File size: 6,556 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 73 74 75 76 77 78 79 80 81 82 83 | # TriDet
> [TriDet: Temporal Action Detection with Relative Boundary Modeling](https://arxiv.org/abs/2303.07347)
> Dingfeng Shi, Yujie Zhong, Qiong Cao, Lin Ma, Jia Li, Dacheng Tao
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## Abstract
In this paper, we present a one-stage framework TriDet for temporal action detection. Existing methods often suffer from imprecise boundary predictions due to the ambiguous action boundaries in videos. To alleviate this problem, we propose a novel Trident-head to model the action boundary via an estimated relative probability distribution around the boundary. In the feature pyramid of TriDet, we propose an efficient Scalable-Granularity Perception (SGP) layer to mitigate the rank loss problem of self-attention that takes place in the video features and aggregate information across different temporal granularities. Benefiting from the Trident-head and the SGP-based feature pyramid, TriDet achieves state-of-the-art performance on three challenging benchmarks: THUMOS14, HACS and EPIC-KITCHEN 100, with lower computational costs, compared to previous methods. For example, TriDet hits an average mAP of 69.3% on THUMOS14, outperforming the previous best by 2.5%, but with only 74.6% of its latency.
## Results and Models
**ActivityNet-1.3** with CUHK classifier.
| Features | mAP@0.5 | mAP@0.75 | mAP@0.95 | ave. mAP | Config | Download |
| :------: | :-----: | :------: | :------: | :------: | :-------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| TSP | 54.84 | 37.46 | 7.98 | 36.51 | [config](anet_tsp.py) | [model](https://drive.google.com/file/d/1qUDzT6xSIKOlx8cHJoYJL8S6eC_c4K3I/view?usp=sharing) \| [log](https://drive.google.com/file/d/1IANbg_ueEg-F75dkgute5BVkUNUUodzX/view?usp=sharing) |
**THUMOS-14**
| Features | mAP@0.3 | mAP@0.4 | mAP@0.5 | mAP@0.6 | mAP@0.7 | ave. mAP | Config | Download |
| :------: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :---------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| I3D | 84.46 | 81.05 | 73.41 | 62.58 | 46.51 | 69.60 | [config](thumos_i3d.py) | [model](https://drive.google.com/file/d/18nQ2jJMJkkqeCfWzj-OfV0Hi_Ej3Y1lN/view?usp=sharing) \| [log](https://drive.google.com/file/d/1YLXoZoliAD1jLHa2Lxtct0EFtpgabesM/view?usp=sharing) |
**HACS**
| Features | mAP@0.5 | mAP@0.75 | mAP@0.95 | ave. mAP | Config | Download |
| :------: | :-----: | :------: | :------: | :------: | :------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| SlowFast | 56.84 | 39.04 | 11.13 | 38.47 | [config](hacs_slowfast.py) | [model](https://drive.google.com/file/d/13e34x-kXRWTm9jNkvv61D10vRdBtRgZ_/view?usp=sharing) \| [log](https://drive.google.com/file/d/1RGwwtenzxiZ5IBw7SdF9V2bFNk5TBZq1/view?usp=sharing) |
**Epic-Kitchens-100**
| Subset | Features | mAP@0.1 | mAP@0.2 | mAP@0.3 | mAP@0.4 | mAP@0.5 | ave. mAP | Config | Download |
| :----: | :------: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :--------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Noun | SlowFast | 24.95 | 23.76 | 22.22 | 20.00 | 16.63 | 21.51 | [config](epic_kitchens_slowfast_noun.py) | [model](https://drive.google.com/file/d/18F0Ruc9RJL9KLOjIEFvBGBQzXxNUkJgH/view?usp=sharing) \| [log](https://drive.google.com/file/d/1Tfi4KyKmnanO2F3rry1hIRDtlvKZDRTT/view?usp=sharing) |
| Verb | SlowFast | 27.88 | 27.00 | 25.52 | 23.74 | 20.72 | 24.97 | [config](epic_kitchens_slowfast_verb.py) | [model](https://drive.google.com/file/d/1LIGCduQFPXKQl98s53h_feIYNLBsXkXd/view?usp=sharing) \| [log](https://drive.google.com/file/d/10xZSt7r6T_tauqvc9UHi3bCiloZPATDp/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 TriDet on THUMOS dataset.
```shell
torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py configs/tridet/thumos_i3d.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 TriDet on THUMOS dataset.
```shell
torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py configs/tridet/thumos_i3d.py --checkpoint exps/thumos/tridet_i3d/gpu1_id0/checkpoint/epoch_37.pth
```
For more details, you can refer to the Test part in the [Usage](../../docs/en/usage.md).
## Citation
```latex
@inproceedings{shi2023tridet,
title={TriDet: Temporal Action Detection with Relative Boundary Modeling},
author={Shi, Dingfeng and Zhong, Yujie and Cao, Qiong and Ma, Lin and Li, Jia and Tao, Dacheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={18857--18866},
year={2023}
}
``` |