{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-stage-factorized-spatio-temporal","title":"Multi-stage Factorized Spatio-Temporal Representation for RGB-D Action and Gesture Recognition","arxiv_id":"2308.12006","date":"2023-08-23","proceeding":null,"authors":["Yujun Ma","Benjia Zhou","Ruili Wang","Pichao Wang"],"abstract":"RGB-D action and gesture recognition remain an interesting topic in human-centered scene understanding, primarily due to the multiple granularities and large variation in human motion. Although many RGB-D based action and gesture recognition approaches have demonstrated remarkable results by utilizing highly integrated spatio-temporal representations across multiple modalities (i.e., RGB and depth data), they still encounter several challenges. Firstly, vanilla 3D convolution makes it hard to capture fine-grained motion differences between local clips under different modalities. Secondly, the intricate nature of highly integrated spatio-temporal modeling can lead to optimization difficulties. Thirdly, duplicate and unnecessary information can add complexity and complicate entangled spatio-temporal modeling. To address the above issues, we propose an innovative heuristic architecture called Multi-stage Factorized Spatio-Temporal (MFST) for RGB-D action and gesture recognition. The proposed MFST model comprises a 3D Central Difference Convolution Stem (CDC-Stem) module and multiple factorized spatio-temporal stages. The CDC-Stem enriches fine-grained temporal perception, and the multiple hierarchical spatio-temporal stages construct dimension-independent higher-order semantic primitives. Specifically, the CDC-Stem module captures bottom-level spatio-temporal features and passes them successively to the following spatio-temporal factored stages to capture the hierarchical spatial and temporal features through the Multi- Scale Convolution and Transformer (MSC-Trans) hybrid block and Weight-shared Multi-Scale Transformer (WMS-Trans) block. The seamless integration of these innovative designs results in a robust spatio-temporal representation that outperforms state-of-the-art approaches on RGB-D action and gesture recognition datasets.","url_abs":"https://arxiv.org/abs/2308.12006v2","url_pdf":"https://arxiv.org/pdf/2308.12006v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-stage-factorized-spatio-temporal","repo_url":"https://github.com/damo-cv/motionrgbd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"}],"methods":[{"method_slug":"3d-convolution","method_name":"3D Convolution"},{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}