1 citations · 1 across the 8 of their papers we have counts for
8 papers
How Should We Evaluate Data Deletion in Graph-Based ANN Indexes?
Tomohiro Yamashita, Daichi Amagata, Yusuke Matsui
Approximate Nearest Neighbor Search (ANNS) has recently gained significant attention due to its many applications, such as Retrieval-Augmented Generation. Such applications require…
RouteExtract: A Modular Pipeline for Extracting Routes from Paper Maps
Bjoern Kremser, Yusuke Matsui
Paper maps remain widely used for hiking and sightseeing because they contain curated trails and locally relevant annotations that are often missing from digital navigation applica…
Region-Wise Correspondence Prediction between Manga Line Art Images
Yingxuan Li, Jiafeng Mao, Qianru Qiu +1
Understanding region-wise correspondences between manga line art images is fundamental for high-level manga processing, supporting downstream tasks such as line art colorization an…
FlexFlood: Efficiently Updatable Learned Multi-dimensional Index
Fuma Hidaka, Yusuke Matsui
A learned multi-dimensional index is a data structure that efficiently answers multi-dimensional orthogonal queries by understanding the data distribution using machine learning mo…
High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image Synthesis
Takuto Onikubo, Yusuke Matsui
Recently, text-to-image generative models have been misused to create unauthorized malicious images of individuals, posing a growing social problem. Previous solutions, such as Ant…
Revisiting Relevance Feedback for CLIP-based Interactive Image Retrieval
Ryoya Nara, Yu-Chieh Lin, Yuji Nozawa +4
Many image retrieval studies use metric learning to train an image encoder. However, metric learning cannot handle differences in users' preferences, and requires data to train an…
ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer
Hiroki Azuma, Yusuke Matsui, Atsuto Maki
Deep learning models achieve high accuracy in segmentation tasks among others, yet domain shift often degrades the models' performance, which can be critical in real-world scenario…
Adversarial Doodles: Interpretable and Human-drawable Attacks Provide Describable Insights
Ryoya Nara, Yusuke Matsui
DNN-based image classifiers are susceptible to adversarial attacks. Most previous adversarial attacks do not have clear patterns, making it difficult to interpret attacks' results…