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Collections including paper arxiv:2403.09611

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

MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Paper • 2403.09611 • Published Mar 14 • 124

Multimodal LLMs

Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22 • 116
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Paper • 2408.11039 • Published Aug 20 • 56
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming

Paper • 2408.16725 • Published Aug 29 • 52
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Paper • 2408.15998 • Published Aug 28 • 83

Multimodal Language Model

What does matter besides data receipt when training a Multimodal language model?

LLaVA-OneVision: Easy Visual Task Transfer

Paper • 2408.03326 • Published Aug 6 • 59
VILA^2: VILA Augmented VILA

Paper • 2407.17453 • Published Jul 24 • 38
PaliGemma: A versatile 3B VLM for transfer

Paper • 2407.07726 • Published Jul 10 • 66
openbmb/MiniCPM-V-2_6

Image-Text-to-Text • Updated 25 days ago • 135k • 799

Perception and abstraction. Each modality is tokenized and embedded into vectors for model to comprehend.

VILA^2: VILA Augmented VILA

Paper • 2407.17453 • Published Jul 24 • 38
Octopus v4: Graph of language models

Paper • 2404.19296 • Published Apr 30 • 117
Octo-planner: On-device Language Model for Planner-Action Agents

Paper • 2406.18082 • Published Jun 26 • 47
Dolphin: Long Context as a New Modality for Energy-Efficient On-Device Language Models

Paper • 2408.15518 • Published Aug 28 • 42

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

MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Paper • 2402.14905 • Published Feb 22 • 126
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 602
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Paper • 2403.09611 • Published Mar 14 • 124
Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28 • 104

MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Paper • 2403.09611 • Published Mar 14 • 124

Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28 • 104
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Paper • 2403.09611 • Published Mar 14 • 124

Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28 • 104
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 39
ViTAR: Vision Transformer with Any Resolution

Paper • 2403.18361 • Published Mar 27 • 52
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Paper • 2403.18814 • Published Mar 27 • 44

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