Papers › Multimodal Fusion via Teacher-Student Network for Indoor Action Recognition

Multimodal Fusion via Teacher-Student Network for Indoor Action Recognition

18 May 2021Association for the Advancement of Artificial Intelligence (AAAI) 2021 5archive 2025-07-28

Bruce X.B. Yu, Yan Liu, Keith C.C. Chan

Indoor action recognition plays an important role in modern society, such as intelligent healthcare in large mobile cabin hospitals. With the wide usage of depth sensors like Kinect, multimodal information including skeleton and RGB modalities brings a promising way to improve the performance. However, existing methods are either focusing on a single data modality or failed to take the advantage of multiple data modalities. In this paper, we propose a Teacher-Student Multimodal Fusion (TSMF) model that fuses the skeleton and RGB modalities at the model level for indoor action recognition. In our TSMF, we utilize a teacher network to transfer the structural knowledge of the skeleton modality to a student network for the RGB modality. With extensive experiments on two benchmarking datasets: NTU RGB+D and PKU-MMD, results show that the proposed TSMF consistently performs better than state-of-the-art single modal and multimodal methods. It also indicates that our TSMF could not only improve the accuracy of the student network but also significantly improve the ensemble accuracy.

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bruceyo/TSMF mentioned in paperpytorch report

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Tasks

Action RecognitionAction Recognition In VideosBenchmarking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Recognition NTU RGB+D TSMF (RGB + Pose) Accuracy (CS) 92.5 #18 of 28 Archive leaderboard report
Action Recognition NTU RGB+D TSMF (RGB + Pose) Accuracy (CV) 97.4 #18 of 28 Archive leaderboard report
Action Recognition In Videos PKU-MMD TSMF X-Sub 95.8 #4 of 5 Archive leaderboard report
Action Recognition In Videos PKU-MMD TSMF X-View 97.8 #4 of 5 Archive leaderboard report

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