Few-shot Object Counting with Low-cost Counting Phase-Reference-Cited by-同舟云学术

Few-shot Object Counting with Low-cost Counting Phase

Published:2023-12-07 Issue: Volume: Page:335-342
ISSN:
Container-title:Proceedings of the 12th International Symposium on Information and Communication Technology
language:
Short-container-title:

Author:

Nguyen-Quang Thuc¹^ORCID,Tran Minh-Triet²^ORCID

Affiliation:

1. Software Engineering Laboratory and John von Neumann Institute, University of Science, VNU-HCM, Viet Nam

2. Faculty of Information Technology, Software Engineering Laboratory, and John von Neumann Institute, University of Science, VNU-HCM, Viet Nam

Funder

University of Science, VNU-HCM

Vingroup Innovation Foundation (VINIF)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3628797.3628937

Reference22 articles.

1. N. Amini-Naieni, K. Amini-Naieni, T. Han, and A. Zisserman. 2023. Open-world Text-specified Object Counting. In British Machine Vision Conference.

2. Qi Fan, Wei Zhuo, and Yu-Wing Tai. 2019. Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), 4012–4021. https://api.semanticscholar.org/CorpusID:199452945

3. Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector

4. Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (Sydney, NSW, Australia) (ICML’17). JMLR.org, 1126–1135.

5. Agrim Gupta, Piotr Dollár, and Ross B. Girshick. 2019. LVIS: A Dataset for Large Vocabulary Instance Segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), 5351–5359. https://api.semanticscholar.org/CorpusID:195441339