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Found 27 papers, 8 papers with code
Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues
Existing methods target instructions from real instruction dialogues as a learning goal and fine-tune a user simulator for posing instructions.
BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval
The burgeoning integration of 3D medical imaging into healthcare has led to a substantial increase in the workload of medical professionals.
Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement
Through multimodal learning on ECG records and associated reports, MERL is capable of performing zero-shot ECG classification with text prompts, eliminating the need for training data in downstream tasks.
Electrocardiogram Instruction Tuning for Report Generation
Electrocardiogram (ECG) serves as the primary non-invasive diagnostic tool for cardiac conditions monitoring, are crucial in assisting clinicians.
ARKS: Active Retrieval in Knowledge Soup for Code Generation
Recently the retrieval-augmented generation (RAG) paradigm has raised much attention for its potential in incorporating external knowledge into large language models (LLMs) without further training.
Enhancing Role-playing Systems through Aggressive Queries: Evaluation and Improvement
Experiments on models improved by RoleAD indicate that our adversarial dataset ameliorates this deficiency, with the improvements demonstrating a degree of generalizability in ordinary scenarios.
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training
Modern healthcare often utilises radiographic images alongside textual reports for diagnostics, encouraging the use of Vision-Language Self-Supervised Learning (VL-SSL) with large pre-trained models to learn versatile medical vision representations.
Efficient Large Language Models: A Survey
Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding, language generation, and complex reasoning and have the potential to make a substantial impact on our society.
T3D: Towards 3D Medical Image Understanding through Vision-Language Pre-training
This underlines T3D's potential in representation learning for 3D medical image analysis.
G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training
G2D achieves superior performance across 6 medical imaging tasks and 25 diseases, particularly in semantic segmentation, which necessitates fine-grained, semantically-grounded image features.
Image Recognition of Oil Leakage Area Based on Logical Semantic Discrimination
The process begins with histogram equalization to enhance the original image, followed by the use of Mask RCNN to identify the preliminary positions and outlines of oil tanks, the ground, and areas of potential oil contamination.
DialogBench: Evaluating LLMs as Human-like Dialogue Systems
In this paper, we propose DialogBench, a dialogue evaluation benchmark that contains 12 dialogue tasks to probe the capabilities of LLMs as human-like dialogue systems should have.
Efficient deep data assimilation with sparse observations and time-varying sensors
We introduce a novel variational DA scheme, named Voronoi-tessellation Inverse operator for VariatIonal Data assimilation (VIVID), that incorporates a DL inverse operator into the assimilation objective function.
OpenAgents: An Open Platform for Language Agents in the Wild
Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs).
IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training
The framework derives multi-level visual features from the chest X-ray (CXR) images and separately aligns these features with the descriptive and the conclusive text encoded in the hierarchical medical report.
Parrot: Enhancing Multi-Turn Chat Models by Learning to Ask Questions
In this paper, we address these challenges by introducing Parrot, a highly scalable solution designed to automatically generate high-quality instruction-tuning data, which are then used to enhance the effectiveness of chat models in multi-turn conversations.
Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images
The advent of text-guided generative models raises a compelling question: Can VLP be implemented solely with synthetic images generated from genuine radiology reports, thereby mitigating the need for extensively pairing and curating image-text datasets?
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training
In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool.
M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization
The proposed method, named Medical vision-language pre-training with Frozen language models and Latent spAce Geometry optimization (M-FLAG), leverages a frozen language model for training stability and efficiency and introduces a novel orthogonality loss to harmonize the latent space geometry.
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation
To address these challenges, we present Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation (GTGM), a framework that extends of VLP to 3D medical images without relying on paired textual descriptions.
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
Med-UniC reaches superior performance across 5 medical image tasks and 10 datasets encompassing over 30 diseases, offering a versatile framework for unifying multi-modal medical data within diverse linguistic communities.
Frozen Language Model Helps ECG Zero-Shot Learning
In downstream classification tasks, METS achieves around 10% improvement in performance without using any annotated data via zero-shot classification, compared to other supervised and SSL baselines that rely on annotated data.
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement
The robust performance of SCDNN provides a new perspective to exploit knowledge across deep learning models from time and spectral domains.
Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings
In this paper, we introduce the task of learning unsupervised dialogue embeddings.
DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings
Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability.
Sequential Sentence Matching Network for Multi-turn Response Selection in Retrieval-based Chatbots
In this work, we propose a matching network, called sequential sentence matching network (S2M), to use the sentence-level semantic information to address the problem.
