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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
Collections
Discover the best community collections!
Collections including paper arxiv:2504.07951
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Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models
Paper • 2504.07951 • Published • 29 -
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability
Paper • 2504.08003 • Published • 49 -
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
Paper • 2504.11468 • Published • 30 -
Towards Learning to Complete Anything in Lidar
Paper • 2504.12264 • Published • 9
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Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
Paper • 2404.08801 • Published • 66 -
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Paper • 2404.07839 • Published • 47 -
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
Paper • 2404.05892 • Published • 39 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 146
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Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 93 -
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
Paper • 2404.10667 • Published • 23 -
Instruction-tuned Language Models are Better Knowledge Learners
Paper • 2402.12847 • Published • 26 -
DoRA: Weight-Decomposed Low-Rank Adaptation
Paper • 2402.09353 • Published • 30
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 28 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
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
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Analyzing The Language of Visual Tokens
Paper • 2411.05001 • Published • 24 -
Large Multi-modal Models Can Interpret Features in Large Multi-modal Models
Paper • 2411.14982 • Published • 19 -
Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration
Paper • 2411.17686 • Published • 20 -
On the Limitations of Vision-Language Models in Understanding Image Transforms
Paper • 2503.09837 • Published • 10
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Compression Represents Intelligence Linearly
Paper • 2404.09937 • Published • 28 -
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
Paper • 2404.06395 • Published • 24 -
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 37 -
Are large language models superhuman chemists?
Paper • 2404.01475 • Published • 19
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BLINK: Multimodal Large Language Models Can See but Not Perceive
Paper • 2404.12390 • Published • 26 -
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
Paper • 2404.12803 • Published • 30 -
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
Paper • 2404.13013 • Published • 31 -
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Paper • 2404.06512 • Published • 30
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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
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 28 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
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
-
Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models
Paper • 2504.07951 • Published • 29 -
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability
Paper • 2504.08003 • Published • 49 -
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
Paper • 2504.11468 • Published • 30 -
Towards Learning to Complete Anything in Lidar
Paper • 2504.12264 • Published • 9
-
Analyzing The Language of Visual Tokens
Paper • 2411.05001 • Published • 24 -
Large Multi-modal Models Can Interpret Features in Large Multi-modal Models
Paper • 2411.14982 • Published • 19 -
Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration
Paper • 2411.17686 • Published • 20 -
On the Limitations of Vision-Language Models in Understanding Image Transforms
Paper • 2503.09837 • Published • 10
-
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
Paper • 2404.08801 • Published • 66 -
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Paper • 2404.07839 • Published • 47 -
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
Paper • 2404.05892 • Published • 39 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 146
-
Compression Represents Intelligence Linearly
Paper • 2404.09937 • Published • 28 -
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
Paper • 2404.06395 • Published • 24 -
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 37 -
Are large language models superhuman chemists?
Paper • 2404.01475 • Published • 19
-
Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 93 -
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
Paper • 2404.10667 • Published • 23 -
Instruction-tuned Language Models are Better Knowledge Learners
Paper • 2402.12847 • Published • 26 -
DoRA: Weight-Decomposed Low-Rank Adaptation
Paper • 2402.09353 • Published • 30
-
BLINK: Multimodal Large Language Models Can See but Not Perceive
Paper • 2404.12390 • Published • 26 -
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
Paper • 2404.12803 • Published • 30 -
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
Paper • 2404.13013 • Published • 31 -
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Paper • 2404.06512 • Published • 30