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In the context of label-efficient learning on video data, the distillationmethod and the structural design of the teacher-student architecture have asignificant impact on knowledge distillation. However, the relationship betweenthese factors has been overlooked in previous research. To address this gap, wepropose a new weakly supervised learning framework for knowledge distillationin video classification that is designed to improve the efficiency and accuracyof the student model. Our approach leverages the concept of substage-basedlearning to distill knowledge based on the combination of student substages andthe correlation of corresponding substages. We also employ the progressivecascade training method to address the accuracy loss caused by the largecapacity gap between the teacher and the student. Additionally, we propose apseudo-label optimization strategy to improve the initial data label. Tooptimize the loss functions of different distillation substages during thetraining process, we introduce a new loss method based on feature distribution.We conduct extensive experiments on both real and simulated data sets,demonstrating that our proposed approach outperforms existing distillationmethods in terms of knowledge distillation for video classification tasks. Ourproposed substage-based distillation approach has the potential to informfuture research on label-efficient learning for video data.

The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised Framework