Person Re-identification by Deep Learning Multi-scale Representations

By Yanbei Chen, Xiatian Zhu, Shaogang Gong. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017

419 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/38cd49b7fc37a2e85822490b4517122ff7936960

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Yi Yang
2. Liang Zheng
3. Qi Tian
4. Wanli Ouyang
5. Shaogang Gong
6. Tao A. Xiang
7. Xiatian Zhu
8. Shengjin Wang

In Computer Vision and Pattern Recognition

1. Yi Yang
2. Xiaogang Wang
3. Luc Van Gool
4. Liang Zheng
5. Dacheng Tao
6. Tao A. Xiang
7. Qi Tian
8. Wanli Ouyang

Citations in the corpus

• Multi-level Factorisation Net for Person Re-identification (2018, methodology): “(3) The closest competitor, DPFL uses multiple network branches to model image input scaled to different resolutions, which is orthogonal to our approach and can be easily combined to improve our performance further.”
• Human Semantic Parsing for Person Re-identification (2018, neutral): “[10] 73.”
• Pose-Invariant Embedding for Deep Person Re-Identification (2017, background): “, DPFL [58], our performance is higher by +2.”
• Pose-Guided Feature Alignment for Occluded Person Re-Identification (2019, background): “We [42] 81.0 63.4 - - Pedestrian[47] 82.0 63.0 - - DSR [11] 83.5 64.2 - - MultiLoss [35] 83.9 64.4 - - TripletLoss [13] 84.9 69.1 - - Adver Occluded [14] 86.5 78.3 79.1 62.1 APR [22] 87.0 66.9 73.9 55.6 MultiScale [4] 88.9 [32].”
• Mask-Guided Contrastive Attention Model for Person Re-identification (2018, methodology): “The compared methods include the Deep Pyramid Feature Learning (DPFL) [10], SVDNet [33], and two re-ranking methods: DaF [42] and Re-ranking [52].”
• Memory-Based Neighbourhood Embedding for Visual Recognition (2019, neutral): “9 DPFL [4] ICCV’17 40.”
• Adaptively Connected Neural Networks (2019, methodology): “+Re-ranking [55], PAN, DPFL [4], and the newly proposed methods SVDNet [39], TriNet + Era.”
• Attention-Aware Compositional Network for Person Re-identification (2018, methodology): “One set is poseirrelevant, which includes the null space semi-supervised learning method (NFST) [49], the domain guided dropout method (JSTL) [44], the deep transfer learning method (Transfer) [16], the Singular Vector Decomposition method (SVDNet) [39], the Online Instance Matching (OIM) method [45”
• In Defense of the Triplet Loss for Person Re-Identification (2017, methodology): “We do not include approaches which are orthogonal to ours and could be integrated in a straightforward manner, such as various re-ranking schemes, data augmentation, and regularization [3, 43, 54, 7, 47, 53, 56].”
• A Pose-Sensitive Embedding for Person Re-identification with Expanded Cross Neighborhood Re-ranking (2017, methodology): “On the Market dataset our embedding performs slightly worse than the DPFL [3] approach which employs two or more multi-scale embeddings.”
• Interaction-And-Aggregation Network for Person Re-Identification (2019, background): “[7] proposed an architecture consisting of multiple branches for learning multi-scale features and one branch for feature fusion.”