EVEv2: Improved Baselines for Encoder-Free Vision-Language Models
Paper
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2502.06788
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Published
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12
MLLM
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Scaling Pre-training to One Hundred Billion Data for Vision Language Models
Paper • 2502.07617 • Published • 29 -
VideoRoPE: What Makes for Good Video Rotary Position Embedding?
Paper • 2502.05173 • Published • 65 -
Qwen2.5-VL Technical Report
Paper • 2502.13923 • Published • 181 -
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
Paper • 2502.14786 • Published • 142 -
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Paper • 2501.03895 • Published • 53 -
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model
Paper • 2501.12368 • Published • 46 -
VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
Paper • 2501.13106 • Published • 91 -
Baichuan-Omni-1.5 Technical Report
Paper • 2501.15368 • Published • 64 -
Token-Efficient Long Video Understanding for Multimodal LLMs
Paper • 2503.04130 • Published • 93 -
Qwen2.5-Omni Technical Report
Paper • 2503.20215 • Published • 138 -
Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources
Paper • 2504.00595 • Published • 34 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 161 -
Kimi-VL Technical Report
Paper • 2504.07491 • Published • 113