File size: 5,299 Bytes
8aa674c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | # G-TAD
> [G-TAD: Sub-Graph Localization for Temporal Action Detection](https://arxiv.org/abs/1911.11462)
> Mengmeng Xu, Chen Zhao, David S. Rojas, Ali Thabet, Bernard Ghanem
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## Abstract
Temporal action detection is a fundamental yet challenging task in video understanding. Video context is a critical cue to effectively detect actions, but current works mainly focus on temporal context, while neglecting semantic context as well as other important context properties. In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem. Specifically, we formulate video snippets as graph nodes, snippet-snippet correlations as edges, and actions associated with context as target sub-graphs. With graph convolution as the basic operation, we design a GCN block called GCNeXt, which learns the features of each node by aggregating its context and dynamically updates the edges in the graph. To localize each sub-graph, we also design an SGAlign layer to embed each sub-graph into the Euclidean space. Extensive experiments show that G-TAD is capable of finding effective video context without extra supervision and achieves state-of-the-art performance on two detection benchmarks. On ActivityNet-1.3, it obtains an average mAP of 34.09%; on THUMOS14, it reaches 51.6% at IoU@0.5 when combined with a proposal processing method.
## Results and Models
**ActivityNet-1.3** with CUHK classifier.
| Features | mAP@0.5 | mAP@0.75 | mAP@0.95 | ave. mAP | Config | Download |
| :------: | :-----: | :------: | :------: | :------: | :-------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| TSN | 50.19 | 35.04 | 8.11 | 34.18 | [config](anet_tsn.py) | [model](https://drive.google.com/file/d/1cyiTPZ73ud9-UxHFr7E4tkp7LEkM7_qE/view?usp=sharing) \| [log](https://drive.google.com/file/d/10MfgZlIS8XIc0sffXnIOgOR7RIXTd5Ve/view?usp=sharing) |
| TSP | 52.33 | 37.58 | 8.42 | 36.20 | [config](anet_tsp.py) | [model](https://drive.google.com/file/d/1CoBygy7JM26Rz7RTzIgKk0negmCa5Ier/view?usp=sharing) \| [log](https://drive.google.com/file/d/1ZQo2SI1TZdPkNL80FcslyForVaFDGTlB/view?usp=sharing) |
**THUMOS-14** with UtrimmedNet classifier
| Features | mAP@0.3 | mAP@0.4 | mAP@0.5 | mAP@0.6 | mAP@0.7 | ave. mAP | Config | Download |
| :------: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :---------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| TSN | 60.52 | 55.08 | 48.50 | 39.60 | 28.74 | 46.49 | [config](thumos_tsn.py) | [model](https://drive.google.com/file/d/1SCzVn2RuuIUQ3g2bktSukt0dmLR27ei0/view?usp=sharing) \| [log](https://drive.google.com/file/d/1VFID5LUY8p0mycYAOcjCWjNg9DkcdvEk/view?usp=sharing) |
| I3D | 63.35 | 59.07 | 51.76 | 42.65 | 31.66 | 49.70 | [config](thumos_i3d.py) | [model](https://drive.google.com/file/d/1qW80nahmt671AUR58PABzyBdQYqP9gO1/view?usp=sharing) \| [log](https://drive.google.com/file/d/12FvCQ3j0aP4qGvus0XJaNTJRxhcxIEa5/view?usp=sharing) |
## Train
You can use the following command to train a model.
```shell
torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py ${CONFIG_FILE} [optional arguments]
```
Example: train GTAD on ActivityNet dataset.
```shell
torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py configs/gtad/anet_tsp.py
```
For more details, you can refer to the Training part in the [Usage](../../docs/en/usage.md).
## Test
You can use the following command to test a model.
```shell
torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py ${CONFIG_FILE} --checkpoint ${CHECKPOINT_FILE} [optional arguments]
```
Example: test GTAD on ActivityNet dataset.
```shell
torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py configs/gtad/anet_tsp.py --checkpoint exps/anet/gtad_tsp_100x100/gpu1_id0/checkpoint/epoch_7.pth
```
For more details, you can refer to the Test part in the [Usage](../../docs/en/usage.md).
## Citation
```latex
@InProceedings{xu2020gtad,
author = {Xu, Mengmeng and Zhao, Chen and Rojas, David S. and Thabet, Ali and Ghanem, Bernard},
title = {G-TAD: Sub-Graph Localization for Temporal Action Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
``` |