Strength in Diversity: Multi-Branch Representation Learning for Vehicle
  Re-Identification
Eurico Almeida Bruno Silva Jorge Batista
This paper presents an efficient and lightweight multi-branch deep
architecture to improve vehicle re-identification (V-ReID). While most V-ReID
work uses a combination of complex multi-branch architectures to extract robust
and diversified embeddings towards re-identification, we advocate that simple
and lightweight architectures can be designed to fulfill the Re-ID task without
compromising performance.
  We propose a combination of Grouped-convolution and Loss-Branch-Split
strategies to design a multi-branch architecture that improve feature diversity
and feature discriminability. We combine a ResNet50 global branch architecture
with a BotNet self-attention branch architecture, both designed within a
Loss-Branch-Split (LBS) strategy. We argue that specialized
loss-branch-splitting helps to improve re-identification tasks by generating
specialized re-identification features. A lightweight solution using grouped
convolution is also proposed to mimic the learning of loss-splitting into
multiple embeddings while significantly reducing the model size. In addition,
we designed an improved solution to leverage additional metadata, such as
camera ID and pose information, that uses 97% less parameters, further
improving re-identification performance.
  In comparison to state-of-the-art (SoTA) methods, our approach outperforms
competing solutions in Veri-776 by achieving 85.6% mAP and 97.7% CMC1 and
obtains competitive results in Veri-Wild with 88.1% mAP and 96.3% CMC1.
Overall, our work provides important insights into improving vehicle
re-identification and presents a strong basis for other retrieval tasks. Our
code is available at the
https://github.com/videturfortuna/vehicle_reid_itsc2023.