An efficient framework for counting pedestrians crossing a line using low-cost devices: the benefits of distilling the knowledge in a neural network

By Yih-Kai Lin, Chu-Fu Wang, Ching-Yu Chang, Hao Sun. 2020

33 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Global and Planetary Change, Transportation, Efficiency & Compression

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. Mohammad Rastegari
2. Sachin Mehta
3. Oncel Tuzel
4. Luc Van Gool
5. Xiaogang Wang
6. Ali Farhadi
7. Dacheng Tao
8. Ming-Hsuan Yang

In Efficiency & Compression

1. Oncel Tuzel
2. Hadi Pouransari
3. Ali Farhadi
4. Mohammad Rastegari
5. Sachin Mehta
6. Mehrdad Farajtabar
7. Dacheng Tao
8. Song Han

Citations in the corpus

• Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement (2023, methodology): “We build on top of the well-known Knowledge Distillation framework [9, 2, 35], the effectiveness of which has been extensively studied [12, 56].”