  Vehicle re-identification (Re-ID) distinguishes between the same vehicle and
other vehicles in images. It is challenging due to significant intra-instance
differences between identical vehicles from different views and subtle
inter-instance differences of similar vehicles. Researchers have tried to
address this problem by extracting features robust to variations of viewpoints
and environments. More recently, they tried to improve performance by using
additional metadata such as key points, orientation, and temporal information.
Although these attempts have been relatively successful, they all require
expensive annotations. Therefore, this paper proposes a novel deep neural
network called a multi-attention-based soft partition (MUSP) network to solve
this problem. This network does not use metadata and only uses multiple soft
attentions to identify a specific vehicle area. This function was performed by
metadata in previous studies. Experiments verified that MUSP achieved
state-of-the-art (SOTA) performance for the VehicleID dataset without any
additional annotations and was comparable to VeRi-776 and VERI-Wild.
