Papers › Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
Amir Shahroudy, Tian-Tsong Ng, Yihong Gong, Gang Wang
Single modality action recognition on RGB or depth sequences has been extensively explored recently. It is generally accepted that each of these two modalities has different strengths and limitations for the task of action recognition. Therefore, analysis of the RGB+D videos can help us to better study the complementary properties of these two types of modalities and achieve higher levels of performance. In this paper, we propose a new deep autoencoder based shared-specific feature factorization network to separate input multimodal signals into a hierarchy of components. Further, based on the structure of the features, a structured sparsity learning machine is proposed which utilizes mixed norms to apply regularization within components and group selection between them for better classification performance. Our experimental results show the effectiveness of our cross-modality feature analysis framework by achieving state-of-the-art accuracy for action classification on five challenging benchmark datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Recognition | NTU RGB+D | DSSCA-SSLM (RGB only) | Accuracy (CS) | 74.9 | #28 of 28 | Archive leaderboard | report |
| Multimodal Activity Recognition | MSR Daily Activity3D dataset | DSSCA-SSLM (RGB+D) | Accuracy | 97.5 | #1 of 6 | 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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