9 citations · 14 across the 6 of their papers we have counts for
6 papers
Omni-Embed-Nemotron: A Unified Multimodal Retrieval Model for Text, Image, Audio, and Video
Mengyao Xu, Wenfei Zhou, Yauhen Babakhin +6
We present Omni-Embed-Nemotron, a unified multimodal retrieval embedding model developed to handle the increasing complexity of real-world information needs. While Retrieval-Augmen…
Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Model
Mengyao Xu, Gabriel Moreira, Ronay Ak +5
Motivated by the growing demand for retrieval systems that operate across modalities, we introduce llama-nemoretriever-colembed, a unified text-image retrieval model that delivers…
MIRACL-VISION: A Large, multilingual, visual document retrieval benchmark
Radek Osmulski, Gabriel de Souza P. Moreira, Ronay Ak +3
Document retrieval is an important task for search and Retrieval-Augmented Generation (RAG) applications. Large Language Models (LLMs) have contributed to improving the accuracy of…
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG
Gabriel de Souza P. Moreira, Ronay Ak, Benedikt Schifferer +3
Ranking models play a crucial role in enhancing overall accuracy of text retrieval systems. These multi-stage systems typically utilize either dense embedding models or sparse lexi…
NV-Retriever: Improving text embedding models with effective hard-negative mining
Gabriel de Souza P. Moreira, Radek Osmulski, Mengyao Xu +3
Text embedding models have been popular for information retrieval applications such as semantic search and Question-Answering systems based on Retrieval-Augmented Generation (RAG).…
Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation
Gabriel de Souza P. Moreira, Sara Rabhi, Ronay Ak +2
Session-based recommendation is an important task for e-commerce services, where a large number of users browse anonymously or may have very distinct interests for different sessio…