Knowledge Distillation for End-to-End Person Search

By Bharti Munjal, Fabio Galasso, Sikandar Amin. 2019

18 citations recorded by the source corpus.

We introduce knowledge distillation for end-to-end person search. End-to-End methods are the current state-of-the-art for person search that solve both detection and re-identification jointly. These approaches for joint optimization show their largest drop in performance due to a sub-optimal detector. We propose two distinct approaches for extra supervision of end-to-end person search methods in a teacher-student setting. The first is adopted from state-of-the-art knowledge distillation in object detection. We employ this to supervise the detector of our person search model at various levels using a specialized detector. The second approach is new, simple and yet considerably more effective. This distills knowledge from a teacher re-identification technique via a pre-computed look-up table of ID features. It relaxes the learning of identification features and allows the student to focus on the detection task. This procedure not only helps fixing the sub-optimal detector training in the joint optimization and simultaneously improving the person search, but also closes the performance gap between the teacher and the student for model compression in this case. Overall, we demonstrate significant improvements for two recent state-of-the-art methods using our proposed knowledge distillation approach on two benchmark datasets. Moreover, on the model compression task our approach brings the performance of smaller models on par with the larger models.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering

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

In Computer Vision and Pattern Recognition

1. Xinlong Wang
2. Chunhua Shen
3. Jiawang Bian
4. Baocai Yin
5. Mingyu You
6. Hongchen Tan
7. Xiuping Liu
8. Meng Liu

In Ocean Engineering

1. Chunhua Shen
2. Xinlong Wang

Citations in the corpus

• KT-GAN: Knowledge-Transfer Generative Adversarial Network for Text-to-Image Synthesis (2020, background): “Reference [2] propose two distinct teacher-student frameworks based on knowledge distillation”
• Diverse Knowledge Distillation for End-to-End Person Search (2020, methodology): “For the Re-ID model, we use the softmax cross-entropy loss and the SGD (Munjal, Amin, and Tombari 2019), (Munjal, Galasso, and Amin 2019) and (Zhong, Wang, and Zhang 2020).”