# High-resolution Image Processing

UniME is a general embedding learning framework based on multimodal large models, significantly enhancing multimodal embedding capabilities through text discriminative knowledge distillation and hard negative sample-enhanced instruction tuning strategies.

UniME is a general embedding learning model based on a multimodal large model, trained with 336×336 image resolution and ranked first on the MMEB leaderboard.

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.

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

AIM-v2 is an efficient image encoder model compatible with the timm framework, suitable for computer vision tasks.