no code implementations • 24 Apr 2024 • Oriol Barbany, Michael Huang, Xinliang Zhu, Arnab Dhua
Multimodal search has become increasingly important in providing users with a natural and effective way to ex-press their search intentions.
1 code implementation • 29 Nov 2022 • Chunyuan Li, Xinliang Zhu, Jiawen Yao, Junzhou Huang
Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical.
no code implementations • 25 Mar 2021 • Zhibo Yang, Muhammet Bastan, Xinliang Zhu, Doug Gray, Dimitris Samaras
In this paper, we present a framework that leverages this implicit hierarchy by imposing a hierarchical structure on the proxies and can be used with any existing proxy-based loss.
1 code implementation • 23 Sep 2020 • Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala, Nicholas Hawkins, Junzhou Huang
We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions.
no code implementations • ECCV 2020 • Jinyu Yang, Weizhi An, Sheng Wang, Xinliang Zhu, Chaochao Yan, Junzhou Huang
Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation.
Ranked #27 on Domain Adaptation on SYNTHIA-to-Cityscapes
no code implementations • 17 Aug 2017 • Feiyun Zhu, Xinliang Zhu, Sheng Wang, Jiawen Yao, Junzhou Huang
In the critic updating, the capped-$\ell_{2}$ norm is used to measure the approximation error, which prevents outliers from dominating our objective.
no code implementations • CVPR 2017 • Xinliang Zhu, Jiawen Yao, Feiyun Zhu, Junzhou Huang
Different from existing state-of-the-arts image-based survival models which extract features using some patches from small regions of WSIs, the proposed framework can efficiently exploit and utilize all discriminative patterns in WSIs to predict patients' survival status.
no code implementations • 25 Mar 2017 • Feiyun Zhu, Peng Liao, Xinliang Zhu, Yaowen Yao, Junzhou Huang
In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth.
no code implementations • 12 Sep 2014 • Feiyun Zhu, Bin Fan, Xinliang Zhu, Ying Wang, Shiming Xiang, Chunhong Pan
Subset selection from massive data with noised information is increasingly popular for various applications.