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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 25 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 12 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 38 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 19
Collections
Discover the best community collections!
Collections including paper arxiv:2406.17720
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SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers
Paper • 2407.09413 • Published • 9 -
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Paper • 2406.20094 • Published • 94 -
Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity
Paper • 2406.17720 • Published • 7 -
LiveBench: A Challenging, Contamination-Free LLM Benchmark
Paper • 2406.19314 • Published • 19
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The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Paper • 2406.17557 • Published • 86 -
Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs
Paper • 2406.16860 • Published • 57 -
Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity
Paper • 2406.17720 • Published • 7 -
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Paper • 2406.20094 • Published • 94
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iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 12 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 53 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 85 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 30
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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 • 17 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 13 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 13 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 30
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Demystifying CLIP Data
Paper • 2309.16671 • Published • 20 -
Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 10 -
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Paper • 2404.01367 • Published • 20 -
On the Scalability of Diffusion-based Text-to-Image Generation
Paper • 2404.02883 • Published • 17
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FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation
Paper • 2403.06775 • Published • 3 -
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Paper • 2010.11929 • Published • 6 -
Data Incubation -- Synthesizing Missing Data for Handwriting Recognition
Paper • 2110.07040 • Published • 2 -
A Mixture of Expert Approach for Low-Cost Customization of Deep Neural Networks
Paper • 1811.00056 • Published • 2