Guided Attention Network for Object Detection and Counting on Drones-Reference-Cited by-同舟云学术

Guided Attention Network for Object Detection and Counting on Drones

Published:2020-10-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM International Conference on Multimedia
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Author:

YuanQiang Cai¹,Du Dawei²,Zhang Libo³,Wen Longyin⁴,Wang Weiqiang¹,Wu Yanjun³,Lyu Siwei²

Affiliation:

1. University of Chinese Academy of Sciences, Beijing, China

2. University at Albany, State University of New York, Albany, NY, NY, USA

3. Institute of Software Chinese Academy of Sciences, Beijing, China

4. JD Finance America Corporation, Mountain View, CA, CA, USA

Funder

Key Research Program of Frontier Sciences, CAS

National Key R&D Program of China

National Natural Science Foundation of China

Ningbo 2025 Key Project of Science and Technology Innovation

NSFC Key Projects of International (Regional) Cooperation and Exchanges

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394171.3413816

Reference55 articles.

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2. Hisham Cholakkal Guolei Sun Fahad Shahbaz Khan and Ling Shao. 2019. Object Counting and Instance Segmentation with Image-level Supervision. CoRR Vol. abs/1903.02494 (2019). Hisham Cholakkal Guolei Sun Fahad Shahbaz Khan and Ling Shao. 2019. Object Counting and Instance Segmentation with Image-level Supervision. CoRR Vol. abs/1903.02494 (2019).

3. Jifeng Dai Yi Li Kaiming He and Jian Sun. 2016. R-FCN: Object Detection via Region-based Fully Convolutional Networks. In NeurIPS. 379--387. Jifeng Dai Yi Li Kaiming He and Jian Sun. 2016. R-FCN: Object Detection via Region-based Fully Convolutional Networks. In NeurIPS. 379--387.

4. PKLot – A robust dataset for parking lot classification

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