Papers › DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning

DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning

25 May 2021arXiv:2105.12085archive 2025-07-28

Wenhao Wu, Yuxiang Zhao, Yanwu Xu, Xiao Tan, Dongliang He, Zhikang Zou, Jin Ye, YingYing Li, Mingde Yao, ZiChao Dong, Yifeng Shi

Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition. Most of the state-of-the-arts focus on short-range spatio-temporal modeling and then average multiple snippet-level predictions to yield the final video-level prediction. Thus, their video-level prediction does not consider spatio-temporal features of how video evolves along the temporal dimension. In this paper, we introduce a novel Dynamic Segment Aggregation (DSA) module to capture relationship among snippets. To be more specific, we attempt to generate a dynamic kernel for a convolutional operation to aggregate long-range temporal information among adjacent snippets adaptively. The DSA module is an efficient plug-and-play module and can be combined with the off-the-shelf clip-based models (i.e., TSM, I3D) to perform powerful long-range modeling with minimal overhead. The final video architecture, coined as DSANet. We conduct extensive experiments on several video recognition benchmarks (i.e., Mini-Kinetics-200, Kinetics-400, Something-Something V1 and ActivityNet) to show its superiority. Our proposed DSA module is shown to benefit various video recognition models significantly. For example, equipped with DSA modules, the top-1 accuracy of I3D ResNet-50 is improved from 74.9% to 78.2% on Kinetics-400. Codes are available at https://github.com/whwu95/DSANet.

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auto_fp16 whwu95/DSANet/codes/core/fp16/decorators.py official repository unverified MIT (permissive) · 2a69c55d04d13a71 · report
build whwu95/DSANet/codes/models/builder.py official repository unverified MIT (permissive) · 67976edf323e2f17 · report
cast_tensor_type whwu95/DSANet/codes/core/fp16/utils.py official repository unverified MIT (permissive) · 7b91d5069635a376 · report
collate whwu95/DSANet/codes/core/parallel/collate.py official repository unverified MIT (permissive) · 1082ce7b7af2c3f7 · report
confusion_matrix whwu95/DSANet/codes/core/evaluation/accuracy.py official repository unverified MIT (permissive) · f739d3fb27d6f608 · report
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get_input_device whwu95/DSANet/codes/core/parallel/_functions.py official repository unverified MIT (permissive) · 72b89f53f5854825 · report
master_only whwu95/DSANet/codes/core/dist_utils.py official repository unverified MIT (permissive) · 6a0d65723b2675dc · report
mean_class_accuracy whwu95/DSANet/codes/core/evaluation/accuracy.py official repository unverified MIT (permissive) · 0ebabe504dafca4b · report
patch_forward_method whwu95/DSANet/codes/core/fp16/hooks.py official repository unverified MIT (permissive) · e059fcc920f08632 · report
patch_norm_fp32 whwu95/DSANet/codes/core/fp16/hooks.py official repository unverified MIT (permissive) · f0c7f23268d50c1b · report
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Tasks

Action RecognitionLong-range modelingRepresentation LearningVideo Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Recognition ActivityNet DSANet (w/ 3D ResNet50) mAP 90.5 #6 of 16 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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