Search Results for author: Zihan Li

Found 25 papers, 16 papers with code

nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance

1 code implementation29 Sep 2023 Yunxiang Li, Bowen Jing, Zihan Li, Jing Wang, You Zhang

The recent developments of foundation models in computer vision, especially the Segment Anything Model (SAM), allow scalable and domain-agnostic image segmentation to serve as a general-purpose segmentation tool.

Few-Shot Learning Image Segmentation +2

SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation

1 code implementation24 Jul 2023 YiQing Wang, Zihan Li, Jieru Mei, Zihao Wei, Li Liu, Chen Wang, Shengtian Sang, Alan Yuille, Cihang Xie, Yuyin Zhou

To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis.

Contrastive Learning Image Reconstruction +4

Go Beyond The Obvious: Probing the gap of INFORMAL reasoning ability between Humanity and LLMs by Detective Reasoning Puzzle Benchmark

no code implementations11 Jul 2023 Zhouhon Gu, Zihan Li, Lin Zhang, Zhuozhi Xiong, Haoning Ye, Yikai Zhang, Wenhao Huang, Xiaoxuan Zhu, Qianyu He, Rui Xu, Sihang Jiang, Shusen Wang, Zili Wang, Hongwei Feng, Zhixu Li, Yanghua Xiao

Informal reasoning ability is the ability to reason based on common sense, experience, and intuition. Humans use informal reasoning every day to extract the most influential elements for their decision-making from a large amount of life-like information. With the rapid development of language models, the realization of general artificial intelligence has emerged with hope.

Common Sense Reasoning Decision Making +1

Multi-view MERA Subspace Clustering

1 code implementation16 May 2023 Zhen Long, Ce Zhu, Jie Chen, Zihan Li, Yazhou Ren, Yipeng Liu

Benefiting from multiple interactions among orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong representation power to capture the complex inter/intra-view information in the self-representation tensor.

Clustering Multi-view Subspace Clustering

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

1 code implementation6 May 2023 Chunkit Chan, Xin Liu, Jiayang Cheng, Zihan Li, Yangqiu Song, Ginny Y. Wong, Simon See

Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to recognize the discourse relations between the arguments with the absence of discourse connectives.

text-classification Text Classification

ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge

1 code implementation24 Mar 2023 Yunxiang Li, Zihan Li, Kai Zhang, Ruilong Dan, Steve Jiang, You Zhang

The primary aim of this research was to address the limitations observed in the medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by creating a specialized language model with enhanced accuracy in medical advice.

Information Retrieval Language Modelling +3

SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data

no code implementations10 Nov 2022 Jiaxin Xiao, Zihan Li, Berkin Bilgic, Jonathan R. Polimeni, Susie Huang, Qiyuan Tian

Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a barrier to feasible and accessible implementation.

Denoising Super-Resolution

Regret Bounds for Noise-Free Cascaded Kernelized Bandits

no code implementations10 Nov 2022 Zihan Li, Jonathan Scarlett

We consider optimizing a function network in the noise-free grey-box setting with RKHS function classes, where the exact intermediate results are observable.

Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency

1 code implementation19 May 2022 Zihan Li, Wentao Chen, Zhiqing Wei, Xingqi Luo, Bing Su

In addition, to cope with new attacks in real-world deployment, we propose an Active Adaption Resampling (AAR) method, which can better discover the distribution of unseen sample data and adapt the parameters of encoder.

A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits

no code implementations3 Feb 2022 Ilija Bogunovic, Zihan Li, Andreas Krause, Jonathan Scarlett

We consider the sequential optimization of an unknown, continuous, and expensive to evaluate reward function, from noisy and adversarially corrupted observed rewards.

Gaussian Process Bandit Optimization with Few Batches

no code implementations15 Oct 2021 Zihan Li, Jonathan Scarlett

In addition, in the case of a constant number of batches (not depending on $T$), we propose a modified version of our algorithm, and characterize how the regret is impacted by the number of batches, focusing on the squared exponential and Mat\'ern kernels.

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

no code implementations8 Mar 2020 Jinghua Zhang, Chen Li, Frank Kulwa, Xin Zhao, Changhao Sun, Zihan Li, Tao Jiang, Hong Li

In order to assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multi-scale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper.

Image Segmentation Semantic Segmentation

SummerTime: Variable-length Time SeriesSummarization with Applications to PhysicalActivity Analysis

no code implementations20 Feb 2020 Kevin M. Amaral, Zihan Li, Wei Ding, Scott Crouter, Ping Chen

Summarization conducted by the \textit{SummerTime} method will be a fixed-length feature vector which can be used in-place of the time series dataset for use with classical machine learning methods.

General Classification regression +2

Learning Erdos-Renyi Random Graphs via Edge Detecting Queries

1 code implementation NeurIPS 2019 Zihan Li, Matthias Fresacher, Jonathan Scarlett

In this paper, we consider the problem of learning an unknown graph via queries on groups of nodes, with the result indicating whether or not at least one edge is present among those nodes.

Learning Erdős-Rényi Random Graphs via Edge Detecting Queries

1 code implementation9 May 2019 Zihan Li, Matthias Fresacher, Jonathan Scarlett

In this paper, we consider the problem of learning an unknown graph via queries on groups of nodes, with the result indicating whether or not at least one edge is present among those nodes.

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