Projects

	New! Algorithms and Theory for Multiple-Source Adaptation Judy Hoffman, Mehryar Mohri, Ningshan Zhang Neural Information Processing Symposium (NIPS), 2018.
	CyCADA: Cycle-Consistent Adversarial Domain Adaptation Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell. International Conference on Machine Learning (ICML), 2018. [code]
	Scaling Human-Object Interaction Recognition through Zero-Shot Learning Liyue Shen, Serena Yeung, Judy Hoffman, Greg Mohri, Li Fei-Fei IEEE Winter Conference on Applications in Computer Vision (WACV), 2018.
	Label Efficient Learning of Transferable Representations across Domains and Tasks Zelun Luo, Yuliang Zou, Judy Hoffman, Li Fei-Fei Neural Information Processing Systems (NIPS), 2017. [project page]
	Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach Timnit Gebru, Judy Hoffman, Li Fei-Fei International Conference in Computer Vision (ICCV), 2017.
	Inferring and Executing Programs for Visual Reasoning Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick. International Conference in Computer Vision (ICCV), 2017. (oral) [project] / [code]
	Adversarial Discriminative Domain Adaptation Eric Tzeng, Judy Hoffman, Trevor Darrell, Kate Saenko Computer Vision and Pattern Recognition (CVPR), 2017. bibtex / cvpr pdf / code A framework for adversarial unsupervised domain adaptation.
	Adapting deep visuomotor representations with weak pairwise constraints Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel, Sergey Levine, Kate Saenko, Trevor Darrell Workshop on Algorithmic Foundations in Robotics (WAFR), 2016. bibtex
	Clockwork Convnets for Video Semantic Segmentation Evan Shelhamer, Kate Rakelly, Judy Hoffman, Trevor Darrell Video Semantic Segmentation Workshop at European Conference in Computer Vision (ECCV), 2016. Authors Contributed Equally. bibtex
	Learning with Side Information through Modality Hallucination Judy Hoffman, Saurabh Gupta, Trevor Darrell In Proc. Computer Vision and Pattern Recognition (CVPR), 2016. (Spotlight) bibtex / press A method to hallucinate mid-level activations for a missing modality at test time.
	Cross Modal Distillation for Supervision Transfer Saurabh Gupta, Judy Hoffman, Jitendra Malik In Proc. Computer Vision and Pattern Recognition (CVPR), 2016. bibtex / models and code We propose a method for pre-training a deep network for a new imaging modality which lacks sufficient supervised training data.
	Fine-To-Coarse Knowledge Transfer For Low-Res Image Classification Xingchao Peng, Judy Hoffman, Stella Yu, Kate Saenko International Conference on Image Processing (ICIP), 2016.
	Cross-Modal Adaptation for RGB-D Detection Judy Hoffman, Saurabh Gupta, Jian Leong, Sergio Guadarrama, Trevor Darrell, IEEE International Conference on Robotics and Automation (ICRA), 2016. bibtex We propose a technique to adapt CNN based object detectors trained on RGB images to effectively leverage depth images at test time to boost detection performance.
	Quantification in-the-wild: data-sets and baselines Oscar Beijbom, Judy Hoffman, Evan Yao, Trevor Darrell, Alberto Rodriguez-Ramirez, Manuel Gonzlez-Rivero, Ove Hoegh-Guldberg. Transfer and Multi-Task Learning: Trends and New Perspectives, Workshop at NIPS, 2015. Introduces two new ecological datasets for domain adaptation for quantification.
	Simultaneous Deep Transfer Across Domains and Tasks Eric Tzeng, Judy Hoffman, Trevor Darrell, Kate Saenko International Conference on Computer Vision (ICCV), 2015. *Equal Contribution bibtex / caffe branch / prototxt We introduce a domain confusion and softlabel loss to simultaneously learn a visual representation which is both discriminative and renders the domains indistinguishable.
