Person attribute or pose information has improved person re-identification performance, however, inaccurate pose or attribute module will damage the final identification performance. Based on this, we propose a multi-scale relation aware network (MSRA) for person re-identification. Specifically, we design an attribute relation mining module to construct an attribute map through constraint loss to learn the correlation between different attributes. Besides, we construct a multi-level Pose Pyramid based on the physical structure of the human body, so as to model the internal relationship between pose points. Finally, we designed a cross-scale graph convolution to infer the cooperative structural relation between different layers of components and fused it with the attribute relation module to reinforce the feature. Many experiments on three large-scale datasets verify the effectiveness and state-of-the-art performance of the proposed method.

Image captioning is an interesting and challenging task. The previously established image captioning approach is based mainly on the encoder-decoder architecture, but it suffers from problems such as inaccurate captioning information, and the generated captioning sentences are not sufficiently rich. This paper proposes a novel image captioning model that is based on a self-attention network and a scene graph relationship network. First, an improved self-attention network is added to the extraction of visual features to evaluate the effectiveness of image global information for image generation. Then, we design a visual intensity parameter to coordinate the strategies of visual features and language model for word generation. Finally, a graph convolutional network is designed to extract the relationships from the scene information to render the generated caption more exciting and to increase the accuracy of the fine-grained captioning. We demonstrated the satisfactory performance of the model on the MS-COCO and Flickr 30K datasets. The experimental results demonstrate that the proposed model realizes state-of-the-art performance.

The challenge of visual tracking mainly comes from intrinsic appearance variations of the target and extrinsic environment changes around the target in a long duration, so the tracker that can simultaneously tolerate these variabilities is largely expected. In this study, the authors propose a new tracking approach based on discriminative stable regions (DSRs). The DSRs are obtained based on the criterion of maximal local entropy and spatial discrimination, which enables the tracker to handle well distractors and appearance variations. The collaborative tracking incorporated hierarchical clustering can tolerate motion noise and occlusions. In addition, as an efficient tool, the principle component analysis is used to discover the potential affine relation between DSR and the target, which timely adapts to the shape deformation of the target. Extensive experiments show that the proposed method achieves superior performance in many challenging target tracking tasks.