✨✨Latest Advances on Multimodal Large Language Models
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Updated
Jun 12, 2024
✨✨Latest Advances on Multimodal Large Language Models
The Paper List of Large Multi-Modality Model, Parameter-Efficient Finetuning, Vision-Language Pretraining, Conventional Image-Text Matching for Preliminary Insight.
[ICML2024 (Oral)] Official PyTorch implementation of DoRA: Weight-Decomposed Low-Rank Adaptation
✨✨Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
[CVPR'24] HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models
🔥🔥🔥 A curated list of papers on LLMs-based multimodal generation (image, video, 3D and audio).
Curated papers on Large Language Models in Healthcare and Medical domain
This repo contains evaluation code for the paper "Are We on the Right Way for Evaluating Large Vision-Language Models"
An official implementation of ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
A curated list of recent and past chart understanding work based on our survey paper: From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models.
Talk2BEV: Language-Enhanced Bird's Eye View Maps (Accepted to ICRA'24)
This is the official repo for Debiasing Large Visual Language Models, including a Post-Hoc debias method and Visual Debias Decoding strategy.
An benchmark for evaluating the capabilities of large vision-language models (LVLMs)
Code and data for the paper "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning"
[ICML 2024] Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models.
An official implementation of ShareGPT4V: Improving Large Multi-modal Models with Better Captions
up-to-date and curated list of awesome state-of-the-art LVLMs hallucinations research work, papers & resources
ShareGPT4Omni: Towards Building Omni Large Multi-modal Models with Comprehensive Multi-modal Annotations
Multi-Agent VQA: Exploring Multi-Agent Foundation Models on Zero-Shot Visual Question Answering
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