3 citations · 8 across the 10 of their papers we have counts for
11 papers
FlexVDR: Learning to Allocate Matryoshka Representation Budgets for Visual Document Retrieval
Haowen Xiang, Yibo Yan, Xin Zou +4
Visual document retrieval relies on rich multi-vector representations to capture fine-grained evidence in document pages, but encoding and matching these representations can be com…
AnchorFold: A Focus-Then-Fold Framework via Recursive Attention Propagation for Efficient Multi-Vector Visual Document Retrieval
Haoyu Zuo, Yibo Yan, Xin Zou +4
Multi-vector vision-language retrievers enable fine-grained Visual Document Retrieval (VDR) through late interaction, but storing and scoring hundreds of visual patch embeddings pe…
MM-Matryoshka: Towards Budget-Elastic Visual Document Retrieval via a 2D Multimodal Matryoshka Training Framework
Haowen Xiang, Yibo Yan, Jiahao Huo +4
Multi-vector visual document retrievers achieve strong fine-grained matching by representing each page with multiple vectors from deep Vision-Language Models (VLMs), but this desig…
Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval
Yibo Yan, Mingdong Ou, Yi Cao +5
Multi-vector models dominate Visual Document Retrieval (VDR) due to their fine-grained matching capabilities, but their high storage and computational costs present a major barrier…
Beyond the Grid: Layout-Informed Multi-Vector Retrieval with Parsed Visual Document Representations
Yibo Yan, Mingdong Ou, Yi Cao +6
Harnessing the full potential of visually-rich documents requires retrieval systems that understand not just text, but intricate layouts, a core challenge in Visual Document Retrie…
Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval
Yibo Yan, Jiahao Huo, Guanbo Feng +12
With the rapid proliferation of multimodal information, Visual Document Retrieval (VDR) has emerged as a critical frontier in bridging the gap between unstructured visually rich da…
Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework
Yibo Yan, Mingdong Ou, Yi Cao +5
Visual Document Retrieval (VDR), which aims to retrieve relevant pages within vast corpora of visually-rich documents, is of significance in current multimodal retrieval applicatio…
CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding
Jiahao Huo, Yu Huang, Yibo Yan +7
Although Multimodal Large Language Models (MLLMs) have shown remarkable potential in Visual Document Retrieval (VDR) through generating high-quality multi-vector embeddings, the su…
Attributed Graph Neural Networks for Recommendation Systems on Large-Scale and Sparse Graph
Yifei Zhao, Mingdong Ou, Rongzhi Zhang +1
Link prediction in structured-data is an important problem for many applications, especially for recommendation systems. Existing methods focus on how to learn the node representat…
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective
Yifei Zhao, Yu-Hang Zhou, Mingdong Ou +2
To maximize cumulative user engagement (e.g. cumulative clicks) in sequential recommendation, it is often needed to tradeoff two potentially conflicting objectives, that is, pursui…
Multinomial Logit Bandit with Linear Utility Functions
Mingdong Ou, Nan Li, Shenghuo Zhu +1
Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a -cardinality subset from candidate i…