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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
[2411. 04997] LLM2CLIP: Powerful Language Model Unlocks Richer Visual . . . CLIP is a foundational multimodal model that aligns image and text features into a shared representation space via contrastive learning on large-scale image-text pairs Its effectiveness primarily stems from the use of natural language as rich supervision
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 To fulfill the requirements, we introduce Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs
[2309. 16671] Demystifying CLIP Data - arXiv. org MetaCLIP takes a raw data pool and metadata (derived from CLIP's concepts) and yields a balanced subset over the metadata distribution Our experimental study rigorously isolates the model and training settings, concentrating solely on data
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