Improved Baselines with Visual Instruction Tuning
Paper
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2310.03744
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Published
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39
Vision Language Models Papers 🖼️💬📝
Papers about vision-language models, most important ones are on top of the list.
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DeepSeek-VL: Towards Real-World Vision-Language Understanding
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Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities
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LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model
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MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
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Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
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Kosmos-2: Grounding Multimodal Large Language Models to the World
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CogVLM: Visual Expert for Pretrained Language Models
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MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens
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MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
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Visual Instruction Tuning
Paper • 2304.08485 • Published • 20 -
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning
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TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal Large Language Models
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BLINK: Multimodal Large Language Models Can See but Not Perceive
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LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images
Paper • 2403.11703 • Published • 17 -
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
Paper • 2404.13013 • Published • 31 -
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
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HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models
Paper • 2310.14566 • Published • 27 -
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
Paper • 2306.17107 • Published • 11 -
LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing
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MyVLM: Personalizing VLMs for User-Specific Queries
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To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Paper • 2311.07574 • Published • 16 -
SILC: Improving Vision Language Pretraining with Self-Distillation
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Woodpecker: Hallucination Correction for Multimodal Large Language Models
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SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
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SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
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PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
Paper • 2404.16994 • Published • 37