Spatial and Semantic Relations for Pedestrian Attribute Recognition This paper addresses the problem of pedestrian attribute recognition. Previous works typically treat the different attributes independently with each other, without considering possible dependencies between them, or just take semantic relations or spatial relations into consideration. In our work, we propose an endtoend learning framework combining with a subnet for multitask classification to take both spatial and semantic relations into account, which proves to be more accurate and effective. Our work can not only deal with binary attributes, but also multiclass attributes in a single network, overcoming the drawbacks in many methods that can only recognize the binary attributes in jointtraining. Experiments have been carried out on several benchmarks and positively demonstrated the superiority of our method.

Joint Learning of Dictionary and Convolutional Network for Pedestrian Attribute Recognition Pedestrian attribute recognition is to predict the presence of a set of attributes from a given image, and it plays an important role in video surveillance applications. Most existing works model the task as a multilabel classification problem. Although effective, they ignore the existence of correlations among attributes. In this work, to learn multiple attributes jointly, the attributes are modeled as a subspace and a dictionary is introduced to represent the subspace. Furthermore, to extract the convolutional features which are more suitable for attribute prediction, the dictionary is modeled as a network layer which is learned jointly with the convolutional network. Finally, a novel learning algorithm is proposed to optimize the dictionary and the convolutional network corporately. Extensive experimental analyses and evaluations on two largest pedestrian attribute benchmarks PETA and PA100K demonstrate that the proposed method achieves stateoftheart performance.