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20232026
most citedNot Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis

32 citations · 39 across the 5 of their papers we have counts for

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cs.CV2026

Privacy in Image Datasets: A Case Study on Pregnancy Ultrasounds

Rawisara Lohanimit, Yankun Wu, Amelia Katirai +2

The rise of generative models has led to increased use of large-scale datasets collected from the internet, often with minimal or no data curation. This raises concerns about the i…

cs.CV2025

Instance-Level Generation for Representation Learning

Yankun Wu, Zakaria Laskar, Giorgos Kordopatis-Zilos +2

Instance-level recognition (ILR) focuses on identifying individual objects rather than broad categories, offering the highest granularity in image classification. However, this fin…

cs.CV2023

Stable Diffusion Exposed: Gender Bias from Prompt to Image

Yankun Wu, Yuta Nakashima, Noa Garcia

Several studies have raised awareness about social biases in image generative models, demonstrating their predisposition towards stereotypes and imbalances. This paper contributes…

cs.CV202332 cited

Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis

Yankun Wu, Yuta Nakashima, Noa Garcia

The duality of content and style is inherent to the nature of art. For humans, these two elements are clearly different: content refers to the objects and concepts in the piece of…

cs.CV20236 cited

Uncurated Image-Text Datasets: Shedding Light on Demographic Bias

Noa Garcia, Yusuke Hirota, Yankun Wu +1

The increasing tendency to collect large and uncurated datasets to train vision-and-language models has raised concerns about fair representations. It is known that even small but…