Graph Distillation for Action Detection with Privileged Modalities

Zelun Luo, Jun-Ting Hsieh, Lu Jiang, Juan Carlos Niebles, Li Fei-Fei; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 166-183


We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available. Common methods in transfer learning do not take advantage of the extra modalities potentially available in the source domain. On the other hand, previous work on multimodal learning only focuses on a single domain or task and does not handle the modality discrepancy between training and testing. In this work, we propose a method termed graph distillation that incorporates rich privileged information from a large-scale multimodal dataset in the source domain, and improves the learning in the target domain where training data and modalities are scarce. We evaluate our approach on action classification and detection tasks in multimodal videos, and show that our model outperforms the state-of-the-art by a large margin on the NTU RGB+D and PKU-MMD benchmarks. The code is released at

Related Material

[pdf] [arXiv]
author = {Luo, Zelun and Hsieh, Jun-Ting and Jiang, Lu and Niebles, Juan Carlos and Fei-Fei, Li},
title = {Graph Distillation for Action Detection with Privileged Modalities},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}