Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization

1 code implementation • 21 Mar 2024 • Guopeng Li, Ming Qian, Gui-Song Xia

This paper investigates the effective utilization of unlabeled data for large-area cross-view geo-localization (CVGL), encompassing both unsupervised and semi-supervised settings.

Re-Ranking

Depth and DOF Cues Make A Better Defocus Blur Detector

1 code implementation • 20 Jun 2023 • Yuxin Jin, Ming Qian, Jincheng Xiong, Nan Xue, Gui-Song Xia

Our method proposes a depth feature distillation strategy to obtain depth knowledge from a pre-trained monocular depth estimation model and uses a DOF-edge loss to understand the relationship between DOF and depth.

Defocus Blur Detection Monocular Depth Estimation

Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs

1 code implementation • ICCV 2023 • Ming Qian, Jincheng Xiong, Gui-Song Xia, Nan Xue

This paper aims to develop an accurate 3D geometry representation of satellite images using satellite-ground image pairs.

Cross-View Image-to-Image Translation Generalizable Novel View Synthesis +2

LUAI Challenge 2021 on Learning to Understand Aerial Images

1 code implementation • 30 Aug 2021 • Gui-Song Xia, Jian Ding, Ming Qian, Nan Xue, Jiaming Han, Xiang Bai, Michael Ying Yang, Shengyang Li, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, Liangpei Zhang, Qiang Zhou, Chao-hui Yu, Kaixuan Hu, Yingjia Bu, Wenming Tan, Zhe Yang, Wei Li, Shang Liu, Jiaxuan Zhao, Tianzhi Ma, Zi-han Gao, Lingqi Wang, Yi Zuo, Licheng Jiao, Chang Meng, Hao Wang, Jiahao Wang, Yiming Hui, Zhuojun Dong, Jie Zhang, Qianyue Bao, Zixiao Zhang, Fang Liu

This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.

Object object-detection +4

Defocus Blur Detection via Salient Region Detection Prior

no code implementations • 19 Nov 2020 • Ming Qian, Min Xia, Chunyi Sun, Zhiwei Wang, Liguo Weng

Defocus blur Detection aims to separate the out-of-focus and depth-of-field areas in photos, which is an important work in computer vision.

Defocus Blur Detection

AIM 2020 Challenge on Rendering Realistic Bokeh

no code implementations • 10 Nov 2020 • Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, Xianrui Luo, Ke Xian, Zijin Wu, Zhiguo Cao, Densen Puthussery, Jiji C V, Hrishikesh P S, Melvin Kuriakose, Saikat Dutta, Sourya Dipta Das, Nisarg A. Shah, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan, Saagara M B, Minnu A L, Sanjana A R, Praseeda S, Ge Wu, Xueqin Chen, Tengyao Wang, Max Zheng, Hulk Wong, Jay Zou

This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.

Bokeh Effect Rendering

BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh

1 code implementation • 4 Nov 2020 • Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng

A photo captured with bokeh effect often means objects in focus are sharp while the out-of-focus areas are all blurred.

Bokeh Effect Rendering Generative Adversarial Network +1

A Comparative Study of English-Chinese Translations of Court Texts by Machine and Human Translators and the Word2Vec Based Similarity Measure's Ability To Gauge Human Evaluation Biases

no code implementations • WS 2019 • Ming Qian, Jessie Liu, Chao-Feng Li, Liming Pals