Section: New Results
Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification
Participants : Hao Chen, Benoit Lagadec, François Brémond.
The smart city vision raises the prospect that cities will become more intelligent in various fields, such as more sustainable environment and a better quality of life for residents. As a key component of smart cities, intelligent transportation system highlights the importance of vehicle re-identification (Re-ID). However, as compared to the rapid progress on person Re-ID, vehicle Re-ID advances at a relatively slow pace. Some previous state-of-the-art approaches strongly rely on extra annotation, like attributes (vehicle color and type) and key-points (wheels and lamps). Recent work on person Re-ID shows that extracting more local features can achieve a better performance without considering extra annotation. In this work, we propose an end-to-end trainable two-branch Partition and Reunion Network (PRN) for the challenging vehicle Re-ID task. Utilizing only identity labels, our proposed method outperforms existing state-of-the-art methods on four vehicle Re-ID benchmark datasets, including VeRi-776, VehicleID, VRIC and CityFlow-ReID by a large margin. The general architecture of our proposed method is represented in the Figure 8.
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Learning Discriminative and Generalizable Representations by Spatial-Channel Partition for Person Re-Identification
In Person Re-Identification (Re-ID) task, combining local and global features is a common strategy to overcome missing key parts and misalignment on models based only on global features. Using this combination, neural networks yield impressive performance in Re-ID task. Previous part-based models mainly focus on spatial partition strategies. Recently, operations on channel information, such as Group Normalization and Channel Attention, have brought significant progress to various visual tasks. However, channel partition has not drawn much attention in Person Re-ID. We conduct a study to exploit the potential of channel partition in Re-ID task [32]. Based on this study, we propose an end-to-end Spatial and Channel partition Representation network (SCR) in order to better exploit both spatial and channel information. Experiments conducted on three mainstream image-based evaluation protocols including Market-1501, DukeMTMC-ReID and CUHK03 and one video-based evaluation protocol MARS validate the performance of our model, which outperforms previous state-of-the-art in both single and cross domain Re-ID tasks. The general architecture of our proposed method is represented in the Figure 9.
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