✨✨Latest Advances on Multimodal Large Language Models
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Updated
Dec 4, 2024
✨✨Latest Advances on Multimodal Large Language Models
[NeurIPS 2024] An official implementation of ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
[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
The Paper List of Large Multi-Modality Model, Parameter-Efficient Finetuning, Vision-Language Pretraining, Conventional Image-Text Matching for Preliminary Insight.
🔥🔥🔥 A curated list of papers on LLMs-based multimodal generation (image, video, 3D and audio).
[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
Curated papers on Large Language Models in Healthcare and Medical domain
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.
[ECCV 2024] ShareGPT4V: Improving Large Multi-modal Models with Better Captions
[NeurIPS 2024] This repo contains evaluation code for the paper "Are We on the Right Way for Evaluating Large Vision-Language Models"
Talk2BEV: Language-Enhanced Bird's Eye View Maps (ICRA'24)
This is the official repo for Debiasing Large Visual Language Models, including a Post-Hoc debias method and Visual Debias Decoding strategy.
[ECCV 2024] API: Attention Prompting on Image for Large Vision-Language Models
[ICML 2024] Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models.
up-to-date curated list of state-of-the-art Large vision language models hallucinations research work, papers & resources
An benchmark for evaluating the capabilities of large vision-language models (LVLMs)
Official Repository of Multi-Object Hallucination in Vision-Language Models (NeurIPS 2024)
Code and data for the ACL 2024 Findings paper "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning"
This repository is the codebase of TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy
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