They're All Doctors: Synthesizing Diverse Counterfactuals to Mitigate
Associative Bias

They're All Doctors: Synthesizing Diverse Counterfactuals to Mitigate Associative Bias

17 June 2024

Salma Abdel Magid

Hanspeter Pfister

Papers citing "They're All Doctors: Synthesizing Diverse Counterfactuals to Mitigate Associative Bias"

9 / 9 papers shown

Title
Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models Salma Abdel Magid Weiwei Pan Simon Warchol Grace Guo Junsik Kim Mahia Rahman Hanspeter Pfister 84 0 0 06 Oct 2024
Adversarial Diffusion Distillation Axel Sauer Dominik Lorenz A. Blattmann Robin Rombach 138 329 0 28 Nov 2023
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision Junaid Ali Matthäus Kleindessner F. Wenzel Kailash Budhathoki V. Cevher Chris Russell VLM 60 10 0 18 Oct 2023
What does a platypus look like? Generating customized prompts for zero-shot image classification Sarah M Pratt Ian Covert Rosanne Liu Ali Farhadi VLM 119 212 0 07 Sep 2022
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Junnan Li Dongxu Li Caiming Xiong S. Hoi MLLM BDL VLM CLIP 388 4,110 0 28 Jan 2022
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search Jialu Wang Yang Liu X. Wang FaML 155 95 0 12 Sep 2021
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval Huaishao Luo Lei Ji Ming Zhong Yang Chen Wen Lei Nan Duan Tianrui Li CLIP VLM 309 778 0 18 Apr 2021
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision Chao Jia Yinfei Yang Ye Xia Yi-Ting Chen Zarana Parekh Hieu H. Pham Quoc V. Le Yun-hsuan Sung Zhen Li Tom Duerig VLM CLIP 293 3,683 0 11 Feb 2021
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets Mor Geva Yoav Goldberg Jonathan Berant 235 319 0 21 Aug 2019