Papers › ViViT: A Video Vision Transformer

ViViT: A Video Vision Transformer

29 Mar 2021ICCV 2021 10arXiv:2103.15691archive 2025-07-28

Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid

We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification. Our model extracts spatio-temporal tokens from the input video, which are then encoded by a series of transformer layers. In order to handle the long sequences of tokens encountered in video, we propose several, efficient variants of our model which factorise the spatial- and temporal-dimensions of the input. Although transformer-based models are known to only be effective when large training datasets are available, we show how we can effectively regularise the model during training and leverage pretrained image models to be able to train on comparatively small datasets. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple video classification benchmarks including Kinetics 400 and 600, Epic Kitchens, Something-Something v2 and Moments in Time, outperforming prior methods based on deep 3D convolutional networks. To facilitate further research, we release code at https://github.com/google-research/scenic/tree/main/scenic/projects/vivit

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Code

Syntology Ran 14 of 21 code samples harvested from 4 repositories linked to this paper; 7 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · fixture could not drive it; 12 ran with no contract checked.

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google-research/scenic officialmentioned in papermentioned on GitHubjax report
KSonPham/ViVit-a-Pytorch-implementation mentioned on GitHubpytorch report
SforAiDl/vformer mentioned on GitHubpytorch report
drv-agwl/ViViT-pytorch mentioned on GitHubpytorch report
mx-mark/videotransformer-pytorch mentioned on GitHubpytorch report
rishikksh20/ViViT-pytorch mentioned on GitHubpytorch report
MS-P3/code7 mindspore report
pwc-1/Paper-9 mindspore report

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1ran · honoured contract
1ran · fixture could not drive it
12ran
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Attention KSonPham/ViVit-a-Pytorch-implementation/models/modeling.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · 9650a9a4002b76d8 · report
BaseClassificationModel SforAiDl/vformer/vformer/models/classification/vivit.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · f7522b96704eba2a · report
Embeddings3D KSonPham/ViVit-a-Pytorch-implementation/models/modeling.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · 4a0e3dfe29d9f024 · report
FDATransformerEncoder drv-agwl/ViViT-pytorch/models.py community (archive-listed) ran MIT (permissive) · 74f97add2ae8db6e · report
FDAttention drv-agwl/ViViT-pytorch/models.py community (archive-listed) ran MIT (permissive) · f4f644381533aebd · report
FSATransformerEncoder drv-agwl/ViViT-pytorch/models.py community (archive-listed) ran MIT (permissive) · fc843fa02ae99869 · report
FSAttention drv-agwl/ViViT-pytorch/models.py community (archive-listed) ran MIT (permissive) · 2261806fbb78432d · report
MLPDecoder SforAiDl/vformer/vformer/models/classification/vivit.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · f04db9370cd8032a · report
Mlp KSonPham/ViVit-a-Pytorch-implementation/models/modeling.py community (archive-listed) ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · 35965045e79a53f5 · report
Registry SforAiDl/vformer/vformer/models/classification/vivit.py community (archive-listed) ran MIT (permissive) · 20fb836cf685bd0e · report
Transformer rishikksh20/ViViT-pytorch/vivit.py community (archive-listed) ran · metamorphic tier: invariant MIT (permissive) · acb104726aac5d2f · report
ViViT rishikksh20/ViViT-pytorch/vivit.py community (archive-listed) ran MIT (permissive) · 2aecd388beda0ef3 · report
pair SforAiDl/vformer/vformer/models/classification/vivit.py community (archive-listed) ran · honoured contract fingerprinted MIT (permissive) · 7deb3c28da2b4c81 · report
Block KSonPham/ViVit-a-Pytorch-implementation/models/modeling.py community (archive-listed) unverified MIT (permissive) · 3aeaa3af2d538769 · report
Encoder KSonPham/ViVit-a-Pytorch-implementation/models/modeling.py community (archive-listed) unverified MIT (permissive) · a13ffe0da0765ccb · report
MyViViT KSonPham/ViVit-a-Pytorch-implementation/models/modeling.py community (archive-listed) unverified MIT (permissive) · f3febe06012a3d59 · report
Transformer KSonPham/ViVit-a-Pytorch-implementation/models/modeling.py community (archive-listed) unverified MIT (permissive) · 085322a052a6aa56 · report
VanillaEncoder SforAiDl/vformer/vformer/models/classification/vivit.py community (archive-listed) unverified MIT (permissive) · a639c271341e65f0 · report
ViViTBackbone drv-agwl/ViViT-pytorch/models.py community (archive-listed) unverified MIT (permissive) · 893bed9821ea5367 · report
ViViTModel2 SforAiDl/vformer/vformer/models/classification/vivit.py community (archive-listed) unverified MIT (permissive) · e4a0f20e13c6ea01 · report
np2th identical code first harvested elsewhere ran · fixture could not drive it licence of this copy not recorded · 7d8eaf5537f699e4 · report

Tasks

Action ClassificationAction RecognitionClassificationGeneral ClassificationImage ClassificationVideo Classificationimage-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Classification Kinetics-400 ViViT-H/16x2 (JFT) Acc@1 84.9 #61 of 207 Archive leaderboard report
Action Classification Kinetics-400 ViViT-H/16x2 (JFT) Acc@5 95.8 #61 of 207 Archive leaderboard report
Action Classification Kinetics-400 ViViT-L/16x2 320 Acc@5 94.7 #203 of 207 Archive leaderboard report
Action Classification Kinetics-600 ViViT-H/16x2 (JFT) Top-1 Accuracy 85.8 #29 of 65 Archive leaderboard report
Action Classification Kinetics-600 ViViT-H/16x2 (JFT) Top-5 Accuracy 96.5 #29 of 65 Archive leaderboard report
Action Classification Kinetics-600 ViViT-L/16x2 Top-1 Accuracy 84.3 #34 of 65 Archive leaderboard report
Action Classification Kinetics-600 ViViT-L/16x2 Top-5 Accuracy 95.6 #34 of 65 Archive leaderboard report
Action Classification Kinetics-600 ViViT-L/16x2 (320x320) Top-1 Accuracy 83.0 #42 of 65 Archive leaderboard report
Action Classification Kinetics-600 ViViT-L/16x2 (320x320) Top-5 Accuracy 95.7 #42 of 65 Archive leaderboard report
Action Classification MiT ViViT-L/16x2 Top 5 Accuracy 64.9 #28 of 29 Archive leaderboard report
Action Recognition EPIC-KITCHENS-100 ViViT-L/16x2 Fact. encoder Action@1 44.0 #23 of 32 Archive leaderboard report
Action Recognition EPIC-KITCHENS-100 ViViT-L/16x2 Fact. encoder Noun@1 56.8 #23 of 32 Archive leaderboard report
Action Recognition EPIC-KITCHENS-100 ViViT-L/16x2 Fact. encoder Verb@1 66.4 #23 of 32 Archive leaderboard report
Action Recognition Something-Something V2 ViViT-L/16x2 Fact. encoder Top-1 Accuracy 65.4 #88 of 123 Archive leaderboard report
Action Recognition Something-Something V2 ViViT-L/16x2 Fact. encoder Top-5 Accuracy 89.8 #88 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.

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