Title: Learning from student's mistakes: Improving mean teacher for end-to-end semi-supervised video action detection. Abstract: In this work, we focus on semi-supervised learning for video action detection. We present Enhanced Mean Teacher, a simple end-to-end student-teacher based framework which rely on pseudo-labels to learn from unlabeled samples. Limited amount of data make the teacher prone to unreliable boundaries while detecting the spatio-temporal actions. We propose a novel auxiliary module, which learns from student’s mistakes on labeled samples and improve the spatio-temporal pseudo-labels generated by the teacher on unlabeled set. The proposed framework utilize spatial and temporal augmentations to generate pseudo-labels where both classification as well as spatio-temporal consistencies are used to train the model. We evaluate our approach on two action detection benchmark datasets, UCF101-24, and JHMDB-21. On UCF101-24, our approach outperforms the supervised baseline by an approximate margin of 19% on f-mAP@0.5 and 25% on v-mAP@0.5. Using merely 10-15% of the annotations in UCF-101-24, the proposed approach provides a competitive performance compared to the supervised baseline trained on 100% annotations. We also evaluate the effectiveness of Enhanced Mean Teacher for video object segmentation demonstrating its generalization capability to other tasks in video domain.