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Collections including paper arxiv:2408.12528

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 • 118
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Paper • 2408.12528 • Published Aug 22 • 50

Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Paper • 2408.12528 • Published Aug 22 • 50

Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Paper • 2408.12528 • Published Aug 22 • 50

Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Paper • 2408.12528 • Published Aug 22 • 50

LongVILA: Scaling Long-Context Visual Language Models for Long Videos

Paper • 2408.10188 • Published Aug 19 • 51
xGen-MM (BLIP-3): A Family of Open Large Multimodal Models

Paper • 2408.08872 • Published Aug 16 • 97
Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22 • 118
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Paper • 2408.12528 • Published Aug 22 • 50

multimodal interesting

MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data

Paper • 2406.18790 • Published Jun 26 • 33
OmniGen: Unified Image Generation

Paper • 2409.11340 • Published Sep 17 • 107
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Paper • 2408.12528 • Published Aug 22 • 50
MonoFormer/MonoFormer_ImageNet_256

Updated Sep 25 • 17 • 4

iVideoGPT: Interactive VideoGPTs are Scalable World Models

Paper • 2405.15223 • Published May 24 • 12
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models

Paper • 2405.15574 • Published May 24 • 53
An Introduction to Vision-Language Modeling

Paper • 2405.17247 • Published May 27 • 85
Matryoshka Multimodal Models

Paper • 2405.17430 • Published May 27 • 31

LM Architectures

Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Paper • 2404.08801 • Published Apr 12 • 63
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Paper • 2404.07839 • Published Apr 11 • 42
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence

Paper • 2404.05892 • Published Apr 8 • 31
Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 138

Vision Language Models

BLINK: Multimodal Large Language Models Can See but Not Perceive

Paper • 2404.12390 • Published Apr 18 • 24
TextSquare: Scaling up Text-Centric Visual Instruction Tuning

Paper • 2404.12803 • Published Apr 19 • 29
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models

Paper • 2404.13013 • Published Apr 19 • 30
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD

Paper • 2404.06512 • Published Apr 9 • 29

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