  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.
