# Knowledge Distillation for Human Action Anticipation

arXiv: https://arxiv.org/abs/1904.04868 · HTML: https://mlsanity.com/paper/1904.04868/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Fostering Video Reasoning via Next-Event Prediction](https://mlsanity.com/paper/2505.22457/) (2025)
- [Synchronization is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs](https://mlsanity.com/paper/2312.02638/) (2023)
- [A Survey on Deep Learning Techniques for Action Anticipation](https://mlsanity.com/paper/2309.17257/) (2023)
- [Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction](https://mlsanity.com/paper/2308.04589/) (2023)
- [RiskProp: Collision-Anchored Self-Supervised Risk Propagation for Early Accident Anticipation](https://mlsanity.com/paper/2603.27165/) (2026)
- [EgoSpeed-Net: Forecasting Speed-Control in Driver Behavior from Egocentric Video Data](https://mlsanity.com/paper/2209.13459/) (2022)
- [Back-Tracking from Clarity: Self-Learning to See Text from Afar](https://mlsanity.com/paper/2609.32477/) (2026)

## Abstract (arXiv)

We consider the task of training a neural network to anticipate human actions in video. This task is challenging given the complexity of video data, the stochastic nature of the future, and the limited amount of annotated training data. In this paper, we propose a novel knowledge distillation framework that uses an action recognition network to supervise the training of an action anticipation network, guiding the latter to attend to the relevant information needed for correctly anticipating the future actions. This framework is possible thanks to a novel loss function to account for positional shifts of semantic concepts in a dynamic video. The knowledge distillation framework is a form of self-supervised learning, and it takes advantage of unlabeled data. Experimental results on JHMDB and EPIC-KITCHENS dataset show the effectiveness of our approach.