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Found 17 papers, 4 papers with code
MedXChat: Bridging CXR Modalities with a Unified Multimodal Large Model
Despite the success of Large Language Models (LLMs) in general image tasks, a gap persists in the medical field for a multimodal large model adept at handling the nuanced diversity of medical images.
Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models
The advancement of Large Language Models (LLMs) has brought substantial attention to the Chain of Thought (CoT) approach, primarily due to its ability to enhance the capability of LLMs on complex reasoning tasks.
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation
While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of input-side multimodal comprehension, lacking the capacity for multimodal content generation.
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis
This work conducts an evaluation of GPT-4V's multimodal capability for medical image analysis, with a focus on three representative tasks of radiology report generation, medical visual question answering, and medical visual grounding.
R2GenGPT: Radiology Report Generation with Frozen LLMs
First, it attains state-of-the-art (SOTA) performance by training only the lightweight visual alignment module while freezing all the parameters of LLM.
METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens
In the encoder, each expert token interacts with both vision tokens and other expert tokens to learn to attend different image regions for image representation.
Q2ATransformer: Improving Medical VQA via an Answer Querying Decoder
To bridge this gap, in this paper, we propose a new Transformer based framework for medical VQA (named as Q2ATransformer), which integrates the advantages of both the classification and the generation approaches and provides a unified treatment for the close-end and open-end questions.
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
Differentially private (DP) mechanisms protect individual-level information by introducing randomness into the statistical analysis procedure.
MICO: Selective Search with Mutual Information Co-training
In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups.
A Medical Semantic-Assisted Transformer for Radiographic Report Generation
Automated radiographic report generation is a challenging cross-domain task that aims to automatically generate accurate and semantic-coherence reports to describe medical images.
CLIP4Caption: CLIP for Video Caption
It is noted that our model is only trained on the MSR-VTT dataset.
CLIP4Caption ++: Multi-CLIP for Video Caption
We make the following improvements on the proposed CLIP4Caption++: We employ an advanced encoder-decoder model architecture X-Transformer as our main framework and make the following improvements: 1) we utilize three strong pre-trained CLIP models to extract the text-related appearance visual features.
A Self-Boosting Framework for Automated Radiographic Report Generation
On one hand, the image-text matching branch helps to learn highly text-correlated visual features for the report generation branch to output high quality reports.
Variance Reduction on General Adaptive Stochastic Mirror Descent
In this work, we investigate the idea of variance reduction by studying its properties with general adaptive mirror descent algorithms in nonsmooth nonconvex finite-sum optimization problems.
Online Regularization towards Always-Valid High-Dimensional Dynamic Pricing
In this paper, we propose a novel approach for designing dynamic pricing policy based regularized online statistical learning with theoretical guarantees.
Directional Pruning of Deep Neural Networks
In the light of the fact that the stochastic gradient descent (SGD) often finds a flat minimum valley in the training loss, we propose a novel directional pruning method which searches for a sparse minimizer in or close to that flat region.
The Sample Complexity of Meta Sparse Regression
A key difference between meta-learning and the classical multi-task learning, is that meta-learning focuses only on the recovery of the parameters of the novel task, while multi-task learning estimates the parameter of all tasks, which requires l to grow with T .
