{"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/stm-spatiotemporal-and-motion-encoding-for","title":"STM: SpatioTemporal and Motion Encoding for Action Recognition","arxiv_id":"1908.02486","date":"2019-08-07","proceeding":"ICCV 2019 10","authors":["Boyuan Jiang","Mengmeng Wang","Weihao Gan","Wei Wu","Junjie Yan"],"abstract":"Spatiotemporal and motion features are two complementary and crucial information for video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn spatiotemporal features and another flow stream to learn motion features. In this work, we aim to efficiently encode these two features in a unified 2D framework. To this end, we first propose an STM block, which contains a Channel-wise SpatioTemporal Module (CSTM) to present the spatiotemporal features and a Channel-wise Motion Module (CMM) to efficiently encode motion features. We then replace original residual blocks in the ResNet architecture with STM blcoks to form a simple yet effective STM network by introducing very limited extra computation cost. Extensive experiments demonstrate that the proposed STM network outperforms the state-of-the-art methods on both temporal-related datasets (i.e., Something-Something v1 & v2 and Jester) and scene-related datasets (i.e., Kinetics-400, UCF-101, and HMDB-51) with the help of encoding spatiotemporal and motion features together.","url_abs":"https://arxiv.org/abs/1908.02486v2","url_pdf":"https://arxiv.org/pdf/1908.02486v2.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":[],"tasks":[{"task_slug":"action-classification","task_name":"Action Classification"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"action-recognition-in-videos-2","task_name":"Action Recognition In Videos"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-classification-on-kinetics-400","task":"Action Classification","dataset":"Kinetics-400","model":"STM (ResNet-50)","rank_in_archive_order":167,"of":207,"metrics":{"Acc@1":"73.7"},"uses_additional_data":false},{"leaderboard":"/sota/action-recognition-in-videos-on-hmdb-51-1","task":"Action Recognition In Videos","dataset":"HMDB-51","model":"STM (ImageNet+Kinetics pretrain)","rank_in_archive_order":1,"of":1,"metrics":{"Average accuracy of 3 splits":"72.2"},"uses_additional_data":false},{"leaderboard":"/sota/action-recognition-in-videos-on-jester-1","task":"Action Recognition In Videos","dataset":"Jester (Gesture Recognition)","model":"STM (Resnet-50, 16 frames)","rank_in_archive_order":2,"of":9,"metrics":{"Val":"96.7"},"uses_additional_data":false},{"leaderboard":"/sota/action-recognition-in-videos-on-something-2","task":"Action Recognition In Videos","dataset":"Something-Something V1","model":"STM (16 frames, ImageNet pretraining)","rank_in_archive_order":1,"of":3,"metrics":{"Top 1 Accuracy":"50.7"},"uses_additional_data":false},{"leaderboard":"/sota/action-recognition-in-videos-on-something-3","task":"Action Recognition In Videos","dataset":"Something-Something V2","model":"STM (16 frames, ImageNet pretraining)","rank_in_archive_order":1,"of":4,"metrics":{"Top-1 Accuracy":"64.2","Top-5 Accuracy":"89.8"},"uses_additional_data":false},{"leaderboard":"/sota/action-recognition-in-videos-on-ucf101-2","task":"Action Recognition In Videos","dataset":"UCF101","model":"STM (ImageNet+Kinetics pretrain)","rank_in_archive_order":1,"of":5,"metrics":{"3-fold Accuracy":"96.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1908.02486","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}