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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 13 -
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:2506.18851
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Large Language Diffusion Models
Paper • 2502.09992 • Published • 122 -
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment
Paper • 2502.10391 • Published • 35 -
Diverse Inference and Verification for Advanced Reasoning
Paper • 2502.09955 • Published • 18 -
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models
Paper • 2502.08130 • Published • 9
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SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers
Paper • 2407.09413 • Published • 11 -
MAVIS: Mathematical Visual Instruction Tuning
Paper • 2407.08739 • Published • 34 -
Kvasir-VQA: A Text-Image Pair GI Tract Dataset
Paper • 2409.01437 • Published • 72 -
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct
Paper • 2409.05840 • Published • 49
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MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Paper • 2405.07526 • Published • 22 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 18 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 16 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 32
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Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Paper • 2504.01990 • Published • 300 -
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Paper • 2504.10479 • Published • 280 -
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models
Paper • 2503.24235 • Published • 55 -
Seedream 3.0 Technical Report
Paper • 2504.11346 • Published • 66
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StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
Paper • 2411.05738 • Published • 15 -
A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents
Paper • 2410.22476 • Published • 29 -
OS-ATLAS: A Foundation Action Model for Generalist GUI Agents
Paper • 2410.23218 • Published • 51 -
Training-free Regional Prompting for Diffusion Transformers
Paper • 2411.02395 • Published • 26
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 13 -
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
-
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Paper • 2405.07526 • Published • 22 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 18 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 16 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 32
-
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Paper • 2504.01990 • Published • 300 -
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Paper • 2504.10479 • Published • 280 -
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models
Paper • 2503.24235 • Published • 55 -
Seedream 3.0 Technical Report
Paper • 2504.11346 • Published • 66
-
Large Language Diffusion Models
Paper • 2502.09992 • Published • 122 -
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment
Paper • 2502.10391 • Published • 35 -
Diverse Inference and Verification for Advanced Reasoning
Paper • 2502.09955 • Published • 18 -
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models
Paper • 2502.08130 • Published • 9
-
StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
Paper • 2411.05738 • Published • 15 -
A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents
Paper • 2410.22476 • Published • 29 -
OS-ATLAS: A Foundation Action Model for Generalist GUI Agents
Paper • 2410.23218 • Published • 51 -
Training-free Regional Prompting for Diffusion Transformers
Paper • 2411.02395 • Published • 26
-
SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers
Paper • 2407.09413 • Published • 11 -
MAVIS: Mathematical Visual Instruction Tuning
Paper • 2407.08739 • Published • 34 -
Kvasir-VQA: A Text-Image Pair GI Tract Dataset
Paper • 2409.01437 • Published • 72 -
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct
Paper • 2409.05840 • Published • 49