no code implementations • 20 May 2024 • Xianpeng Liu, Ce Zheng, Ming Qian, Nan Xue, Chen Chen, Zhebin Zhang, Chen Li, Tianfu Wu
We present Multi-View Attentive Contextualization (MvACon), a simple yet effective method for improving 2D-to-3D feature lifting in query-based multi-view 3D (MV3D) object detection.
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.
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.
Ranked #1 on Defocus Blur Detection on EBD
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.
Ranked #1 on Cross-View Image-to-Image Translation on CVACT
Cross-View Image-to-Image Translation Generalizable Novel View Synthesis +2
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.
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.
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.
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.