Papers2

#visual document retrieval

Multi-Vector Index Compression in Any Modality

Hanxiang Qin, Alexander Martin et al.Feb 24arXiv

Searching through videos, images, and long documents is powerful but gets very expensive when every tiny piece is stored separately.

#multi-vector retrieval#late interaction#index compression

Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking

Intermediate

Mingxin Li, Yanzhao Zhang et al.Jan 8arXiv

This paper builds two teamwork models, Qwen3-VL-Embedding and Qwen3-VL-Reranker, that understand text, images, visual documents, and videos in one shared space so search works across all of them.

#multimodal retrieval#unified embedding space#cross-encoder reranker