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Collections including paper arxiv:2405.02246

Multi-Modal Model

What matters when building vision-language models?

Paper • 2405.02246 • Published May 3 • 98
MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data

Paper • 2406.18790 • Published Jun 26 • 33
Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published 28 days ago • 109
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Paper • 2408.12528 • Published 28 days ago • 50

Vision-language model (VLMs)

What matters when building vision-language models?

Paper • 2405.02246 • Published May 3 • 98
An Introduction to Vision-Language Modeling

Paper • 2405.17247 • Published May 27 • 84
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output

Paper • 2407.03320 • Published Jul 3 • 92
Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published 28 days ago • 109

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Paper • 2406.06525 • Published Jun 10 • 64
Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning

Paper • 2406.06469 • Published Jun 10 • 23
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Paper • 2406.04271 • Published Jun 6 • 27
Block Transformer: Global-to-Local Language Modeling for Fast Inference

Paper • 2406.02657 • Published Jun 4 • 36

What matters when building vision-language models?

Paper • 2405.02246 • Published May 3 • 98

What matters when building vision-language models?

Paper • 2405.02246 • Published May 3 • 98

What matters when building vision-language models?

Paper • 2405.02246 • Published May 3 • 98
An Introduction to Vision-Language Modeling

Paper • 2405.17247 • Published May 27 • 84
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark

Paper • 2405.19707 • Published May 30 • 4

MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training

Paper • 2311.17049 • Published Nov 28, 2023
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Paper • 2405.04434 • Published May 7 • 13
A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision

Paper • 2303.17376 • Published Mar 30, 2023
Sigmoid Loss for Language Image Pre-Training

Paper • 2303.15343 • Published Mar 27, 2023 • 4

image llm works

Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation

Paper • 2404.19752 • Published Apr 30 • 22
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Paper • 2404.16821 • Published Apr 25 • 53
MoAI: Mixture of All Intelligence for Large Language and Vision Models

Paper • 2403.07508 • Published Mar 12 • 75
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Paper • 2403.09611 • Published Mar 14 • 123

What matters when building vision-language models?

Paper • 2405.02246 • Published May 3 • 98

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 83
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time

Paper • 2404.10667 • Published Apr 16 • 15
Instruction-tuned Language Models are Better Knowledge Learners

Paper • 2402.12847 • Published Feb 20 • 24
DoRA: Weight-Decomposed Low-Rank Adaptation

Paper • 2402.09353 • Published Feb 14 • 24

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