Search Results for author:
Found 26 papers, 17 papers with code
ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation
The proposed ScribFormer model has a triple-branch structure, i. e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch.
Ranked #1 on
Semantic Segmentation
on ACDC Scribbles
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
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.
Can Large Language Models Understand Real-World Complex Instructions?
To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding
We evaluate ScribbleVC on three benchmark datasets and compare it with state-of-the-art methods.
Ranked #2 on
Semantic Segmentation
on ACDC Scribbles
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
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.
Piecing Together Clues: A Benchmark for Evaluating the Detective Skills of Large Language Models
We introduces the DetectBench, a reading comprehension dataset designed to assess a model's ability to jointly ability in key information detection and multi-hop reasoning when facing complex and implicit information.
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs).
Multi-view MERA Subspace Clustering
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.
DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition
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.
Domain Mastery Benchmark: An Ever-Updating Benchmark for Evaluating Holistic Domain Knowledge of Large Language Model--A Preliminary Release
Domain knowledge refers to the in-depth understanding, expertise, and familiarity with a specific subject, industry, field, or area of special interest.
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge
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.
Implicit Neural Representation for Cooperative Low-light Image Enhancement
Finally, extensive experiments demonstrate the robustness and superior effectiveness of our proposed NeRCo.
Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment
Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19.
Ranked #1 on
Medical Image Segmentation
on MosMedData
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data
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.
Regret Bounds for Noise-Free Cascaded Kernelized Bandits
We consider optimizing a function network in the noise-free grey-box setting with RKHS function classes, where the exact intermediate results are observable.
TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation
Medical image segmentation is one of the most fundamental tasks concerning medical information analysis.
Ranked #5 on
Semantic Segmentation
on ACDC Scribbles
LViT: Language meets Vision Transformer in Medical Image Segmentation
In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data.
Ranked #1 on
Medical Image Segmentation
on MoNuSeg
Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency
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
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
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.
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks
EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions.
A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation
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.
SummerTime: Variable-length Time SeriesSummarization with Applications to PhysicalActivity Analysis
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.
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries
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.
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
Ranked #8 on
Dialogue State Tracking
on CoSQL
Learning Erdős-Rényi Random Graphs via Edge Detecting Queries
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.
