In this work, we focus on label efficient learning for video action
detection. We develop a novel semi-supervised active learning approach which
utilizes both labeled as well as unlabeled data along with informative sample
selection for action detection. Video action detection requires spatio-temporal
localization along with classification, which poses several challenges for both
active learning informative sample selection as well as semi-supervised
learning pseudo label generation. First, we propose NoiseAug, a simple
augmentation strategy which effectively selects informative samples for video
action detection. Next, we propose fft-attention, a novel technique based on
high-pass filtering which enables effective utilization of pseudo label for SSL
in video action detection by emphasizing on relevant activity region within a
video. We evaluate the proposed approach on three different benchmark datasets,
UCF-101-24, JHMDB-21, and Youtube-VOS. First, we demonstrate its effectiveness
on video action detection where the proposed approach outperforms prior works
in semi-supervised and weakly-supervised learning along with several baseline
approaches in both UCF101-24 and JHMDB-21. Next, we also show its effectiveness
on Youtube-VOS for video object segmentation demonstrating its generalization
capability for other dense prediction tasks in videos.

本文主要研究标签高效学习、视频动作检测、半监督主动学习、信息样本选择和时空定位等主题。提出了一个半监督主动学习方法，利用标记和未标记数据以及信息样本选择来进行动作检测。通过提出的噪声增强和基于高通滤波的关注机制（fft-attention）等技术，提高了半监督学习在视频动作检测中的准确性和泛化能力。通过在 UCF-101-24、JHMDB-21 和 Youtube-VOS 等数据集上的实验证明了该方法的有效性。