| VIMPAC |
- Average accuracy of 3 splits
- 65.9
|
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning 2021-06-21 |
코드 1 |
| S:VGG-16, T:VGG-16 (ImageNet pretrained) |
- Average accuracy of 3 splits
- 65.4
|
Convolutional Two-Stream Network Fusion for Video Action Recognition 2016-04-22 |
코드 1 · 코드 2 |
| Dynamic Image Networks + IDT |
- Average accuracy of 3 splits
- 65.2
|
Dynamic Image Networks for Action Recognition 2016-06-01 |
코드 1 |
| LTC |
- Average accuracy of 3 splits
- 64.8
|
Long-term Temporal Convolutions for Action Recognition 2016-04-15 |
코드 1 |
| R-STAN-50 |
- Average accuracy of 3 splits
- 62.8
|
R-STAN: Residual Spatial-Temporal Attention Network for Action Recognition 2019-06-19 |
연결 없음 |
| DMC-Net (ResNet-18) |
- Average accuracy of 3 splits
- 62.8
|
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition 2019-01-11 |
연결 없음 |
| SUSiNet (multi, Kinetics pretrained) |
- Average accuracy of 3 splits
- 62.7
|
SUSiNet: See, Understand and Summarize it 2018-12-03 |
연결 없음 |
| Two-Stream (ImageNet pretrained) |
- Average accuracy of 3 splits
- 59.4
|
Two-Stream Convolutional Networks for Action Recognition in Videos 2014-06-09 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 |
| ActionFlowNet |
- Average accuracy of 3 splits
- 56.4
|
ActionFlowNet: Learning Motion Representation for Action Recognition 2016-12-09 |
연결 없음 |
| R-STAN-152 |
- Average accuracy of 3 splits
- 55.16
|
R-STAN: Residual Spatial-Temporal Attention Network for Action Recognition 2019-06-19 |
연결 없음 |
| Res3D |
- Average accuracy of 3 splits
- 54.9
|
ConvNet Architecture Search for Spatiotemporal Feature Learning 2017-08-16 |
코드 1 |
| R(2+1)D-18 (DistInit pretraining) |
- Average accuracy of 3 splits
- 54.8
|
DistInit: Learning Video Representations Without a Single Labeled Video 2019-01-26 |
연결 없음 |
| JRMN |
- Average accuracy of 3 splits
- 54.2
|
Pose And Joint-Aware Action Recognition 2020-10-16 |
코드 1 |
| CD-UAR |
- Average accuracy of 3 splits
- 51.8
|
Towards Universal Representation for Unseen Action Recognition 2018-03-22 |
연결 없음 |
| C3D |
- Average accuracy of 3 splits
- 51.6
|
Learning Spatiotemporal Features with 3D Convolutional Networks 2014-12-02 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 · 코드 10 · 코드 11 · 코드 12 · 코드 13 · 코드 14 · 코드 15 · 코드 16 · 코드 17 · 코드 18 · 코드 19 · 코드 20 · 코드 21 · 코드 22 · 코드 23 · 코드 24 · 코드 25 · 코드 26 · 코드 27 · 코드 28 · 코드 29 |
| R[2+1]D (VideoMoCo) |
- Average accuracy of 3 splits
- 49.2
|
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples 2021-03-10 |
코드 1 |
| 3D-ResNet-18 (VideoMoCo) |
- Average accuracy of 3 splits
- 43.6
|
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples 2021-03-10 |
코드 1 |