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Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
Fine-tuned reranking model for multilingual, cross-lingual text question-answering retrieval, with long context support.
Enhance and modify high-quality compositions using real-time rendering and generative AI output without affecting a hero product asset.
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.
English text embedding model for question-answering retrieval.
Multilingual text question-answering retrieval, transforming textual information into dense vector representations.
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.
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.
