RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning

This paper delves into the results of two resource-constrained deep learning challenges, part of the workshop on Resource-Efficient Deep Learning for Computer Vision (RCV) at ICCV 2023, focusing on memory and time limitations. The challenges garnered significant global participation and showcased a...

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Bibliographic Details
Published in:	2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) pp. 1526 - 1535
Main Authors:	Tiwari, Rishabh, Chavan, Arnav, Gupta, Deepak, Mago, Gowreesh, Gupta, Animesh, Gupta, Akash, Sharan, Suraj, Yang, Yukun, Zhao, Shanwei, Wang, Shihao, Kwak, Youngjun, Jeong, Seonghun, Lee, Yunseung, Kim, Changick, Kim, Subin, Gankhuyag, Ganzorig, Jung, Ho, Ryu, Junwhan, Kim, HaeMoon, Kim, Byeong H., Vo, Tu, Zaheer, Sheir, Holston, Alexander, Park, Chan, Dixit, Dheemant, Lele, Nahush, Bhushan, Kushagra, Bhowmick, Debjani, Arya, Devanshu, Gulshad, Sadaf, Habibian, Amirhossein, Ghodrati, Amir, Bejnordi, Babak, Gupta, Jai, Liu, Zhuang, Yu, Jiahui, Prasad, Dilip, Shen, Zhiqiang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 02-10-2023
Subjects:	Benchmark testing Computational modeling Computer vision Conferences Deep learning Focusing Training
Online Access:	Get full text
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Summary:	This paper delves into the results of two resource-constrained deep learning challenges, part of the workshop on Resource-Efficient Deep Learning for Computer Vision (RCV) at ICCV 2023, focusing on memory and time limitations. The challenges garnered significant global participation and showcased a range of intriguing solutions. The paper outlines the problem statements for both tracks, summarizes baseline and top-performing approaches, and provides a detailed analysis of the methods used. While the presented solutions constitute promising initial progress, they represent the beginning of efforts needed to address this complex issue. We conclude by emphasizing the importance of sustained research efforts to fully address the challenges of resource-constrained deep learning.
ISSN:	2473-9944
DOI:	10.1109/ICCVW60793.2023.00168