2023 Low-Power Computer Vision Challenge (LPCVC) Summary

no code implementations • 11 Mar 2024 • Leo Chen, Benjamin Boardley, Ping Hu, Yiru Wang, Yifan Pu, Xin Jin, Yongqiang Yao, Ruihao Gong, Bo Li, Gao Huang, Xianglong Liu, Zifu Wan, Xinwang Chen, Ning Liu, Ziyi Zhang, Dongping Liu, Ruijie Shan, Zhengping Che, Fachao Zhang, Xiaofeng Mou, Jian Tang, Maxim Chuprov, Ivan Malofeev, Alexander Goncharenko, Andrey Shcherbin, Arseny Yanchenko, Sergey Alyamkin, Xiao Hu, George K. Thiruvathukal, Yung Hsiang Lu

This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC).

AmphibianDetector: adaptive computation for moving objects detection

1 code implementation • 15 Nov 2020 • David Svitov, Sergey Alyamkin

In this paper, we propose an approach to object detection which makes it possible to reduce the number of false-positive detections by processing only moving objects and reduce the required processing power for algorithm inference.

Object object-detection +1

MarginDistillation: distillation for margin-based softmax

1 code implementation • 5 Mar 2020 • David Svitov, Sergey Alyamkin

The usage of convolutional neural networks (CNNs) in conjunction with a margin-based softmax approach demonstrates a state-of-the-art performance for the face recognition problem.

Face Identification Face Recognition

Fast Adjustable Threshold For Uniform Neural Network Quantization (Winning solution of LPIRC-II)

4 code implementations • 19 Dec 2018 • Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

It can be performed without fine-tuning using calibration procedure (calculation of parameters necessary for quantization), or it is possible to train the network with quantization from scratch.

Quantization