Multi-Task Learning With Low Rank Attribute Embedding for Person Re-Identification

Chi Su, Fan Yang, Shiliang Zhang, Qi Tian, Larry S. Davis, Wen Gao; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3739-3747

Abstract


We propose a novel Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) framework for person re-identification. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information to improve re-identification accuracy. Both low level features and semantic/data-driven attributes are utilized. Since attributes are generally correlated, we introduce a low rank attribute embedding into the MTL formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered to better describe people. The learning objective function consists of a quadratic loss regarding class labels and an attribute embedding error, which is solved by an alternating optimization procedure. Experiments on three person re-identification datasets have demonstrated that MTL-LORAE outperforms existing approaches by a large margin and produces state-of-the-art results.

Related Material


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[bibtex]
@InProceedings{Su_2015_ICCV,
author = {Su, Chi and Yang, Fan and Zhang, Shiliang and Tian, Qi and Davis, Larry S. and Gao, Wen},
title = {Multi-Task Learning With Low Rank Attribute Embedding for Person Re-Identification},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}