World-class multilingual and cross-lingual question-answering retrieval.

Cutting-edge vision-language model exceling in high-quality reasoning from images.

Cutting-edge vision-Language model exceling in high-quality reasoning from images.

Cutting-edge open multimodal model exceling in high-quality reasoning from images.

NV-DINOv2 is a visual foundation model that generates vector embeddings for the input image.

Vision foundation model capable of performing diverse computer vision and vision language tasks.

Verify compatibility of OpenUSD assets with instant RTX render and rule-based validation.

GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.

GPU-accelerated generation of text embeddings used for question-answering retrieval.

GPU-accelerated generation of text embeddings used for question-answering retrieval.

Visual Changenet detects pixel-level change maps between two images and outputs a semantic change segmentation mask

Cutting-edge open multimodal model exceling in high-quality reasoning from images.

Vision language model adept at comprehending text and visual inputs to produce informative responses

GPU-accelerated generation of text embeddings used for question-answering retrieval.

GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.

Generate images and stunning visuals with realistic aesthetics.

Groundbreaking multimodal model designed to understand and reason about visual elements in images.

One-shot visual language understanding model that translates images of plots into tables.

Multi-modal vision-language model that understands text/images and generates informative responses