BMN: Boundary-Matching Network for Temporal Action Proposal Generation

边界匹配网络[时序动作提名]

Figure 2. Illustration of BM confidence map. Proposals in the same row have the same temporal duration, and proposals in the same column have the same starting time. The ending boundaries of proposals at right-bottom corner exceed the range of video, thus these proposals are not considered during training and inference.

图2。BM置信图图解。同一行中的提案具有相同的时间持续时间，同一列中的提案具有相同的开始时间。由于右下角建议的结束边界超出了视频的范围，所以在训练和推理时不考虑这些建议。

Temporal Action Proposal Generation

时序动作提名

As aforementioned, the goal of temporal action detection task is to detect action instances in untrimmed videos with temporal boundaries and action categories, which can be divided into temporal proposal generation and action classification stages.

如前所述，时间动作检测任务的目标是检测未修剪视频中具有时间边界和动作类别的动作实例，分为时间提议生成和动作分类两个阶段。

These two stages are taken apart in most detection methods [23, 25, 35], and are taken together as single model in some methods [18, 2]. For proposal generation task, most previous works [3, 4, 8, 12, 23] adopt top-down fashion to generate proposals with pre-defined duration and interval, where the main drawback is the lack of boundary precision and duration flexibility. There are also some methods [35, 17] adopt bottom-up fashion. TAG [35] generates proposals using temporal watershed algorithm, but lack confidence scores for retrieving. Recently, BSN [17] generates proposals via locally locating temporal boundaries and globally evaluating confidence scores, and achieves significant performance promotion over previous proposal generation methods. In this work, we propose the Boundary-Matching mechanism for proposal confidence evaluation, which can largely simplify the pipeline of BSN and bring significant promotion in both efficiency and effectiveness.