The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy with constraints such as latency, memory capacity, and energy use. The 2025 challenge featured three tracks: (1) Image classification under various lighting conditions and styles, (2) Open-Vocabulary Segmentation with Text Prompt, and (3) Monocular Depth Estimation. This paper presents the design of LPCVC 2025, including its competition structure and evaluation framework, which integrates the Qualcomm AI Hub for consistent and reproducible benchmarking. The paper also introduces the top-performing solutions from each track and outlines key trends and observations. The paper concludes with suggestions for future computer vision competitions.
Cite
@article{arxiv.2604.19054,
title = {Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge},
author = {Zihao Ye and Yung-Hsiang Lu and Xiao Hu and Shuai Zhang and Taotao Jing and Xin Li and Zhen Yao and Bo Lang and Zhihao Zheng and Seungmin Oh and Hankyul Kang and Seunghun Kang and Jongbin Ryu and Kexin Chen and Yuan Qi and George K Thiruvathukal and Mooi Choo Chuah},
journal= {arXiv preprint arXiv:2604.19054},
year = {2026}
}