Search Results for author: Fan Zhong

Found 7 papers, 3 papers with code

TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation

1 code implementation17 Nov 2023 Xiaoqiong Xia, Chaoyu Zhu, Yuqi Shan, Fan Zhong, Lei Liu

Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR), their performances can be improved by addressing issues such as insufficient data modality, suboptimal fusion algorithms, and poor generalizability for novel drugs or cell lines.

Drug Response Prediction Transfer Learning

For A More Comprehensive Evaluation of 6DoF Object Pose Tracking

no code implementations14 Sep 2023 Yang Li, Fan Zhong, Xin Wang, Shuangbing Song, Jiachen Li, Xueying Qin, Changhe Tu

The limitations of previous scoring methods and error metrics are analyzed, based on which we introduce our improved evaluation methods.

Pose Tracking

Guided Linear Upsampling

no code implementations13 Jul 2023 Shuangbing Song, Fan Zhong, Tianju Wang, Xueying Qin, Changhe Tu

We demonstrate the advantages of our method for both interactive image editing and real-time high-resolution video processing.

Large-displacement 3D Object Tracking with Hybrid Non-local Optimization

1 code implementation26 Jul 2022 Xuhui Tian, Xinran Lin, Fan Zhong, Xueying Qin

Optimization-based 3D object tracking is known to be precise and fast, but sensitive to large inter-frame displacements.

3D Object Tracking Object Tracking

BCOT: A Markerless High-Precision 3D Object Tracking Benchmark

no code implementations CVPR 2022 Jiachen Li, Bin Wang, Shiqiang Zhu, Xin Cao, Fan Zhong, Wenxuan Chen, Te Li, Jason Gu, Xueying Qin

Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes.

3D Object Tracking Object +2

Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation

2 code implementations19 May 2018 Jichao Zhang, Yezhi Shu, Songhua Xu, Gongze Cao, Fan Zhong, Meng Liu, Xueying Qin

To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled.

Attribute Image-to-Image Translation +3

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