This is an official implementaion of paper "Attention in Attention: Modeling Context Correlation for Efficient Video Classification", which has been accepted by IEEE TCVST 2022. Paper link
The code is built with following libraries:
For video data pre-processing, you may need ffmpeg.
Here we provide some of the pretrained models.
Model | Frame view clip | Top-1 Acc. | Top-5 Acc. | Checkpoint |
---|---|---|---|---|
AIA(TSN) ResNet50 | 8 1 1 | 48.5% | 77.2% | [link]() |
Model | Frame view clip | Top-1 Acc. | Top-5 Acc. | Checkpoint |
---|---|---|---|---|
AIA(TSN) ResNet50 | 8 1 1 | 60.3% | 86.4% | [link]() |
Model | Frame view clip | Top-1 Acc. | Checkpoint |
---|---|---|---|
AIA(TSN) ResNet50 | 8 1 1 | 79.3% | [link]() |
AIA(TSM) ResNet50 | 8 1 1 | 79.4% | [link]() |
Model | Frame view clip | Split1 | Split2 | Split3 |
---|---|---|---|---|
AIA(TSN) ResNet50 | 8 1 1 | 63.7% | 62.1% | 61.5% |
AIA(TSN) ResNet50 | 8 1 1 | 64.7% | 63.3% | 62.2% |
GC codes are jointly written and owned by Dr. Yanbin Hao and [Dr. Shuo Wang].
Thanks for the following Github projects: