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BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation

15 Sep 2020arXiv:2009.07641archive 2025-07-28

Haisheng Su, Weihao Gan, Wei Wu, Yu Qiao, Junjie Yan

Generating human action proposals in untrimmed videos is an important yet challenging task with wide applications. Current methods often suffer from the noisy boundary locations and the inferior quality of confidence scores used for proposal retrieving. In this paper, we present BSN++, a new framework which exploits complementary boundary regressor and relation modeling for temporal proposal generation. First, we propose a novel boundary regressor based on the complementary characteristics of both starting and ending boundary classifiers. Specifically, we utilize the U-shaped architecture with nested skip connections to capture rich contexts and introduce bi-directional boundary matching mechanism to improve boundary precision. Second, to account for the proposal-proposal relations ignored in previous methods, we devise a proposal relation block to which includes two self-attention modules from the aspects of position and channel. Furthermore, we find that there inevitably exists data imbalanced problems in the positive/negative proposals and temporal durations, which harm the model performance on tail distributions. To relieve this issue, we introduce the scale-balanced re-sampling strategy. Extensive experiments are conducted on two popular benchmarks: ActivityNet-1.3 and THUMOS14, which demonstrate that BSN++ achieves the state-of-the-art performance. Not surprisingly, the proposed BSN++ ranked 1st place in the CVPR19 - ActivityNet challenge leaderboard on temporal action localization task.

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bi_loss xxcheng0708/BSNPlusPlus-boundary-sensitive-network/loss_function.py community (archive-listed) unverified MIT (permissive) · 2a1b5cbf26a5e2ba · report
cbg_loss_func xxcheng0708/BSNPlusPlus-boundary-sensitive-network/loss_function.py community (archive-listed) unverified MIT (permissive) · d57bf724ccd7a70d · report
conv_block xxcheng0708/BSNPlusPlus-boundary-sensitive-network/models.py community (archive-listed) unverified MIT (permissive) · 83a6fac7349fd1f0 · report
getDatasetDict xxcheng0708/BSNPlusPlus-boundary-sensitive-network/post_processing.py community (archive-listed) unverified MIT (permissive) · 13551e06a372ae9c · report
get_mask xxcheng0708/BSNPlusPlus-boundary-sensitive-network/loss_function.py community (archive-listed) unverified MIT (permissive) · 1cd5e9eec5a11010 · report
ioa_with_anchors xxcheng0708/BSNPlusPlus-boundary-sensitive-network/utils.py community (archive-listed) unverified MIT (permissive) · 49b3db69edc148a7 · report
iou_with_anchors xxcheng0708/BSNPlusPlus-boundary-sensitive-network/utils.py community (archive-listed) unverified MIT (permissive) · 1ac2acb4c84fccca · report
load_json xxcheng0708/BSNPlusPlus-boundary-sensitive-network/dataset.py community (archive-listed) unverified MIT (permissive) · 1e0bb35d27bdc115 · report
load_json xxcheng0708/BSNPlusPlus-boundary-sensitive-network/post_processing.py community (archive-listed) unverified MIT (permissive) · e924a491a8d3290b · report

Tasks

Action LocalizationTemporal Action LocalizationTemporal Action Proposal Generation

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Temporal Action Localization ActivityNet-1.3 BSN++ mAP 34.88 #23 of 33 Archive leaderboard report
Temporal Action Localization ActivityNet-1.3 BSN++ mAP IOU@0.5 51.27 #23 of 33 Archive leaderboard report
Temporal Action Localization ActivityNet-1.3 BSN++ mAP IOU@0.75 35.70 #23 of 33 Archive leaderboard report
Temporal Action Localization ActivityNet-1.3 BSN++ mAP IOU@0.95 8.33 #23 of 33 Archive leaderboard report
Temporal Action Proposal Generation ActivityNet-1.3 BSN++ AR@100 76.52 #6 of 11 Archive leaderboard report
Temporal Action Proposal Generation ActivityNet-1.3 BSN++ AUC (val) 68.26 #6 of 11 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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