| # BMN | |
| > [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://arxiv.org/abs/1907.09702) | |
| > Tianwei Lin, Xiao Liu, Xin Li, Errui Ding, Shilei Wen | |
| <!-- [ALGORITHM] --> | |
| ## Abstract | |
| Temporal action proposal generation is an challenging and promising task which aims to locate temporal regions in real-world videos where action or event may occur. Current bottom-up proposal generation methods can generate proposals with precise boundary, but cannot efficiently generate adequately reliable confidence scores for retrieving proposals. To address these difficulties, we introduce the Boundary-Matching (BM) mechanism to evaluate confidence scores of densely distributed proposals, which denote a proposal as a matching pair of starting and ending boundaries and combine all densely distributed BM pairs into the BM confidence map. Based on BM mechanism, we propose an effective, efficient and end-to-end proposal generation method, named Boundary-Matching Network (BMN), which generates proposals with precise temporal boundaries as well as reliable confidence scores simultaneously. The two-branches of BMN are jointly trained in an unified framework. We conduct experiments on two challenging datasets: THUMOS-14 and ActivityNet-1.3, where BMN shows significant performance improvement with remarkable efficiency and generalizability. Further, combining with existing action classifier, BMN can achieve state-of-the-art temporal action detection performance. | |
| ## Results and Models | |
| **ActivityNet-1.3** with CUHK classifier. | |
| | Features | mAP@0.5 | mAP@0.75 | mAP@0.95 | ave. mAP | Config | Download | | |
| | :------: | :-----: | :------: | :------: | :------: | :-------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | |
| | TSN | 50.97 | 34.98 | 8.35 | 34.21 | [config](anet_tsn.py) | [model](https://drive.google.com/file/d/1yWu05_BS2RuHnItIpFvKVieiYOGWbzNj/view?usp=sharing) \| [log](https://drive.google.com/file/d/1KtICNUOt_IVEWfSXqZ-6R_msKm40AaTU/view?usp=sharing) | | |
| | TSP | 52.90 | 37.30 | 9.67 | 36.40 | [config](anet_tsp.py) | [model](https://drive.google.com/file/d/1sQQble7270Ja5Nx8kR5tApeJGGS42t6M/view?usp=sharing) \| [log](https://drive.google.com/file/d/1sxXjeIzEt0Nx_YZNj_tCcAsN9_TJe6xU/view?usp=sharing) | | |
| Use above checkpoints to evaluate the recall performance: | |
| | Features | AR@1 | AR@5 | AR@10 | AR@100 | AUC | Config | Download | | |
| | :------: | :---: | :---: | :---: | :----: | :---: | :--------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | |
| | TSN | 33.58 | 49.16 | 56.53 | 75.34 | 67.23 | [config](anet_tsn_recall.py) | [model](https://drive.google.com/file/d/1yWu05_BS2RuHnItIpFvKVieiYOGWbzNj/view?usp=sharing) \| [log](https://drive.google.com/file/d/1xT6T7s8tNcuVgItWXyF2gI2cCU6T993O/view?usp=sharing) | | |
| | TSP | 34.14 | 51.35 | 58.44 | 76.24 | 68.47 | [config](anet_tsp_recall.py) | [model](https://drive.google.com/file/d/1sQQble7270Ja5Nx8kR5tApeJGGS42t6M/view?usp=sharing) \| [log](https://drive.google.com/file/d/12Q5vhXaH_YQoAxqtWFAzePAb5MfGGJ1Z/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 | 60.51 | 56.03 | 47.56 | 38.23 | 28.64 | 46.19 | [config](thumos_tsn.py) | [model](https://drive.google.com/file/d/14GANupLwRE4dAesyafeU4aGx-8c38ogd/view?usp=sharing) \| [log](https://drive.google.com/file/d/1DeDCLezP4rRZwq7ppPG5coA2ol0fSvkZ/view?usp=sharing) | | |
| | I3D | 64.99 | 60.70 | 54.54 | 44.11 | 34.16 | 51.70 | [config](thumos_i3d.py) | [model](https://drive.google.com/file/d/1aPl_jNOlbcr7Qc2DzNM10dRiMDfcSJ7V/view?usp=sharing) \| [log](https://drive.google.com/file/d/1TJGylck9Zhqew5IECeDjqwLNO8Zkq0QD/view?usp=sharing) | | |
| **HACS** with TCANet classifier. | |
| | Features | mAP@0.5 | mAP@0.75 | mAP@0.95 | ave. mAP | Config | Download | | |
| | :------: | :-----: | :------: | :------: | :------: | :----------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | |
| | SlowFast | 52.64 | 36.18 | 11.46 | 35.78 | [config](hacs_slowfast_192.py) | [model](https://drive.google.com/file/d/1gLOmyu-zLi7Gpph2czH9bgiJCKyeUvsm/view?usp=sharing) \| [log](https://drive.google.com/file/d/1fMm1sjNtbgWNIcFmdRSpnvKfiXOmsLQ2/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 BMN on ActivityNet dataset. | |
| ```shell | |
| torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py configs/bmn/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 BMN on ActivityNet dataset. | |
| ```shell | |
| torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py configs/bmn/anet_tsp.py --checkpoint exps/anet/bmn_tsp_128/gpu1_id0/checkpoint/epoch_9.pth | |
| ``` | |
| To test the recal performance: | |
| ```shell | |
| torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py configs/bmn/anet_tsp_recall.py --checkpoint exps/anet/bmn_tsp_128/gpu1_id0/checkpoint/epoch_9.pth | |
| ``` | |
| For more details, you can refer to the Test part in the [Usage](../../docs/en/usage.md). | |
| ## Citation | |
| ```latex | |
| @inproceedings{lin2019bmn, | |
| title={Bmn: Boundary-matching network for temporal action proposal generation}, | |
| author={Lin, Tianwei and Liu, Xiao and Li, Xin and Ding, Errui and Wen, Shilei}, | |
| booktitle={Proceedings of the IEEE/CVF international conference on computer vision}, | |
| pages={3889--3898}, | |
| year={2019} | |
| } | |
| ``` |