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ACM Computing Surveys

Volume 56, Issue 12

December 2024

2023 Impact Factor: 23.8 (ranked 1/143 in Computer Science Theory & Methods)

These comprehensive, readable surveys and tutorial papers give guided tours through the literature and explain topics to those who seek to learn the basics of areas outside their specialties in an accessible way. The carefully planned and presented introductions in Computing Surveys (CSUR) are also an excellent way for researchers and professionals to develop perspectives on, and identify trends in complex technologies. Contributions which bridge existing and emerging technologies (such as machine learning) with a variety of science and engineering domains in a novel and interesting way are also welcomed.

Contributions are intended to be accessible to a broad audience, featuring clear exposition, a lively tutorial style, and pointers to the literature for further study.

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Latest Issue

Volume 56, Issue 12 December 2024
Issue-in-Progress
ISSN:0360-0300
EISSN:1557-7341
View Table of Contents
survey
Open Access
Meta-learning Approaches for Few-Shot Learning: A Survey of Recent Advances
- Hassan Gharoun
  Data Science Institute, University of Technology Sydney, Sydney, Australia
  ,
- Fereshteh Momenifar
  Western Sydney University, Sydney, Australia
  ,
- Fang Chen
  Data Science Institute, University of Technology Sydney, Sydney, Australia
  ,
- Amir Gandomi
  Data Science Institute, University of Technology Sydney, Sydney, Australia and Obuda University, Budapest, Hungary
July 2024Article No.: 294 pp 1-41 https://doi.org/10.1145/3659943
survey
Open Access
Visual Tuning
- Bruce X.B. Yu
  Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining, China and Zhejiang Provincial Engineering Research Center for Multimodal Transport Logistics Large Models, Haining China
  ,
- Jianlong Chang
  Huawei, Shenzhen China
  ,
- Haixin Wang
  Peking University, National Engineering Research Center for Software Engineering, Beijing China
  ,
- Lingbo Liu
  Peng Cheng Laboratory, Shenzhen China
  ,
- Shijie Wang
  Huawei, Shenzhen China
  ,
- Zhiyu Wang
  Huawei, Shenzhen China
  ,
- Junfan Lin
  Peng Cheng Laboratory, Shenzhen China
  ,
- Lingxi Xie
  Huawei, Shenzhen China
  ,
- Haojie Li
  Shandong University of Science and Technology, College of Computer Science and Engineering, Qingdao China
  ,
- Zhouchen Lin
  National Key Lab of General AI, Peking University, School of Intelligence Science and Technology, Peking University, Beijing, China and Pazhou Laboratory (Huangpu), Guangzhou China
  ,
- Qi Tian
  Huawei, Shenzhen China
  ,
- Chang Wen Chen
  The Hong Kong Polytechnic University, Department of Computing, Hong Kong, Hong Kong
July 2024Article No.: 297 pp 1-38 https://doi.org/10.1145/3657632