Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-identification

By Nikolaos Karianakis, Zicheng Liu, Yinpeng Chen, Stefano Soatto. 2017

43 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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. Jianbing Shen
2. Ling Shao
3. Mang Ye
4. Tao A. Xiang
5. Steven C. H. Hoi
6. Ajmal Saeed Mian
7. Gaojie Lin
8. Fahad Shahbaz Khan

In Computer Vision and Pattern Recognition

1. Ling Shao
2. Jianbing Shen
3. Luc Van Gool
4. Steven C. H. Hoi
5. Ming-Hsuan Yang
6. Fahad Shahbaz Khan
7. Dacheng Tao
8. Xiaogang Wang

In Representation & Self-Supervised Learning

1. Ling Shao
2. Tao A. Xiang
3. Jianbing Shen
4. Steven C. H. Hoi
5. Ajmal Saeed Mian
6. Fahad Shahbaz Khan
7. Mang Ye
8. Saeed Anwar

Citations in the corpus

• Deep Learning for Person Re-Identification: A Survey and Outlook (2020, background): “[184] leverage the large RGB datasets to design a split-rate RGB-to-Depth transfer method, which bridges the gap between the depth images and the RGB images.”
• Visual Attention Methods in Deep Learning: An In-Depth Survey (2022, background): “In [81], reinforcement attention was proposed to extract better temporal context from video.”