Papers › TAda! Temporally-Adaptive Convolutions for Video Understanding

TAda! Temporally-Adaptive Convolutions for Video Understanding

12 Oct 2021ICLR 2022 4arXiv:2110.06178archive 2025-07-28

Ziyuan Huang, Shiwei Zhang, Liang Pan, Zhiwu Qing, Mingqian Tang, Ziwei Liu, Marcelo H. Ang Jr

Spatial convolutions are widely used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different frames. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modelling complex temporal dynamics in videos. Specifically, TAdaConv empowers the spatial convolutions with temporal modelling abilities by calibrating the convolution weights for each frame according to its local and global temporal context. Compared to previous temporal modelling operations, TAdaConv is more efficient as it operates over the convolution kernels instead of the features, whose dimension is an order of magnitude smaller than the spatial resolutions. Further, the kernel calibration brings an increased model capacity. We construct TAda2D and TAdaConvNeXt networks by replacing the 2D convolutions in ResNet and ConvNeXt with TAdaConv, which leads to at least on par or better performance compared to state-of-the-art approaches on multiple video action recognition and localization benchmarks. We also demonstrate that as a readily plug-in operation with negligible computation overhead, TAdaConv can effectively improve many existing video models with a convincing margin.

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alibaba-mmai-research/pytorch-video-understanding officialmentioned in papermentioned on GitHubpytorchMIT report
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TAdaConv2d alibaba-mmai-research/TAdaConv/tadaconv/models/module_zoo/ops/tadaconv.py community (archive-listed) unverified Apache-2.0 (permissive) · cf623eebda41b1bf · report

Tasks

Action ClassificationAction RecognitionTemporal Action LocalizationVideo Understanding

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Classification Kinetics-400 TAdaConvNeXt-T Acc@1 79.1 #120 of 207 Archive leaderboard report
Action Classification Kinetics-400 TAdaConvNeXt-T Acc@5 93.7 #120 of 207 Archive leaderboard report
Action Classification Kinetics-400 TAda2D-En (ResNet-50, 8+16 frames) Acc@1 78.2 #128 of 207 Archive leaderboard report
Action Classification Kinetics-400 TAda2D-En (ResNet-50, 8+16 frames) Acc@5 93.5 #128 of 207 Archive leaderboard report
Action Classification Kinetics-400 TAda2D (ResNet-50, 16 frames) Acc@1 77.4 #139 of 207 Archive leaderboard report
Action Classification Kinetics-400 TAda2D (ResNet-50, 16 frames) Acc@5 93.1 #139 of 207 Archive leaderboard report
Action Classification Kinetics-400 TAda2D (ResNet-50, 8 frames) Acc@1 76.7 #146 of 207 Archive leaderboard report
Action Classification Kinetics-400 TAda2D (ResNet-50, 8 frames) Acc@5 92.6 #146 of 207 Archive leaderboard report
Action Recognition Something-Something V2 TAda2D-En (ResNet-50, 8+16 frames) Top-1 Accuracy 67.2 #69 of 123 Archive leaderboard report
Action Recognition Something-Something V2 TAda2D-En (ResNet-50, 8+16 frames) Top-5 Accuracy 89.8 #69 of 123 Archive leaderboard report
Action Recognition Something-Something V2 TAdaConvNeXt-T Top-1 Accuracy 67.1 #71 of 123 Archive leaderboard report
Action Recognition Something-Something V2 TAdaConvNeXt-T Top-5 Accuracy 90.4 #71 of 123 Archive leaderboard report
Action Recognition Something-Something V2 TAda2D (ResNet-50, 16 frames) Top-1 Accuracy 65.6 #87 of 123 Archive leaderboard report
Action Recognition Something-Something V2 TAda2D (ResNet-50, 16 frames) Top-5 Accuracy 89.2 #87 of 123 Archive leaderboard report
Action Recognition Something-Something V2 TAda2D (ResNet-50, 8 frames) Top-1 Accuracy 64.0 #97 of 123 Archive leaderboard report
Action Recognition Something-Something V2 TAda2D (ResNet-50, 8 frames) Top-5 Accuracy 88.0 #97 of 123 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.

Methods

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionGlobal Average PoolingKaiming InitializationMax PoolingReLUResidual BlockResidual Connection

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