Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-
overlapping cameras. With the advancement of deep neural networks and increasing …

Existing fully-supervised person re-identification (ReID) methods usually suffer from poor
generalization capability caused by domain gaps. The key to solving this problem lies in …

For person re-identification (re-id), attention mechanisms have become attractive as they
aim at strengthening discriminative features and suppressing irrelevant ones, which …

The combination of global and partial features has been an essential solution to improve
discriminative performances in person re-identification (Re-ID) tasks. Previous part-based …

Abstract This paper proposes Attribute Attention Network (AANet), a new architecture that
integrates person attributes and attribute attention maps into a classification framework to …

We propose a novel deep network called Mancs that solves the person re-identification
problem from the following aspects: fully utilizing the attention mechanism for the person …

We propose a densely semantically aligned person re-identification (re-ID) framework. It
fundamentally addresses the body misalignment problem caused by pose/viewpoint …

Since the person re-identification task often suffers from the problem of pose changes and
occlusions, some attentive local features are often suppressed when training CNNs. In this …

The fundamental difficulty in person re-identification (ReID) lies in learning the
correspondence among individual cameras. It strongly demands costly inter-camera …

We address the problem of cross-modal fine-grained action retrieval between text and video.
Cross-modal retrieval is commonly achieved through learning a shared embedding space …