2 citations · 4 across the 6 of their papers we have counts for
6 papers
Normative Alignment of Recommender Systems via Internal Label Shift
Johannes Kruse, Kasper Lindskow, Michael Riis Andersen +4
We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions…
Attributed Synthetic Data Generation for Zero-shot Domain-specific Image Classification
Shijian Wang, Linxin Song, Ryotaro Shimizu +2
Zero-shot domain-specific image classification is challenging in classifying real images without ground-truth in-domain training examples. Recent research involved knowledge from t…
Vision and Language Reference Prompt into SAM for Few-shot Segmentation
Kosuke Sakurai, Ryotaro Shimizu, Masayuki Goto
Segment Anything Model (SAM) represents a large-scale segmentation model that enables powerful zero-shot capabilities with flexible prompts. While SAM can segment any object in zer…
LARE: Latent Augmentation using Regional Embedding with Vision-Language Model
Kosuke Sakurai, Tatsuya Ishii, Ryotaro Shimizu +2
In recent years, considerable research has been conducted on vision-language models that handle both image and text data; these models are being applied to diverse downstream tasks…
Partial Visual-Semantic Embedding: Fashion Intelligence System with Sensitive Part-by-Part Learning
Ryotaro Shimizu, Takuma Nakamura, Masayuki Goto
In this study, we propose a technology called the Fashion Intelligence System based on the visual-semantic embedding (VSE) model to quantify abstract and complex expressions unique…
Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding
Ryotaro Shimizu, Masanari Kimura, Masayuki Goto
Several techniques to map various types of components, such as words, attributes, and images, into the embedded space have been studied. Most of them estimate the embedded represen…