Multi-Task Learning with Low Rank Attribute Embedding for Person Re-identification

Chi Su1*, Fan Yang2*, Shiliang Zhang1, Qi Tian3, Larry S. Davis2 and Wen Gao1

1Peking University, China
2University of Maryland College Park, United States
3University of Texas at San Antonio, United States


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 fea- tures 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 al- ternating optimization procedure. Experiments on four per- son re-identification datasets have demonstrated that MTL- LORAE outperforms existing approaches by a large margin and produces promising results.

Experimental Results

To be uploaded.


To be uploaded.