Search Results for author: Zhihua Liu

Found 12 papers, 8 papers with code

UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo Matching

no code implementations ICCV 2021 Yamin Mao, Zhihua Liu, Weiming Li, Yuchao Dai, Qiang Wang, Yun-Tae Kim, Hong-Seok Lee

Extensive experiments show that the proposed method achieves the highest ground truth covering ratio compared with other cascade cost volume based stereo matching methods.

Stereo Matching

Structured Context Enhancement Network for Mouse Pose Estimation

1 code implementation1 Dec 2020 Feixiang Zhou, Zheheng Jiang, Zhihua Liu, Fang Chen, Long Chen, Lei Tong, Zhile Yang, Haikuan Wang, Minrui Fei, Ling Li, Huiyu Zhou

However, quantifying mouse behaviours from videos or images remains a challenging problem, where pose estimation plays an important role in describing mouse behaviours.

Animal Pose Estimation

Perceptual underwater image enhancement with deep learning and physical priors

1 code implementation21 Aug 2020 Long Chen, Zheheng Jiang, Lei Tong, Zhihua Liu, Aite Zhao, Qianni Zhang, Junyu Dong, Huiyu Zhou

Underwater image enhancement, as a pre-processing step to improve the accuracy of the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration.

Image Enhancement Image Generation +2

CANet: Context Aware Network for 3D Brain Glioma Segmentation

1 code implementation15 Jul 2020 Zhihua Liu, Lei Tong, Long Chen, Feixiang Zhou, Zheheng Jiang, Qianni Zhang, Yinhai Wang, Caifeng Shan, Ling Li, Huiyu Zhou

Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.

Brain Tumor Segmentation Tumor Segmentation

Underwater object detection using Invert Multi-Class Adaboost with deep learning

1 code implementation23 May 2020 Long Chen, Zhihua Liu, Lei Tong, Zheheng Jiang, Shengke Wang, Junyu Dong, Huiyu Zhou

In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet.

object-detection Small Object Detection

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