no code implementations • 25 Apr 2024 • Zhiwei Wang, Ying Zhou, Shiquan He, Ting Li, Fan Huang, Qiang Ding, Xinxia Feng, Mei Liu, Qiang Li
Photometric constraint is indispensable for self-supervised monocular depth estimation.
no code implementations • 1 Apr 2024 • Qiang Hu, Zhenyu Yi, Ying Zhou, Ting Li, Fan Huang, Mei Liu, Qiang Li, Zhiwei Wang
We propose MonoBox, an innovative box-supervised segmentation method constrained by monotonicity to liberate its training from the user-unfriendly box-tightness assumption.
no code implementations • 6 Feb 2024 • Mei Liu, Leon Yang
Deep learning algorithms can process and learn from large amounts of data and can also be trained using unsupervised learning techniques, meaning they don't require labelled data to detect anomalies.
no code implementations • 11 Oct 2023 • Zhiwei Wang, Qiang Hu, Hongkuan Shi, Li He, Man He, Wenxuan Dai, Ting Li, Yitong Zhang, Dun Li, Mei Liu, Qiang Li
In response, we propose two innovative learning fashions, Improved Box-dice (IBox) and Contrastive Latent-Anchors (CLA), and combine them to train a robust box-supervised model IBoxCLA.
no code implementations • 24 Aug 2023 • Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Bin Liu, Mei Liu, Zijun Yao
Therefore, in this paper, we propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework that utilizes survival durations from both censored and observed data to define temporal distinctiveness and construct negative sample pairs with adjustable hardness for contrastive learning.
1 code implementation • journal 2023 • Chuan Qin, Liangming Chen, Zangtai Cai, Mei Liu, Long Jin
As the number of long short-term memory (LSTM) layers increases, vanishing/exploding gradient problems exacerbate and have a negative impact on the performance of the LSTM.
no code implementations • 20 Oct 2021 • Sijia Liu, Andrew Wen, LiWei Wang, Huan He, Sunyang Fu, Robert Miller, Andrew Williams, Daniel Harris, Ramakanth Kavuluru, Mei Liu, Noor Abu-el-rub, Dalton Schutte, Rui Zhang, Masoud Rouhizadeh, John D. Osborne, Yongqun He, Umit Topaloglu, Stephanie S Hong, Joel H Saltz, Thomas Schaffter, Emily Pfaff, Christopher G. Chute, Tim Duong, Melissa A. Haendel, Rafael Fuentes, Peter Szolovits, Hua Xu, Hongfang Liu, Natural Language Processing, Subgroup, National COVID Cohort Collaborative
Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.
2 code implementations • 9 Jul 2021 • Mei Liu, Liangming Chen, Xiaohao Du, Long Jin, Mingsheng Shang
The experimental results also demonstrate that the proposed method is able to be adopted in various deep neural networks to improve their performance.
no code implementations • 14 Sep 2020 • Liangming Chen, Long Jin, Xiujuan Du, Shuai Li, Mei Liu
With visualizations of loss landscapes, we evaluate the flatnesses of minima obtained by both the original optimizer and optimizers enhanced by VDMs on CIFAR-100.
no code implementations • 24 Jul 2020 • Liangming Chen, Long Jin, Xiujuan Du, Shuai Li, Mei Liu
Furthermore, the flatter minima could be obtained by exploiting the proposed deformation functions, which is verified on CIFAR-100, with visualizations of loss landscapes near the critical points obtained by both the original optimizer and optimizer enhanced by deformation functions.
no code implementations • 13 Jul 2020 • Jingqi Wang, Noor Abu-el-rub, Josh Gray, Huy Anh Pham, Yujia Zhou, Frank Manion, Mei Liu, Xing Song, Hua Xu, Masoud Rouhizadeh, Yaoyun Zhang
To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text.
no code implementations • Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019), Phuket, Thailand 2019 • Liting Zhu, Kinbo Chen, Jinjun Rao, Mei Liu
Aiming at the dense texture features of the end face of rolled steel coil, a method of detecting the defects of the end face of steel coils was proposed.