14 citations · 16 across the 11 of their papers we have counts for
11 papers
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…
EMMA: Concept Erasure Benchmark with Comprehensive Semantic Metrics and Diverse Categories
Lu Wei, Yuta Nakashima, Noa Garcia
The widespread adoption of text-to-image (T2I) generation has raised concerns about privacy, bias, and copyright violations. Concept erasure techniques offer a promising solution b…
From Global to Local: Social Bias Transfer in CLIP
Ryan Ramos, Yusuke Hirota, Yuta Nakashima +1
The recycling of contrastive language-image pre-trained (CLIP) models as backbones for a large number of downstream tasks calls for a thorough analysis of their transferability imp…
Processing and acquisition traces in visual encoders: What does CLIP know about your camera?
Ryan Ramos, Vladan Stojnić, Giorgos Kordopatis-Zilos +3
Prior work has analyzed the robustness of visual encoders to image transformations and corruptions, particularly in cases where such alterations are not seen during training. When…
No Annotations for Object Detection in Art through Stable Diffusion
Patrick Ramos, Nicolas Gonthier, Selina Khan +2
Object detection in art is a valuable tool for the digital humanities, as it allows for faster identification of objects in artistic and historical images compared to humans. Howev…
Gender Bias Evaluation in Text-to-image Generation: A Survey
Yankun Wu, Yuta Nakashima, Noa Garcia
The rapid development of text-to-image generation has brought rising ethical considerations, especially regarding gender bias. Given a text prompt as input, text-to-image models ge…
Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph
Wanqing Zhao, Yuta Nakashima, Haiyuan Chen +1
Fake news detection in social media has become increasingly important due to the rapid proliferation of personal media channels and the consequential dissemination of misleading in…
Would Deep Generative Models Amplify Bias in Future Models?
Tianwei Chen, Yusuke Hirota, Mayu Otani +2
We investigate the impact of deep generative models on potential social biases in upcoming computer vision models. As the internet witnesses an increasing influx of AI-generated im…
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…
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…
Situating the social issues of image generation models in the model life cycle: a sociotechnical approach
Amelia Katirai, Noa Garcia, Kazuki Ide +2
The race to develop image generation models is intensifying, with a rapid increase in the number of text-to-image models available. This is coupled with growing public awareness of…