Hybrid Vision Transformer for Domain Adaptable Person Re-identification.

Person re-identification refers to finding person images taken from different cameras at different times. Supervised re-id methods rely on labeled dataset, which is usually not available in real word situations. Therefore, a procedure must be devised to adapt unseen domains in an unsupervised manner. In this work we have proposed a domain adaptation methodology by using hybrid Vision Transformers and incorporating Cluster loss along with the widely used Triplet loss. Our proposed methodology has shown to improve results of exiting unsupervised domain adaptation methods for person re-id.