Demystifying CLIP Data
Paper
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2309.16671
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Published
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17
Papers - Image - Clip
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Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 9 -
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Paper • 2404.01367 • Published • 19 -
On the Scalability of Diffusion-based Text-to-Image Generation
Paper • 2404.02883 • Published • 17 -
Learning Transferable Visual Models From Natural Language Supervision
Paper • 2103.00020 • Published • 7 -
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion
Paper • 2310.03502 • Published • 74 -
Transferable and Principled Efficiency for Open-Vocabulary Segmentation
Paper • 2404.07448 • Published • 8 -
Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models
Paper • 2404.07973 • Published • 28 -
RegionGPT: Towards Region Understanding Vision Language Model
Paper • 2403.02330 • Published • 2 -
On Speculative Decoding for Multimodal Large Language Models
Paper • 2404.08856 • Published • 9 -
GLIGEN: Open-Set Grounded Text-to-Image Generation
Paper • 2301.07093 • Published • 3 -
A Multimodal Automated Interpretability Agent
Paper • 2404.14394 • Published • 19 -
MultiBooth: Towards Generating All Your Concepts in an Image from Text
Paper • 2404.14239 • Published • 7 -
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data
Paper • 2404.15653 • Published • 24 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 11 -
DOCCI: Descriptions of Connected and Contrasting Images
Paper • 2404.19753 • Published • 5 -
InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation
Paper • 2404.19427 • Published • 60