global semantic high-order relations. The experimental results on three long-term action recognition datasets, namely, Breakfast, Charades, and MultiThumos, demonstrate the effectiveness of our model.

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Tasks

Action RecognitionLong-video Activity RecognitionVideo ClassificationVocal Bursts Intensity Prediction

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Long-video Activity Recognition Breakfast GHRM (I3D-K400-Pretrain-feature) mAP 65.86 #5 of 8 Archive leaderboard report
Video Classification Breakfast GHRM Accuracy (%) 75.5 #7 of 9 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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