2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.RO2024★ 2 cited
CoFRIDA: Self-Supervised Fine-Tuning for Human-Robot Co-Painting
Peter Schaldenbrand, Gaurav Parmar, Jun-Yan Zhu +2
Prior robot painting and drawing work, such as FRIDA, has focused on decreasing the sim-to-real gap and expanding input modalities for users, but the interaction with these systems…
cs.CV2024
SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation
Zhixuan Liu, Peter Schaldenbrand, Beverley-Claire Okogwu +5
Accurate representation in media is known to improve the well-being of the people who consume it. Generative image models trained on large web-crawled datasets such as LAION are kn…