TriDet
TriDet: Temporal Action Detection with Relative Boundary Modeling
Dingfeng Shi, Yujie Zhong, Qiong Cao, Lin Ma, Jia Li, Dacheng Tao
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 | model | log |
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 | model | log |
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 | model | log |
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 | model | log |
| Verb | SlowFast | 27.88 | 27.00 | 25.52 | 23.74 | 20.72 | 24.97 | config | model | log |
Train
You can use the following command to train a model.
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.
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.
Test
You can use the following command to test a model.
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.
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.
Citation
@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}
}