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Collections including paper arxiv:2501.03895

LinFusion: 1 GPU, 1 Minute, 16K Image

Paper • 2409.02097 • Published Sep 3, 2024 • 34
Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion

Paper • 2409.11406 • Published Sep 17, 2024 • 27
Diffusion Models Are Real-Time Game Engines

Paper • 2408.14837 • Published Aug 27, 2024 • 126
Segment Anything with Multiple Modalities

Paper • 2408.09085 • Published Aug 17, 2024 • 22

about 18 hours ago

DocLLM: A layout-aware generative language model for multimodal document understanding

Paper • 2401.00908 • Published Dec 31, 2023 • 190
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training

Paper • 2401.00849 • Published Jan 1, 2024 • 17
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents

Paper • 2311.05437 • Published Nov 9, 2023 • 51
LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing

Paper • 2311.00571 • Published Nov 1, 2023 • 43

Exploring the Potential of Encoder-free Architectures in 3D LMMs

Paper • 2502.09620 • Published Feb 13 • 26
The Evolution of Multimodal Model Architectures

Paper • 2405.17927 • Published May 28, 2024 • 1
What matters when building vision-language models?

Paper • 2405.02246 • Published May 3, 2024 • 103
Efficient Architectures for High Resolution Vision-Language Models

Paper • 2501.02584 • Published Jan 5

Multimodal-Models

LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52

Image-Video General Tasks

Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

Paper • 2501.04001 • Published Jan 7 • 47
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52
An Empirical Study of Autoregressive Pre-training from Videos

Paper • 2501.05453 • Published Jan 9 • 41
MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training

Paper • 2501.07556 • Published Jan 13 • 7

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6, 2024 • 29
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6, 2024 • 14
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7, 2024 • 44
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7, 2024 • 23

The Evolution of Multimodal Model Architectures

Paper • 2405.17927 • Published May 28, 2024 • 1
What matters when building vision-language models?

Paper • 2405.02246 • Published May 3, 2024 • 103
Efficient Architectures for High Resolution Vision-Language Models

Paper • 2501.02584 • Published Jan 5
Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22, 2024 • 133

ML Optimization Papers

FAST: Efficient Action Tokenization for Vision-Language-Action Models

Paper • 2501.09747 • Published Jan 16 • 27
Tensor Product Attention Is All You Need

Paper • 2501.06425 • Published Jan 11 • 90
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training

Paper • 2501.06842 • Published Jan 12 • 16
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52

Multimodal models

Papers on AI models that combine vision and language capabilities.

LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs

Paper • 2501.06186 • Published Jan 10 • 65
Multimodal LLMs Can Reason about Aesthetics in Zero-Shot

Paper • 2501.09012 • Published Jan 15 • 10

LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

Paper • 2501.04001 • Published Jan 7 • 47
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Paper • 2501.04003 • Published Jan 7 • 27
VideoRAG: Retrieval-Augmented Generation over Video Corpus

Paper • 2501.05874 • Published Jan 10 • 75

LinFusion: 1 GPU, 1 Minute, 16K Image

Paper • 2409.02097 • Published Sep 3, 2024 • 34
Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion

Paper • 2409.11406 • Published Sep 17, 2024 • 27
Diffusion Models Are Real-Time Game Engines

Paper • 2408.14837 • Published Aug 27, 2024 • 126
Segment Anything with Multiple Modalities

Paper • 2408.09085 • Published Aug 17, 2024 • 22

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6, 2024 • 29
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6, 2024 • 14
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7, 2024 • 44
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7, 2024 • 23

about 18 hours ago

DocLLM: A layout-aware generative language model for multimodal document understanding

Paper • 2401.00908 • Published Dec 31, 2023 • 190
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training

Paper • 2401.00849 • Published Jan 1, 2024 • 17
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents

Paper • 2311.05437 • Published Nov 9, 2023 • 51
LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing

Paper • 2311.00571 • Published Nov 1, 2023 • 43

The Evolution of Multimodal Model Architectures

Paper • 2405.17927 • Published May 28, 2024 • 1
What matters when building vision-language models?

Paper • 2405.02246 • Published May 3, 2024 • 103
Efficient Architectures for High Resolution Vision-Language Models

Paper • 2501.02584 • Published Jan 5
Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22, 2024 • 133

Exploring the Potential of Encoder-free Architectures in 3D LMMs

Paper • 2502.09620 • Published Feb 13 • 26
The Evolution of Multimodal Model Architectures

Paper • 2405.17927 • Published May 28, 2024 • 1
What matters when building vision-language models?

Paper • 2405.02246 • Published May 3, 2024 • 103
Efficient Architectures for High Resolution Vision-Language Models

Paper • 2501.02584 • Published Jan 5

ML Optimization Papers

FAST: Efficient Action Tokenization for Vision-Language-Action Models

Paper • 2501.09747 • Published Jan 16 • 27
Tensor Product Attention Is All You Need

Paper • 2501.06425 • Published Jan 11 • 90
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training

Paper • 2501.06842 • Published Jan 12 • 16
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52

Multimodal-Models

LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52

Multimodal models

Papers on AI models that combine vision and language capabilities.

LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs

Paper • 2501.06186 • Published Jan 10 • 65
Multimodal LLMs Can Reason about Aesthetics in Zero-Shot

Paper • 2501.09012 • Published Jan 15 • 10

Image-Video General Tasks

Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

Paper • 2501.04001 • Published Jan 7 • 47
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52
An Empirical Study of Autoregressive Pre-training from Videos

Paper • 2501.05453 • Published Jan 9 • 41
MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training

Paper • 2501.07556 • Published Jan 13 • 7

LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Paper • 2501.03895 • Published Jan 7 • 52
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

Paper • 2501.04001 • Published Jan 7 • 47
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Paper • 2501.04003 • Published Jan 7 • 27
VideoRAG: Retrieval-Augmented Generation over Video Corpus

Paper • 2501.05874 • Published Jan 10 • 75

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