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LinFusion: 1 GPU, 1 Minute, 16K Image
Paper • 2409.02097 • Published • 34 -
Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion
Paper • 2409.11406 • Published • 27 -
Diffusion Models Are Real-Time Game Engines
Paper • 2408.14837 • Published • 126 -
Segment Anything with Multiple Modalities
Paper • 2408.09085 • Published • 22
Collections
Discover the best community collections!
Collections including paper arxiv:2501.03895
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The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 103 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 133
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FAST: Efficient Action Tokenization for Vision-Language-Action Models
Paper • 2501.09747 • Published • 28 -
Tensor Product Attention Is All You Need
Paper • 2501.06425 • Published • 90 -
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training
Paper • 2501.06842 • Published • 16 -
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Paper • 2501.03895 • Published • 52
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LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Paper • 2501.03895 • Published • 52 -
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs
Paper • 2501.06186 • Published • 65 -
Multimodal LLMs Can Reason about Aesthetics in Zero-Shot
Paper • 2501.09012 • Published • 10
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LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Paper • 2501.03895 • Published • 52 -
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
Paper • 2501.04001 • Published • 47 -
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives
Paper • 2501.04003 • Published • 27 -
VideoRAG: Retrieval-Augmented Generation over Video Corpus
Paper • 2501.05874 • Published • 75
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LLM Pruning and Distillation in Practice: The Minitron Approach
Paper • 2408.11796 • Published • 58 -
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
Paper • 2408.09174 • Published • 52 -
To Code, or Not To Code? Exploring Impact of Code in Pre-training
Paper • 2408.10914 • Published • 45 -
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Paper • 2408.11878 • Published • 63
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Exploring the Potential of Encoder-free Architectures in 3D LMMs
Paper • 2502.09620 • Published • 26 -
The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 103 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published
-
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
Paper • 2501.04001 • Published • 47 -
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Paper • 2501.03895 • Published • 52 -
An Empirical Study of Autoregressive Pre-training from Videos
Paper • 2501.05453 • Published • 41 -
MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training
Paper • 2501.07556 • Published • 7
-
LinFusion: 1 GPU, 1 Minute, 16K Image
Paper • 2409.02097 • Published • 34 -
Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion
Paper • 2409.11406 • Published • 27 -
Diffusion Models Are Real-Time Game Engines
Paper • 2408.14837 • Published • 126 -
Segment Anything with Multiple Modalities
Paper • 2408.09085 • Published • 22
-
LLM Pruning and Distillation in Practice: The Minitron Approach
Paper • 2408.11796 • Published • 58 -
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
Paper • 2408.09174 • Published • 52 -
To Code, or Not To Code? Exploring Impact of Code in Pre-training
Paper • 2408.10914 • Published • 45 -
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Paper • 2408.11878 • Published • 63
-
The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 103 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 133
-
Exploring the Potential of Encoder-free Architectures in 3D LMMs
Paper • 2502.09620 • Published • 26 -
The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 103 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published
-
FAST: Efficient Action Tokenization for Vision-Language-Action Models
Paper • 2501.09747 • Published • 28 -
Tensor Product Attention Is All You Need
Paper • 2501.06425 • Published • 90 -
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training
Paper • 2501.06842 • Published • 16 -
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Paper • 2501.03895 • Published • 52
-
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Paper • 2501.03895 • Published • 52 -
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs
Paper • 2501.06186 • Published • 65 -
Multimodal LLMs Can Reason about Aesthetics in Zero-Shot
Paper • 2501.09012 • Published • 10
-
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
Paper • 2501.04001 • Published • 47 -
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Paper • 2501.03895 • Published • 52 -
An Empirical Study of Autoregressive Pre-training from Videos
Paper • 2501.05453 • Published • 41 -
MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training
Paper • 2501.07556 • Published • 7
-
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
Paper • 2501.03895 • Published • 52 -
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
Paper • 2501.04001 • Published • 47 -
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives
Paper • 2501.04003 • Published • 27 -
VideoRAG: Retrieval-Augmented Generation over Video Corpus
Paper • 2501.05874 • Published • 75