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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters EVA-CLIP-18B demonstrates the potential of EVA-style weak-to-strong visual model scaling With our model weights made publicly available, we hope to facilitate future research in vision and multimodal foundation models
[2103. 00020] Learning Transferable Visual Models From Natural Language . . . State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision We
arXiv. org e-Print archive This paper explores pre-training models for learning state-of-the-art image representations using natural language captions paired with images
Alpha-CLIP: A CLIP Model Focusing on Wherever You Want 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 However, for a finer understanding and controlled editing of images, it becomes crucial to focus on specific
Jina CLIP: Your CLIP Model Is Also Your Text Retriever Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors These models are key to multimodal information retrieval and related tasks However, CLIP models generally underperform in text-only tasks compared to specialized text models This creates inefficiencies for information
[2309. 16671] Demystifying CLIP Data - arXiv. org 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 We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective However, CLIP only provides very limited information about its data and how it has
Long-CLIP: Unlocking the Long-Text Capability of CLIP 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 The length of the text token is restricted to 77, and an empirical study shows the actual
un$^2$CLIP: Improving CLIPs Visual Detail Capturing Ability via . . . Therefore, we propose to invert unCLIP (dubbed un 2 CLIP) to improve the CLIP model In this way, the improved image encoder can gain unCLIP's visual detail capturing ability while preserving its alignment with the original text encoder simultaneously
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese The tremendous success of CLIP (Radford et al , 2021) has promoted the research and application of contrastive learning for vision-language pretraining 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