Deep metric learning papers from the past four years have consistently claimed great
advances in accuracy, often more than doubling the performance of decade-old methods. In …

Attention has become more attractive in person re-identification (ReID) as it is capable of
biasing the allocation of available resources towards the most informative parts of an input …

Transformers have shown outstanding results for natural language understanding and, more
recently, for image classification. We here extend this work and propose a transformer …

Employing attention mechanisms to model both global and local features as a final
pedestrian representation has become a trend for person re-identification (Re-ID) …

In recent years a vast amount of visual content has been generated and shared from various
fields, such as social media platforms, medical images, and robotics. This abundance of …

Person re-identification (re-ID) is of great importance to video surveillance systems by
estimating the similarity between a pair of cross-camera person shorts. Current methods for …

In crowd counting, due to the problem of laborious labelling, it is perceived intractability of
collecting a new large-scale dataset which has plentiful images with large diversity in …

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) aims at searching corresponding
natural images with the given free-hand sketches, under the more realistic and challenging …

Deep metric learning, which learns discriminative features to process image clustering and
retrieval tasks, has attracted extensive attention in recent years. A number of deep metric …

Spatial redundancy widely exists in visual recognition tasks, ie, discriminative features in an
image or video frame usually correspond to only a subset of pixels, while the remaining …