2.4k citations · 3.1k across the 7 of their papers we have counts for
4 papers · 1 filter
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Viraj Prabhu, David Acuna, Andrew Liao +5
Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domai…
Mitigating Bias in Visual Transformers via Targeted Alignment
Sruthi Sudhakar, Viraj Prabhu, Arvindkumar Krishnakumar +1
As transformer architectures become increasingly prevalent in computer vision, it is critical to understand their fairness implications. We perform the first study of the fairness…
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
Judy Hoffman, Dequan Wang, Fisher Yu +1
Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be su…
Deep Domain Confusion: Maximizing for Domain Invariance
Eric Tzeng, Judy Hoffman, Ning Zhang +2
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning dee…