9.7k citations · 22.1k across the 16 of their papers we have counts for
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One-Shot Adaptation of Supervised Deep Convolutional Models
Judy Hoffman, Eric Tzeng, Jeff Donahue +3
Dataset bias remains a significant barrier towards solving real world computer vision tasks. Though deep convolutional networks have proven to be a competitive approach for image c…
Deep Convolutional Ranking for Multilabel Image Annotation
Yunchao Gong, Yangqing Jia, Thomas Leung +2
Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual featur…
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals +4
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks c…
Pooling-Invariant Image Feature Learning
Yangqing Jia, Oriol Vinyals, Trevor Darrell
Unsupervised dictionary learning has been a key component in state-of-the-art computer vision recognition architectures. While highly effective methods exist for patch-based dictio…
Why Size Matters: Feature Coding as Nystrom Sampling
Oriol Vinyals, Yangqing Jia, Trevor Darrell
Recently, the computer vision and machine learning community has been in favor of feature extraction pipelines that rely on a coding step followed by a linear classifier, due to th…