2.4k citations · 7.6k across the 125 of their papers we have counts for
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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…
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning
Judy Hoffman, Deepak Pathak, Trevor Darrell +1
We develop methods for detector learning which exploit joint training over both weak and strong labels and which transfer learned perceptual representations from strongly-labeled a…
Learning Deep Object Detectors from 3D Models
Xingchao Peng, Baochen Sun, Karim Ali +1
Crowdsourced 3D CAD models are becoming easily accessible online, and can potentially generate an infinite number of training images for almost any object category.We show that aug…
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach +4
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effe…
LSDA: Large Scale Detection Through Adaptation
Judy Hoffman, Sergio Guadarrama, Eric Tzeng +5
A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) hav…