2 papers
cs.CV2024
They're All Doctors: Synthesizing Diverse Counterfactuals to Mitigate Associative Bias
Salma Abdel Magid, Jui-Hsien Wang, Kushal Kafle +1
Vision Language Models (VLMs) such as CLIP are powerful models; however they can exhibit unwanted biases, making them less safe when deployed directly in applications such as text-…
cs.CV2024
FairDeDup: Detecting and Mitigating Vision-Language Fairness Disparities in Semantic Dataset Deduplication
Eric Slyman, Stefan Lee, Scott Cohen +1
Recent dataset deduplication techniques have demonstrated that content-aware dataset pruning can dramatically reduce the cost of training Vision-Language Pretrained (VLP) models wi…