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5 months ago
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning
Piergiovanni AJ ; Kuo Weicheng ; Angelova Anelia

Abstract
We present a simple approach which can turn a ViT encoder into an efficientvideo model, which can seamlessly work with both image and video inputs. Bysparsely sampling the inputs, the model is able to do training and inferencefrom both inputs. The model is easily scalable and can be adapted tolarge-scale pre-trained ViTs without requiring full finetuning. The modelachieves SOTA results and the code will be open-sourced.
Code Repositories
daniel-code/TubeViT
pytorch
Mentioned in GitHub
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| action-classification-on-charades | TubeViT-L | MAP: 66.2 |
| action-classification-on-kinetics-400 | TubeVit-L (ImageNet-1k) | Acc@1: 90.2 Acc@5: 98.6 FLOPs (G) x views: 95300x4x3 Parameters (M): 307 |
| action-classification-on-kinetics-400 | TubeViT-H (ImageNet-1k) | Acc@1: 90.9 Acc@5: 98.9 FLOPs (G) x views: 176400x4x3 Parameters (M): 632 |
| action-classification-on-kinetics-400 | TubeVit-B (ImageNet-1k) | Acc@1: 88.6 Acc@5: 97.6 FLOPs (G) x views: 8700x3x4 Parameters (M): 86 |
| action-classification-on-kinetics-600 | TubeVit-L | Top-1 Accuracy: 91.5 Top-5 Accuracy: 98.7 |
| action-classification-on-kinetics-600 | TubeVit-B | Top-1 Accuracy: 90.9 Top-5 Accuracy: 97.3 |
| action-classification-on-kinetics-600 | TubeVit-H | Top-1 Accuracy: 91.8 Top-5 Accuracy: 98.9 |
| action-classification-on-kinetics-700 | TubeViT-L | Top-1 Accuracy: 83.8 Top-5 Accuracy: 96.6 |
| action-recognition-in-videos-on-something | TubeViT-L | Top-1 Accuracy: 76.1 Top-5 Accuracy: 95.2 |
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