1 citations · 1 across the 3 of their papers we have counts for
5 papers
e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings
Haonan Chen, Sicheng Gao, Radu Timofte +2
Modern information systems often involve different types of items, e.g., a text query, an image, a video clip, or an audio segment. This motivates omni-modal embedding models that…
MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings
Haonan Chen, Hong Liu, Yuping Luo +4
Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three key limitations: the use o…
mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data
Haonan Chen, Liang Wang, Nan Yang +4
Multimodal embedding models have gained significant attention for their ability to map data from different modalities, such as text and images, into a unified representation space.…
Chain-of-Retrieval Augmented Generation
Liang Wang, Haonan Chen, Nan Yang +3
This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Conventional R…
Little Giants: Synthesizing High-Quality Embedding Data at Scale
Haonan Chen, Liang Wang, Nan Yang +4
Synthetic data generation has become an increasingly popular way of training models without the need for large, manually labeled datasets. For tasks like text embedding, synthetic…