# Image-Text Retrieval

SigLIP 2 is an improved model based on the SigLIP pre-training objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a multilingual vision-language encoder that integrates SigLIP's pretraining objectives and introduces new training schemes, enhancing semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a vision-language model based on SigLIP, enhanced with improved semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved model based on the SigLIP pre-training objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved model based on SigLIP, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved model based on the SigLIP pretraining objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an advanced vision-language model that integrates multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved multilingual vision-language encoder based on SigLIP, enhancing semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved vision-language model based on SigLIP, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a vision-language model that integrates multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a vision-language model based on SigLIP, enhancing semantic understanding, localization, and dense feature extraction through a unified training approach.

SigLIP 2 is a multilingual vision-language encoder with improved semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved multilingual vision-language encoder based on SigLIP, enhancing semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved version of SigLIP, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

TiC-CLIP is an improved vision-language model based on OpenCLIP, focusing on continual temporal learning, with training data spanning from 2014 to 2022

A Japanese CLIP model developed by LY Corporation, trained on approximately 1 billion web-collected image-text pairs, suitable for various vision tasks.

A Japanese version of the CLIP model that maps Japanese text and images into the same embedding space, suitable for zero-shot image classification, text-image retrieval, and other tasks.

ALIGN is a vision-language dual-encoder model that aligns image and text representations through contrastive learning, achieving state-of-the-art cross-modal representation with large-scale noisy data.