LocLoc: Low-level Cues and Local-area Guides for Weakly Supervised Object Localization-Reference-Cited by-同舟云学术

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LocLoc: Low-level Cues and Local-area Guides for Weakly Supervised Object Localization

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
language:
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Author:

Cao Xinzi¹^ORCID,Zheng Xiawu²^ORCID,Shen Yunhang³^ORCID,Li Ke³^ORCID,Chen Jie²^ORCID,Lu Yutong⁴^ORCID,Tian Yonghong⁵^ORCID

Affiliation:

1. Sun Yat-Sen University & Peng Cheng Laboratory, Guangzhou, China

2. Peng Cheng Laboratory, Shenzhen, China

3. Tencent, Shanghai, China

4. Sun Yat-Sen University & The National Supercomputer Center in Guangzhou, Guangzhou, China

5. Peking University & Peng Cheng Laboratory, Beijing, China

Funder

Key-Area Research and Development Program of Guangdong Province

The Major Program of Guangdong Basic and Applied Research

The Major Key Project of PCL

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612165

Reference42 articles.

1. Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration

2. End-to-End Object Detection with Transformers

3. Emerging Properties in Self-Supervised Vision Transformers

4. Image Processing and Analysis

5. Histograms of oriented mosaic gradients for snapshot spectral image description

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