Search Results for author:
Found 18 papers, 13 papers with code
ChemMLLM: Chemical Multimodal Large Language Model
To fill this gap, in this paper, we propose ChemMLLM, a unified chemical multimodal large language model for molecule understanding and generation.
Anyprefer: An Agentic Framework for Preference Data Synthesis
High-quality preference data is essential for aligning foundation models with human values through preference learning.
MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding
These agents engage in multi-modal context retrieval, combining their individual insights to achieve a more comprehensive understanding of the document's content.
MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation
Recent advancements in video generation have significantly improved the ability to synthesize videos from text instructions.
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation
Inspired by the density-based clustering hypothesis, we propose using feature density to locate sparse regions within feature clusters.
MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization
Previous attempts to enhance modality alignment in Med-LVLMs through preference optimization have inadequately mitigated clinical relevance in preference data, making these samples easily distinguishable and reducing alignment effectiveness.
MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models
Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning.
MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
Extensive experiments demonstrate the effectiveness of our benchmark and metrics in providing a comprehensive evaluation of interleaved LVLMs.
RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models
Second, in cases where the model originally responds correctly, applying RAG can lead to an over-reliance on retrieved contexts, resulting in incorrect answers.
TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM
Furthermore, we design a prior-aligned injector to inject explicit priors into the segmentation process, which can facilitate knowledge sharing across multi-modality features and allow our framework to be trained in a parameter-efficient fashion.
OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding
With approximately 285 hours of surgical videos, OphNet is about 20 times larger than the largest existing surgical workflow analysis benchmark.
CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models
Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare.
Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations
Subsequently, to improve the robustness of the decoupled representations, class and domain prototypes are employed to interpolate the disentangled representations while data-aware weights are designed to focus on rare classes and domains.
Diffusion Model Driven Test-Time Image Adaptation for Robust Skin Lesion Classification
In this paper, we propose a test-time image adaptation method to enhance the accuracy of the model on test data by simultaneously updating and predicting test images.
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding
We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category.
Fine-Grained Visual Recognition
Graph Representation Learning
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
Long-tailed multi-label visual recognition (LTML) task is a highly challenging task due to the label co-occurrence and imbalanced data distribution.
Ranked #1 on
Long-tail Learning
on COCO-MLT
(using extra training data)
Chinese grammatical error correction based on knowledge distillation
In view of the poor robustness of existing Chinese grammatical error correction models on attack test sets and large model parameters, this paper uses the method of knowledge distillation to compress model parameters and improve the anti-attack ability of the model.
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy.
