80 citations · 115 across the 6 of their papers we have counts for
6 papers · 1 filter
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods
Skanda Koppula, Yazhe Li, Evan Shelhamer +5
Self-supervised methods have achieved remarkable success in transfer learning, often achieving the same or better accuracy than supervised pre-training. Most prior work has done so…
On-target Adaptation
Dequan Wang, Shaoteng Liu, Sayna Ebrahimi +2
Domain adaptation seeks to mitigate the shift between training on the \emph{source} domain and testing on the \emph{target} domain. Most adaptation methods rely on the source data…
Dynamic Scale Inference by Entropy Minimization
Dequan Wang, Evan Shelhamer, Bruno Olshausen +1
Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field. Rather than enumerate…
Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields
Evan Shelhamer, Dequan Wang, Trevor Darrell
The visual world is vast and varied, but its variations divide into structured and unstructured factors. We compose free-form filters and structured Gaussian filters, optimized end…
Few-Shot Segmentation Propagation with Guided Networks
Kate Rakelly, Evan Shelhamer, Trevor Darrell +2
Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of…
Fully Convolutional Networks for Semantic Segmentation
Evan Shelhamer, Jonathan Long, Trevor Darrell
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, impro…