no code implementations • ICCV 2023 • Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training.
1 code implementation • CVPR 2023 • Haoqian Wu, Zhipeng Hu, Lincheng Li, Yongqiang Zhang, Changjie Fan, Xin Yu
Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images.
Ranked #2 on Surface Normals Estimation on Stanford-ORB
no code implementations • CVPR 2023 • Yongqiang Zhang, Zhipeng Hu, Haoqian Wu, Minda Zhao, Lincheng Li, Zhengxia Zou, Changjie Fan
In this paper, we argue that this limited accuracy is due to the bias of their volume rendering strategies, especially when the viewing direction is close to be tangent to the surface.
no code implementations • 6 Dec 2022 • Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision.
no code implementations • 9 May 2022 • Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes.
1 code implementation • NeurIPS 2021 • Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
In view of this limitation, we consider a more practical setting of complete absence of co-occurrence of the base and novel classes for the object detection task.
no code implementations • 21 Jun 2020 • Gaofeng Pan, Jia Ye, Yongqiang Zhang, Mohamed-Slim Alouini
Aerial relays have been regarded as an alternative and promising solution to extend and improve satellite-terrestrial communications, as the probability of line-of-sight transmissions increases compared with adopting terrestrial relays.
no code implementations • ECCV 2018 • Yancheng Bai, Yongqiang Zhang, Mingli Ding, Bernard Ghanem
In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection.
no code implementations • CVPR 2018 • Yongqiang Zhang, Yancheng Bai, Mingli Ding, Yongqiang Li, Bernard Ghanem
Finally, we use these pseudo ground-truths to train a fully-supervised detector.
no code implementations • CVPR 2018 • Yancheng Bai, Yongqiang Zhang, Mingli Ding, Bernard Ghanem
In this paper, we proposed an algorithm to directly generate a clear high-resolution face from a blurry small one by adopting a generative adversarial network (GAN).
no code implementations • CVPR 2017 • Yongqiang Zhang, Daming Shi, Junbin Gao, Dansong Cheng
Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications.