TimeGate：远程活动中的段的条件门控
TimeGate: Conditional Gating of Segments in Long-range Activities
摘要：
当识别长距离活动时，探索整个视频是详尽的，并且计算成本很高，因为它可能长达几分钟。因此，仅对视频的显著部分进行采样是非常重要的。我们提出了TimeGate以及一个新型的条件门控模块，用于从长期活动中采样最具代表性的片段。TimeGate有两个新颖之处，解决了以前采样方法的缺点，如SCSampler。首先，它实现了对段的可微分采样。因此，TimeGate可以与现代CNN相匹配，并作为一个单一的统一模型进行端到端的训练。其次，采样以片段及其上下文为条件。因此，TimeGate更适合长距离活动，其中片段的重要性在很大程度上取决于视频上下文。TimeGate减少了现有CNN在长距离活动的三个基准上的计算：Charades，Breakfast和MultiThumos。特别是，TimeGate将I3D的计算量减少了50%，同时保持了分类精度。
英文摘要：
When recognizing a long-range activity, exploring the entire video is exhaustive and computationally expensive, as it can span up to a few minutes. Thus, it is of great importance to sample only the salient parts of the video. We propose TimeGate, along with a novel conditional gating module, for sampling the most representative segments from the long-range activity. TimeGate has two novelties that address the shortcomings of previous sampling methods, as SCSampler. First, it enables a differentiable sampling of segments. Thus, TimeGate can be fitted with modern CNNs and trained end-to-end as a single and unified model.Second, the sampling is conditioned on both the segments and their context. Consequently, TimeGate is better suited for long-range activities, where the importance of a segment heavily depends on the video context.TimeGate reduces the computation of existing CNNs on three benchmarks for long-range activities: Charades, Breakfast and MultiThumos. In particular, TimeGate reduces the computation of I3D by 50% while maintaining the classification accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
