21 citations · 23 across the 7 of their papers we have counts for
7 papers
Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks
Shengyao Zhuang, Ekaterina Khramtsova, Xueguang Ma +3
Recent advancements in dense retrieval have introduced vision-language model (VLM)-based retrievers, such as DSE and ColPali, which leverage document screenshots embedded as vector…
Source-Free Domain-Invariant Performance Prediction
Ekaterina Khramtsova, Mahsa Baktashmotlagh, Guido Zuccon +2
Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most exist…
Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection
Ekaterina Khramtsova, Teerapong Leelanupab, Shengyao Zhuang +2
In this demo we present a web-based application for selecting an effective pre-trained dense retriever to use on a private collection. Our system, DenseQuest, provides unsupervised…
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang +1
Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing upt…
Leveraging LLMs for Unsupervised Dense Retriever Ranking
Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh +1
In this paper we present Large Language Model Assisted Retrieval Model Ranking (LARMOR), an effective unsupervised approach that leverages LLMs for selecting which dense retriever…
Selecting which Dense Retriever to use for Zero-Shot Search
Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh +2
We propose the new problem of choosing which dense retrieval model to use when searching on a new collection for which no labels are available, i.e. in a zero-shot setting. Many de…
Rethinking Persistent Homology for Visual Recognition
Ekaterina Khramtsova, Guido Zuccon, Xi Wang +1
Persistent topological properties of an image serve as an additional descriptor providing an insight that might not be discovered by traditional neural networks. The existing resea…