Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification

By Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, Jing Shao, Junjie Yan, Shengjin Wang, Hongsheng Li, Xiaogang Wang. IEEE International Conference on Computer Vision, 2017

359 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/7e186b41f5d2cfdf1940009e61d4e34a47b33c7c

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Jun Liu
2. Jie Zhou
3. Jiwen Lu
4. Ling-yu Duan
5. Ramalingam Chellappa
6. G. Wang
7. Luc Van Gool
8. Dacheng Tao

In Ocean Engineering

1. Zhengbiao Zhu
2. Ramalingam Chellappa

Citations in the corpus

• The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification (2020, background): “key-points [41, 16, 17] and parts bounding boxes [11, 46] in addition to the ID of the objects of interest.”
• SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving (2023, methodology): “While early methods require full depth annotations to supervise the depth estimation model [15, 25, 66], later research focuses on self-supervised depth estimation as it does not require intensive human annotations [70, 17, 5, 67, 62, 56, 69, 43, 9].”
• VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild (2019, methodology): “Compared with OIFE [21], which used key-point alignment in vehicle feature representation, the proposed FDA-Net achieves much better performance.”