Nemotron ColEmbed V2: Raising the Bar for Multimodal Retrieval with ViDoRe V3’s Top Model

The Nemotron ColEmbed V2 series from NVIDIA represents a significant advancement in multimodal retrieval, designed to address the challenges of processing heterogeneous document images that include text, tables, charts, and other visual elements. Built on enhanced vision-language models, these late-interaction embedding architectures allow for detailed semantic relationships through multi-vector interactions, improving accuracy in retrieving relevant information from complex documents. The series includes models of varying sizes—3B, 4B, and 8B—which excel on the ViDoRe V3 benchmark, a standard for industry-level visual document retrieval, by employing bi-directional self-attention and advanced training methodologies using multilingual synthetic data. These models, available on platforms like Hugging Face, aim to support researchers and developers in creating high-accuracy multimodal retrieval systems applicable to multimedia search engines, cross-modal retrieval systems, and conversational AI, offering a robust foundation for exploring state-of-the-art technologies in enterprise settings.