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OpenGVLab/InternVL-Chat-V1-5
Visual Question Answering • Updated • 34.7k • 299 -
OpenGVLab/InternVL-Chat-V1-5-Int8
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OpenGVLab/Mini-InternVL-Chat-2B-V1-5
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OpenGVLab/Mini-InternVL-Chat-4B-V1-5
Visual Question Answering • Updated • 2
Collections
Discover the best community collections!
Collections including paper arxiv:2404.16821
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 21 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 9 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 31 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 19
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Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation
Paper • 2404.19752 • Published • 18 -
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Paper • 2404.16821 • Published • 49 -
MoAI: Mixture of All Intelligence for Large Language and Vision Models
Paper • 2403.07508 • Published • 71 -
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Paper • 2403.09611 • Published • 119
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How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Paper • 2404.16821 • Published • 49 -
Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings
Paper • 2404.16820 • Published • 15 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 11
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BLINK: Multimodal Large Language Models Can See but Not Perceive
Paper • 2404.12390 • Published • 23 -
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
Paper • 2404.12803 • Published • 27 -
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
Paper • 2404.13013 • Published • 26 -
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Paper • 2404.06512 • Published • 29
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Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation
Paper • 2403.16990 • Published • 24 -
ViTAR: Vision Transformer with Any Resolution
Paper • 2403.18361 • Published • 48 -
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Paper • 2404.01197 • Published • 29 -
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Paper • 2404.01367 • Published • 19
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Visual Instruction Tuning
Paper • 2304.08485 • Published • 8 -
Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities
Paper • 2308.12966 • Published • 6 -
Improved Baselines with Visual Instruction Tuning
Paper • 2310.03744 • Published • 32 -
SILC: Improving Vision Language Pretraining with Self-Distillation
Paper • 2310.13355 • Published • 5