[2103.00020] Learning Transferable Visual Models From Natural …
网页2021年2月26日 · View a PDF of the paper titled Learning Transferable Visual Models From Natural Language Supervision, by Alec Radford and 11 other authors. State-of-the-art computer vision systems are trained to predict a fixed set of …
Title: Long-CLIP: Unlocking the Long-Text Capability of CLIP
网页2024年3月22日 · Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities. Despite its widespread adoption, a significant limitation of CLIP lies in the inadequate length of text input.
网页ConVIRT trained from scratch, which we call CLIP, for Con-trastive Language-Image Pre-training, is an efficient method of learning from natural language supervision. We study the scalability of CLIP by training a series of eight models spanning almost 2 orders of magnitude of compute and ob-serve that transfer performance is a smoothly predictable
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
网页2022年11月2日 · In this work, we construct a large-scale dataset of image-text pairs in Chinese, where most data are retrieved from publicly available datasets, and we pretrain Chinese CLIP models on the new dataset. We develop 5 Chinese CLIP models of multiple sizes, spanning from 77 to 958 million parameters.
[2309.16671] Demystifying CLIP Data - arXiv.org
网页2023年9月28日 · View a PDF of the paper titled Demystifying CLIP Data, by Hu Xu and 9 other authors. Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative models.
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
网页2021年10月9日 · View a PDF of the paper titled CLIP-Adapter: Better Vision-Language Models with Feature Adapters, by Peng Gao and 7 other authors. Large-scale contrastive vision-language pre-training has shown significant progress in visual representation learning.
Title: Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
网页2023年12月6日 · Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual modalities to comprehend the entire image, including all the details, even those irrelevant to specific tasks.
Title: EVA-CLIP: Improved Training Techniques for CLIP at Scale
网页2023年3月27日 · Contrastive language-image pre-training, CLIP for short, has gained increasing attention for its potential in various scenarios. In this paper, we propose EVA-CLIP, a series of models that significantly improve the …
[2305.20088] Improving CLIP Training with Language Rewrites
网页2023年5月31日 · In this paper, we introduce Language augmented CLIP (LaCLIP), a simple yet highly effective approach to enhance CLIP training through language rewrites. Leveraging the in-context learning capability of large language models, we rewrite the text descriptions associated with each image.
Hierarchical Text-Conditional Image Generation with CLIP Latents
网页2022年4月13日 · View a PDF of the paper titled Hierarchical Text-Conditional Image Generation with CLIP Latents, by Aditya Ramesh and 4 other authors. Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style.