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Judy Hoffman

63 papers hereh-index 5129.2k citations98 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author9
  • middle author33
  • last author21

Across the 63 of 63 papers where every author was matched, so the position is known.

fields
  • cs.CV51
  • cs.LG10
  • stat.ML2
same name
  • Judy Hoffman — 7 papers, h 4
  • Judy Hoffman — 5 papers, h 3
  • Judy Hoffman — 4 papers, h 3
  • Judy Hoffman — 1 paper
  • Judy Hoffman — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132023
most citedDeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

3.5k citations · 9.4k across the 54 of their papers we have counts for

collaborators
Showing 2015 · cs.CVShow all

4 papers · 2 filters

cs.CV2015

Adapting Deep Visuomotor Representations with Weak Pairwise Constraints

Eric Tzeng, Coline Devin, Judy Hoffman +5

Real-world robotics problems often occur in domains that differ significantly from the robot's prior training environment. For many robotic control tasks, real world experience is…

cs.CV2015★ 3 cited

Spatial Semantic Regularisation for Large Scale Object Detection

Damian Mrowca, Marcus Rohrbach, Judy Hoffman +3

Large scale object detection with thousands of classes introduces the problem of many contradicting false positive detections, which have to be suppressed. Class-independent non-ma…

cs.CV2015

Simultaneous Deep Transfer Across Domains and Tasks

Eric Tzeng, Judy Hoffman, Trevor Darrell +1

Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain…

cs.CV2015★ 23 cited

Cross Modal Distillation for Supervision Transfer

Saurabh Gupta, Judy Hoffman, Jitendra Malik

In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as a superviso…

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