	Spatial Semantic Regularisation for Large Scale Object Detection Damian Mrowca, Marcus Rohrbach, Judy Hoffman, Ronghang Hu, Kate Saenko, Trevor Darrell International Conference on Computer Vision (ICCV), 2015. bibtex We propose a multi-class spatial regularization method based on adaptive affinity propagation clustering which simultaneously optimizes across all categories and all proposed locations in the image to improve both location and categorization of selected detection proposals.
	Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning Judy Hoffman, Deepak Pathak, Trevor Darrell, Kate Saenko Computer Vision and Pattern Recognition (CVPR), 2015. bibtex We propose a model that simultaneously trains a representation and detectors for categories with either image-level or bounding-box localized labels present. We provide a novel formulation of a joint multiple instance learning method that combines the heterogenous data sources.
	LSDA: Large Scale Detection through Adaptation Judy Hoffman, Sergio Guadarrama, Eric Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko Neural Information Processing Symposium (NIPS), 2014. bibtex / project page Released >7.5K detector! We present a method to transform classifiers into detectors by transferring knowledge from known detector categories.
	Continuous Manifold Based Adaptation for Evolving Visual Domains Judy Hoffman, Trevor Darrell, Kate Saenko Computer Vision and Pattern Recognition (CVPR), 2014. bibtex / video / project page We propose a method for adapting to unlabeled data over time by modeling a continuosly evolving domain.
	Interactive Adaptation of Real-Time Object Detectors Daniel Goehring, Judy Hoffman, Erik Rodner, Kate Saenko, Trevor Darrell International Conference in Robotics and Automation (ICRA), 2014. bibtex / project page We propose a method for quickly training detectors for novel categories on in-situ image data.
	DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell International Conference in Machine Learning (ICML), 2014. bibtex / code We propose a new feature based on deep convolutional neural networks and show improvement over state-of-the-art visual feature representations.
	Asymmetric and Category Invariant Feature Transformations for Domain Adaptation Judy Hoffman, Erik Rodner, Jeff Donahue, Brian Kulis, Kate Saenko International Journal of Computer Vision, Special Domain Adaptation Addition, 2013. bibtex
	Efficient Learning of Domain-invariant Image Representations Judy Hoffman, Erik Rodner, Jeff Donahue, Kate Saenko, Trevor Darrell International Conference on Learning Representations (ICLR), 2013. (Oral) bibtex / talk / code We learn a category invariant feature transformation, which maps target points into the source domain such that they corrected classified by the source classifier.
	Semi-Supervised Domain Adaptation with Instance Constraints Jeff Donahue, Judy Hoffman, Erik Rodner, Kate Saenko, Trevor Darrell Computer Vision and Pattern Recognition (CVPR), 2013. bibtex / poster By using instance constraints, available through tracking or other methods, we can improve unsupervised domain adaptation performance.
	Discovering Latent Domains For Multisource Domain Adaptation Judy Hoffman, Brian Kulis, Trevor Darrell, Kate Saenko European Conference in Computer Vision (ECCV), 2012. supplementary material / bibtex / poster / video / code We learn to separate large heterogeneous data sources into multiple latent visual domains and show that using this learned clustering improves classification performance.
	Weakly Supervised Learning of Object Segmentations from Web-Scale Video Glen Hartmann, Matthias Grundmann, Judy Hoffman, David Tsai, Vivek Kwatra, Omid Madani, Sudheendra Vijayanarasimhan, Irfan Essa, James Rehg, Rahul Sukthankar European Conference in Computer Vision (ECCV) Workshop on Web-scale Vision and Social Media, 2012. (Best Paper Award) bibtex We learn segment level video classification using videos with only weakly labeled tag information.
	Domain Adaptation with Multiple Latent Domains Judy Hoffman, Kate Saenko, Brian Kulis, Trevor Darrell NIPS Domain Adaptation Workshop Talk, 2011. (Best Student Paper Award) We present a method for multi-source adaptation with latent source domains. See ECCV2012 paper for more details.
	EG-RRT: Environment-Guided Random Trees for Kinodynamic Motion Planning with Uncertainty and Obstacles Leonard Jaillet, Judy Hoffman, Jur van den Berg, Pieter Abbeel, Josep M. Porta, Ken Goldberg IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011. bibtex