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Long-CLIP: Unlocking the Long-Text Capability of CLIP To this end, we propose Long-CLIP as a plug-and-play alternative to CLIP that supports long-text input, retains or even surpasses its zero-shot generalizability, and aligns the CLIP latent space, making it readily replace CLIP without any further adaptation in downstream frameworks
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
[2411. 04997] LLM2CLIP: Powerful Language Model Unlocks Richer Visual . . . Motivated by the remarkable advancements in large language models (LLMs), this work explores how LLMs' superior text understanding and extensive open-world knowledge can enhance CLIP's capability, especially for processing longer and more complex image captions
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters Scaling up contrastive language-image pretraining (CLIP) is critical for empowering both vision and multimodal models We present EVA-CLIP-18B, the largest and most powerful open-source CLIP model to date, with 18-billion parameters
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese 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