# Multimodal Contrastive Learning

The Perception Encoder is a state-of-the-art image and video understanding encoder trained through simple vision-language learning, achieving top performance across various visual tasks.

The Perception Encoder (PE) is a state-of-the-art image and video understanding encoder trained through simple vision-language learning, achieving top performance across various visual tasks.

A large-scale visual encoder model developed by Meta, achieving state-of-the-art performance in various vision tasks through contrastive pre-training and fine-tuning on synthetic video data

A vision-language model fine-tuned from openai/clip-vit-large-patch14, trained for 10 epochs

Git-RSCLIP is a vision-language model pretrained on the Git-10M dataset, specializing in multimodal understanding of remote sensing images.

A SigLIP 2 vision-language model trained on the WebLI dataset, suitable for zero-shot image classification tasks.

EVA02 CLIP is a large-scale vision-language model based on the CLIP architecture, supporting tasks such as zero-shot image classification.

CLIP model based on ViT-L/14 and masked self-attention Transformer for zero-shot image classification research

CLIP model based on the ResNet50x64 architecture from the OpenCLIP library, supporting zero-shot image classification tasks.

FashionCLIP is a vision-language model based on CLIP, specifically fine-tuned for the fashion domain, capable of generating universal fashion product representations.

This is a vision-language model based on the CLIP architecture, specifically post-trained on 80 million face images.

CLIP model developed by OpenAI, based on Vision Transformer architecture, supporting joint understanding of images and text

A zero-shot image classification model based on the CLIP architecture, trained using the DataComp dataset, supporting efficient image-text matching tasks.

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks, trained on the laion-s1B-b8K dataset

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks, trained on the DataComp.M dataset

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks.

Zero-shot image classification model based on CLIP architecture, supporting general vision-language tasks

A zero-shot image classification model based on the CLIP architecture, trained on the DataComp dataset, supporting various vision tasks.

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

A vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks

Large-scale vision-language model based on EVA02 architecture, supporting zero-shot image classification 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.

FashionCLIP is a vision-language model fine-tuned specifically for the fashion domain based on CLIP, capable of generating universal product representations.

AltCLIP is a simple and efficient bilingual CLIP model supporting Chinese-English text-image representation tasks.

CLIP is a vision-language model developed by OpenAI, trained via contrastive learning for image and text encoders, supporting zero-shot image classification.

CXR-BERT is a specialized language model developed for the chest X-ray domain, optimized for radiology text processing through improved vocabulary and pretraining procedures

A remote sensing image-specific model fine-tuned based on OpenAI CLIP, enhancing zero-shot classification and image retrieval capabilities

CLIP is a multimodal model developed by OpenAI that maps images and text into a shared embedding space through contrastive learning, enabling zero-shot image classification capabilities.

A remote sensing image-specific model fine-tuned based on OpenAI CLIP, enhancing zero-shot classification and cross-modal retrieval capabilities