Multi-task Recurrent Neural Network for Immediacy Prediction
Xiao Chu, Wanli Ouyang, Wei Yang, and Xiaogang Wang
Department of Electronic Engineering, The Chinese University of Hong Kong.
Xiao Chu, Wanli Ouyang, Wei Yang, and Xiaogang Wang
Department of Electronic Engineering, The Chinese University of Hong Kong.
In this paper, we propose the problem of predicting immediacy for interacting persons from still images. A complete immediacy set includes interactions, relative distance, body leaning direction and standing orientation. These measures are found to be related to the attitude, social relationship, social interaction, action, nationality, and religion of the communicators. A large-scale dataset with 10,000 images is constructed, in which all the immediacy cues and the human poses are annotated. We propose a rich set of immediacy representations that help to predict immediacy from imperfect 1-person and 2-person pose estimation results. A multi-task deep recurrent neural network is constructed to take the proposed rich immediacy representations as the input and learn the complex relationship among immediacy predictions through multiple steps of refinement. The effectiveness of the proposed approach is proved through extensive experiments on the large-scale dataset.