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Collections including paper arxiv:2405.02246

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6 • 25
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6 • 12
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7 • 38
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7 • 19

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 Aug 22 • 116
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Paper • 2408.12528 • Published Aug 22 • 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 • 85
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 Aug 22 • 116

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 • 85
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark

Paper • 2405.19707 • Published May 30 • 4
Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations

Paper • 2410.08049 • Published about 1 month ago • 8

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 • 124

What matters when building vision-language models?

Paper • 2405.02246 • Published May 3 • 98

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