# BMN [Bmn: Boundary-matching network for temporal action proposal generation](https://openaccess.thecvf.com/content_ICCV_2019/html/Lin_BMN_Boundary-Matching_Network_for_Temporal_Action_Proposal_Generation_ICCV_2019_paper.html) ## 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 feature | config | feature | gpus | AR@100 | AUC | AP@0.5 | AP@0.75 | AP@0.95 | mAP | gpu_mem(M) | iter time(s) | ckpt | log | json | | :-----------------------------------------------------------------------------------------------------------: | :------------: | :--: | :----: | :---: | :----: | :-----: | :-----: | :---: | :--------: | ------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------: | -------------------------------------------------------------------------------------------------------------------------------------------------- | | [bmn_400x100_9e_2x8_activitynet_feature](/configs/localization/bmn/bmn_400x100_2x8_9e_activitynet_feature.py) | cuhk_mean_100 | 2 | 75.28 | 67.22 | 42.47 | 31.31 | 9.92 | 30.34 | 5420 | 3.27 | [ckpt](https://download.openmmlab.com/mmaction/localization/bmn/bmn_400x100_9e_activitynet_feature/bmn_400x100_9e_activitynet_feature_20200619-42a3b111.pth) | [log](https://download.openmmlab.com/mmaction/localization/bmn/bmn_400x100_9e_activitynet_feature/bmn_400x100_9e_activitynet_feature.log) | [json](https://download.openmmlab.com/mmaction/localization/bmn/bmn_400x100_9e_activitynet_feature/bmn_400x100_9e_activitynet_feature.log.json) | | | mmaction_video | 2 | 75.43 | 67.22 | 42.62 | 31.56 | 10.86 | 30.77 | 5420 | 3.27 | [ckpt](https://download.openmmlab.com/mmaction/localization/bmn/bmn_400x100_2x8_9e_mmaction_video/bmn_400x100_2x8_9e_mmaction_video_20200809-c9fd14d2.pth) | [log](https://download.openmmlab.com/mmaction/localization/bmn/bmn_400x100_2x8_9e_mmaction_video/bmn_400x100_2x8_9e_mmaction_video_20200809.log) | [json](https://download.openmmlab.com/mmaction/localization/bmn/bmn_400x100_2x8_9e_mmaction_video/bmn_400x100_2x8_9e_mmaction_video_20200809.json) | | | mmaction_clip | 2 | 75.35 | 67.38 | 43.08 | 32.19 | 10.73 | 31.15 | 5420 | 3.27 | [ckpt](https://download.openmmlab.com/mmaction/localization/bmn/bmn_400x100_2x8_9e_mmaction_clip/bmn_400x100_2x8_9e_mmaction_clip_20200809-10d803ce.pth) | [log](https://download.openmmlab.com/mmaction/localization/bmn/bmn_400x100_2x8_9e_mmaction_clip/bmn_400x100_2x8_9e_mmaction_clip_20200809.log) | [json](https://download.openmmlab.com/mmaction/localization/bmn/bmn_400x100_2x8_9e_mmaction_clip/bmn_400x100_2x8_9e_mmaction_clip_20200809.json) | | [BMN-official](https://github.com/JJBOY/BMN-Boundary-Matching-Network) (for reference)\* | cuhk_mean_100 | - | 75.27 | 67.49 | 42.22 | 30.98 | 9.22 | 30.00 | - | - | - | - | - | :::{note} 1. The **gpus** indicates the number of gpu we used to get the checkpoint. According to the [Linear Scaling Rule](https://arxiv.org/abs/1706.02677), you may set the learning rate proportional to the batch size if you use different GPUs or videos per GPU, e.g., lr=0.01 for 4 GPUs x 2 video/gpu and lr=0.08 for 16 GPUs x 4 video/gpu. 2. For feature column, cuhk_mean_100 denotes the widely used cuhk activitynet feature extracted by [anet2016-cuhk](https://github.com/yjxiong/anet2016-cuhk), mmaction_video and mmaction_clip denote feature extracted by mmaction, with video-level activitynet finetuned model or clip-level activitynet finetuned model respectively. 3. We evaluate the action detection performance of BMN, using [anet_cuhk_2017](https://download.openmmlab.com/mmaction/localization/cuhk_anet17_pred.json) submission for ActivityNet2017 Untrimmed Video Classification Track to assign label for each action proposal. ::: \*We train BMN with the [official repo](https://github.com/JJBOY/BMN-Boundary-Matching-Network), evaluate its proposal generation and action detection performance with [anet_cuhk_2017](https://download.openmmlab.com/mmaction/localization/cuhk_anet17_pred.json) for label assigning. For more details on data preparation, you can refer to ActivityNet feature in [Data Preparation](/docs/en/data_preparation.md). ## Train You can use the following command to train a model. ```shell python tools/train.py ${CONFIG_FILE} [optional arguments] ``` Example: train BMN model on ActivityNet features dataset. ```shell python tools/train.py configs/localization/bmn/bmn_400x100_2x8_9e_activitynet_feature.py ``` For more details and optional arguments infos, you can refer to **Training setting** part in [getting_started](/docs/en/getting_started.md#training-setting) . ## Test You can use the following command to test a model. ```shell python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments] ``` Example: test BMN on ActivityNet feature dataset. ```shell # Note: If evaluated, then please make sure the annotation file for test data contains groundtruth. python tools/test.py configs/localization/bmn/bmn_400x100_2x8_9e_activitynet_feature.py checkpoints/SOME_CHECKPOINT.pth --eval AR@AN --out results.json ``` You can also test the action detection performance of the model, with [anet_cuhk_2017](https://download.openmmlab.com/mmaction/localization/cuhk_anet17_pred.json) prediction file and generated proposal file (`results.json` in last command). ```shell python tools/analysis/report_map.py --proposal path/to/proposal_file ``` :::{note} 1. (Optional) You can use the following command to generate a formatted proposal file, which will be fed into the action classifier (Currently supports SSN and P-GCN, not including TSN, I3D etc.) to get the classification result of proposals. ```shell python tools/data/activitynet/convert_proposal_format.py ``` ::: For more details and optional arguments infos, you can refer to **Test a dataset** part in [getting_started](/docs/en/getting_started.md#test-a-dataset) . ## Citation ```BibTeX @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 International Conference on Computer Vision}, pages={3889--3898}, year={2019} } ``` ```BibTeX @article{zhao2017cuhk, title={Cuhk \& ethz \& siat submission to activitynet challenge 2017}, author={Zhao, Y and Zhang, B and Wu, Z and Yang, S and Zhou, L and Yan, S and Wang, L and Xiong, Y and Lin, D and Qiao, Y and others}, journal={arXiv preprint arXiv:1710.08011}, volume={8}, year={2017} } ```