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 …

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) …

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 …

Recently, contrastive learning attracts increasing interests in neural text generation as a new
solution to alleviate the exposure bias problem. It introduces a sequence-level training …

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 …

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 …

In zero-shot image retrieval (ZSIR) task, embedding learning becomes more attractive,
however, many methods follow the traditional metric learning idea and omit the problems …

Deep metric learning has been effectively used to learn distance metrics for different visual
tasks like image retrieval, clustering, etc. In order to aid the training process, existing …

Zero-shot image retrieval involves retrieving images of unseen classes using a query image
of the same class. To determine whether a given image is of the same class as the query …

Image clustering is a crucial but challenging task in machine learning and computer vision.
Its performance highly depends on the quality of image feature representations. Recently …