# Vision-Text Alignment

Ovis2-1B is the latest member of the Ovis series of multimodal large language models (MLLM), focusing on structural alignment of vision and text embeddings, featuring high performance for small models, enhanced reasoning capabilities, video and multi-image processing, and multilingual OCR enhancement.

SigLIP-based vision model that enhances maximum resolution to 980x980 through interpolated positional embeddings and implements NaViT strategy for variable resolution and aspect ratio-preserving image processing

Chinese CLIP model, based on VIT architecture, supports Chinese vision-language tasks

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

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.