Visual Instruction Tuning
Paper
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2304.08485
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Published
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13
Vision-Language Model
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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 • 37 -
SILC: Improving Vision Language Pretraining with Self-Distillation
Paper • 2310.13355 • Published • 6 -
CogVLM: Visual Expert for Pretrained Language Models
Paper • 2311.03079 • Published • 23 -
ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
Paper • 2311.12793 • Published • 18 -
DeepSeek-VL: Towards Real-World Vision-Language Understanding
Paper • 2403.05525 • Published • 39 -
OmniFusion Technical Report
Paper • 2404.06212 • Published • 74 -
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
Paper • 2404.12387 • Published • 38 -
Pegasus-v1 Technical Report
Paper • 2404.14687 • Published • 30 -
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Paper • 2404.16821 • Published • 53 -
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
Paper • 2404.16994 • Published • 35 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 98 -
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
Paper • 2407.03320 • Published • 92 -
Understanding Alignment in Multimodal LLMs: A Comprehensive Study
Paper • 2407.02477 • Published • 21 -
μ-Bench: A Vision-Language Benchmark for Microscopy Understanding
Paper • 2407.01791 • Published • 5