44 citations · 90 across the 34 of their papers we have counts for
6 papers · 1 filter
Culture in Action: Evaluating Text-to-Image Models through Social Activities
Sina Malakouti, Boqing Gong, Adriana Kovashka
Text-to-image (T2I) diffusion models achieve impressive photorealism by training on large-scale web data, but models inherit cultural biases and fail to depict underrepresented reg…
The Face of Persuasion: Analyzing Bias and Generating Culture-Aware Ads
Aysan Aghazadeh, Adriana Kovashka
Text-to-image models are appealing for customizing visual advertisements and targeting specific populations. We investigate this potential by examining the demographic bias within…
A Multimodal Recaptioning Framework to Account for Perceptual Diversity Across Languages in Vision-Language Modeling
Kyle Buettner, Jacob T. Emmerson, Adriana Kovashka
When captioning an image, people describe objects in diverse ways, such as by using different terms and/or including details that are perceptually noteworthy to them. Descriptions…
Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting
Cagri Gungor, Derek Eppinger, Adriana Kovashka
Tactile sensing, which relies on direct physical contact, is critical for human perception and underpins applications in computer vision, robotics, and multimodal learning. Because…
Role Bias in Diffusion Models: Diagnosing and Mitigating through Intermediate Decomposition
Sina Malakouti, Adriana Kovashka
Text-to-image (T2I) diffusion models exhibit impressive photorealistic image generation capabilities, yet they struggle in compositional image generation. In this work, we introduc…
Leveraging Large Models to Evaluate Novel Content: A Case Study on Advertisement Creativity
Zhaoyi Joey Hou, Adriana Kovashka, Xiang Lorraine Li
Evaluating creativity is challenging, even for humans, not only because of its subjectivity but also because it involves complex cognitive processes. Inspired by work in marketing,…