  Object Re-Identification (Re-ID) aims to identify and retrieve specific
objects from images captured at different places and times. Recently, object
Re-ID has achieved great success with the advances of Vision Transformers
(ViT). However, the effects of the global-local relation have not been fully
explored in Transformers for object Re-ID. In this work, we first explore the
influence of global and local features of ViT and then further propose a novel
Global-Local Transformer (GLTrans) for high-performance object Re-ID. We find
that the features from last few layers of ViT already have a strong
representational ability, and the global and local information can mutually
enhance each other. Based on this fact, we propose a Global Aggregation Encoder
(GAE) to utilize the class tokens of the last few Transformer layers and learn
comprehensive global features effectively. Meanwhile, we propose the Local
Multi-layer Fusion (LMF) which leverages both the global cues from GAE and
multi-layer patch tokens to explore the discriminative local representations.
Extensive experiments demonstrate that our proposed method achieves superior
performance on four object Re-ID benchmarks.
