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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 30 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
Collections
Discover the best community collections!
Collections including paper arxiv:2412.10360
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iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 17 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 55 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 90 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 34
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MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 303 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 290 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 164 -
Apollo: An Exploration of Video Understanding in Large Multimodal Models
Paper • 2412.10360 • Published • 147
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ProcessBench: Identifying Process Errors in Mathematical Reasoning
Paper • 2412.06559 • Published • 86 -
Maya: An Instruction Finetuned Multilingual Multimodal Model
Paper • 2412.07112 • Published • 28 -
OpenAI o1 System Card
Paper • 2412.16720 • Published • 37 -
Diving into Self-Evolving Training for Multimodal Reasoning
Paper • 2412.17451 • Published • 42
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RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
Paper • 2412.14922 • Published • 88 -
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
Paper • 2412.17256 • Published • 47 -
OpenAI o1 System Card
Paper • 2412.16720 • Published • 37 -
Revisiting In-Context Learning with Long Context Language Models
Paper • 2412.16926 • Published • 32
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DocLLM: A layout-aware generative language model for multimodal document understanding
Paper • 2401.00908 • Published • 191 -
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training
Paper • 2401.00849 • Published • 17 -
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
Paper • 2311.05437 • Published • 51 -
LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing
Paper • 2311.00571 • Published • 42
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AURA: Always-On Understanding and Real-Time Assistance via Video Streams
Paper • 2604.04184 • Published • 50 -
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
Paper • 2403.09626 • Published • 15 -
ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
Paper • 2506.01300 • Published -
Mobile-VideoGPT: Fast and Accurate Video Understanding Language Model
Paper • 2503.21782 • Published
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Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 164 -
Qwen2.5 Technical Report
Paper • 2412.15115 • Published • 379 -
Are Your LLMs Capable of Stable Reasoning?
Paper • 2412.13147 • Published • 93 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 108
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Flowing from Words to Pixels: A Framework for Cross-Modality Evolution
Paper • 2412.15213 • Published • 28 -
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 43 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 164 -
Autoregressive Video Generation without Vector Quantization
Paper • 2412.14169 • Published • 15
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 30 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
-
DocLLM: A layout-aware generative language model for multimodal document understanding
Paper • 2401.00908 • Published • 191 -
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training
Paper • 2401.00849 • Published • 17 -
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
Paper • 2311.05437 • Published • 51 -
LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing
Paper • 2311.00571 • Published • 42
-
iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 17 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 55 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 90 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 34
-
AURA: Always-On Understanding and Real-Time Assistance via Video Streams
Paper • 2604.04184 • Published • 50 -
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
Paper • 2403.09626 • Published • 15 -
ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
Paper • 2506.01300 • Published -
Mobile-VideoGPT: Fast and Accurate Video Understanding Language Model
Paper • 2503.21782 • Published
-
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 303 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 290 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 164 -
Apollo: An Exploration of Video Understanding in Large Multimodal Models
Paper • 2412.10360 • Published • 147
-
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 164 -
Qwen2.5 Technical Report
Paper • 2412.15115 • Published • 379 -
Are Your LLMs Capable of Stable Reasoning?
Paper • 2412.13147 • Published • 93 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 108
-
ProcessBench: Identifying Process Errors in Mathematical Reasoning
Paper • 2412.06559 • Published • 86 -
Maya: An Instruction Finetuned Multilingual Multimodal Model
Paper • 2412.07112 • Published • 28 -
OpenAI o1 System Card
Paper • 2412.16720 • Published • 37 -
Diving into Self-Evolving Training for Multimodal Reasoning
Paper • 2412.17451 • Published • 42
-
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
Paper • 2412.14922 • Published • 88 -
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
Paper • 2412.17256 • Published • 47 -
OpenAI o1 System Card
Paper • 2412.16720 • Published • 37 -
Revisiting In-Context Learning with Long Context Language Models
Paper • 2412.16926 • Published • 32
-
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution
Paper • 2412.15213 • Published • 28 -
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 43 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 164 -
Autoregressive Video Generation without Vector Quantization
Paper • 2412.14169 • Published • 15