Lightweight Multi-Branch Network For Person Re-Identification

@article{Herzog2021LightweightMN,
  title={Lightweight Multi-Branch Network For Person Re-Identification},
  author={Fabian Herzog and Xunbo Ji and Torben Teepe and Stefan H{\"o}rmann and Johannes Gilg and Gerhard Rigoll},
  journal={2021 IEEE International Conference on Image Processing (ICIP)},
  year={2021},
  pages={1129-1133}
}
Person Re-Identification aims to retrieve person identities from images captured by multiple cameras or the same cameras in different time instances and locations. Because of its importance in many vision applications from surveillance to human-machine interaction, person re-identification methods need to be reliable and fast. While more and more deep architectures are proposed for increasing performance, those methods also increase overall model complexity. This paper proposes a lightweight… 

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References

SHOWING 1-10 OF 33 REFERENCES
Deep Learning for Person Re-Identification: A Survey and Outlook
TLDR
A powerful AGW baseline is designed, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks, and a new evaluation metric (mINP) is introduced, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re- ID system for real applications.
Person Re-identification: Past, Present and Future
TLDR
The history of person re-identification and its relationship with image classification and instance retrieval is introduced and two new re-ID tasks which are much closer to real-world applications are described and discussed.
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
TLDR
A novel coarse-to-fine pyramid model is proposed to relax the need of bounding boxes and learn discriminative identity representation, which not only incorporates local and global information, but also integrates the gradual cues between them.
Batch DropBlock Network for Person Re-Identification and Beyond
TLDR
The Batch DropBlock (BDB) Network is a two branch network composed of a conventional ResNet-50 as the global branch and a feature dropping branch that achieves state-of-the-art on person re-identification and it is also applicable to general metric learning tasks.
Learning Diverse Features with Part-Level Resolution for Person Re-Identification
TLDR
Empirical evidence demonstrates that the proposed PLR-OSNet achieves state-of-the-art performance on popular person Re-ID datasets, including Market1501, DukeMTMC-reID and CUHK03, despite its small model size.
Omni-Scale Feature Learning for Person Re-Identification
TLDR
A novel deep ReID CNN is designed, termed Omni-Scale Network (OSNet), for omni-scale feature learning by designing a residual block composed of multiple convolutional feature streams, each detecting features at a certain scale.
Bag of Tricks and a Strong Baseline for Deep Person Re-Identification
TLDR
A simple and efficient baseline for person re-identification with deep neural networks by combining effective training tricks together, which achieves 94.5% rank-1 and 85.9% mAP on Market1501 with only using global features.
Learning Discriminative and Generalizable Representations by Spatial-Channel Partition for Person Re-Identification
TLDR
This study proposes an end-to-end Spatial and Channel partition Representation network (SCR) in order to better exploit both spatial and channel information in Person Re-Identification task.
Scalable Person Re-identification: A Benchmark
TLDR
A minor contribution, inspired by recent advances in large-scale image search, an unsupervised Bag-of-Words descriptor is proposed that yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large- scale 500k dataset.
Compact Network Training for Person ReID
TLDR
This study focuses on a small-sized randomly initialized model that enables us to easily introduce architecture and training modifications suitable for person ReID and shows the robustness of the network by outperforming the SotA on both Market1501 and DukeMTMC.
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