Temporal action localization aims to localize starting and ending time with
action category. Limited by GPU memory, mainstream methods pre-extract features
for each video. Therefore, feature quality determines the upper bound of
detection performance. In this technical report, we explored classic
convolution-based backbones and the recent surge of transformer-based
backbones. We found that the transformer-based methods can achieve better
classification performance than convolution-based, but they cannot generate
accuracy action proposals. In addition, extracting features with larger frame
resolution to reduce the loss of spatial information can also effectively
improve the performance of temporal action localization. Finally, we achieve
42.42% in terms of mAP on validation set with a single SlowFast feature by a
simple combination: BMN+TCANet, which is 1.87% higher than the result of 2020's
multi-model ensemble. Finally, we achieve Rank 1st on the CVPR2021 HACS
supervised Temporal Action Localization Challenge.

本文研究了时域动作定位的方法，发现基于变压器的方法可以实现更好的分类性能，但不能生成准确的动作提案，最后通过以较高的帧分辨率提取特征来提高时域动作定位的性能，最终在 CVPR2021HACS 挑战中取得了第一名。