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Prof. Trevor Darrell
CS Division, University of California,
Berkeley Faculty Director, UC Berkeley PATH Managing Director, Berkeley Vision and Learning
Center |
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Prof. Darrell is on the faculty of the CS
Division of the EECS Department at UC Berkeley and he is also appointed at the
UC-affiliated International Computer Science Institute (ICSI). Darrell’s group develops algorithms for
large-scale perceptual learning, including object and activity recognition and
detection, for a variety of applications including multimodal interaction with
robots and mobile devices. His interests include computer vision, machine
learning, computer graphics, and perception-based human computer interfaces.
Prof. Darrell was previously on the faculty of the MIT EECS department from
1999-2008, where he directed the Vision Interface Group. He was a member of the
research staff at Interval Research Corporation from 1996-1999, and received
the S.M., and PhD. degrees from MIT in 1992 and 1996, respectively. He obtained
the B.S.E. degree from the University of Pennsylvania in 1988, having started
his career in computer vision as an undergraduate researcher in Ruzena Bajcsy's GRASP lab.
Software
·
CAFFE – Open Source
Deep Learning; Yangqing Jia,
Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick,
Sergio Guadarrama, Trevor Darrell
·
LRCN
–
Long-term Recurrent Convolutional Networks; Jeff Donahue, Lisa Anne Hendricks,
Sergio Guadarrama, Marcus Rohrbach,
Subhashini Venugopalan,
Kate Saenko, Trevor Darrell; Recent Results on
LCRN Language and Vision Captioning: See our project page
(and also P. Dollar’s blog post on related efforts
from other groups).
·
LSDA
–
Large Scale Detector Adaptation; Judy Hoffman, Sergio Guadarrama,
Eric Tzeng, Jeff Donahue, Ross Girshick,
Trevor Darrell, Kate Saenko
·
RAPTOR
–
Interactive Detector Learning; Daniel Göhring and
Judy Hoffman and Erik Rodner and Kate Saenko, and Trevor Darrell
2015
Publications
arXiv:
·
End-to-End
Training of Deep Visuomotor Policies, S Levine, C
Finn, T Darrell, P Abbeel, arXiv preprint
arXiv:1504.00702
·
Sequence
to Sequence--Video to Text, S Venugopalan, M Rohrbach, J Donahue, R Mooney, T Darrell, K Saenko, arXiv preprint
arXiv:1505.00487
·
Constrained
Convolutional Neural Networks for Weakly Supervised Segmentation, D Pathak,
P Krähenbühl, T Darrell, arXiv
preprint arXiv:1506.03648
CVPR
·
Deformable Part Models are Convolutional Neural Networks [full paper] [ext. abstract] Ross Girshick, Forrest Iandola, Trevor
Darrell, Jitendra Malik
·
Long-Term Recurrent Convolutional Networks for Visual
Recognition and Description [full paper] [ext. abstract] Jeffrey Donahue,
Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor
Darrell
·
Long-Term Recurrent Convolutional Networks for Visual
Recognition and Description [full
paper] [ext.
abstract] Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama,
Marcus Rohrbach, Subhashini
Venugopalan, Kate Saenko,
Trevor Darrell
·
Detector Discovery in the Wild: Joint Multiple Instance and
Representation Learning [full
paper] [ext.
abstract] Judy Hoffman, Deepak Pathak, Trevor Darrell, Kate Saenko
·
Fully Convolutional Networks for Semantic Segmentation [full
paper] [ext.
abstract]
Jonathan Long, Evan Shelhamer, Trevor Darrell
ICCV
2015, (to appear)
·
Deep Dynamic Convolutional Networks
·
Constrained Convolutional Neural Networks for Weakly
Supervised Segmentation
·
Simultaneous Deep Transfer Across Domains and Tasks
·
Sequence to Sequence Video to Text
·
Spatial Semantic Regularisation
for Large Scale Object Detection
2014
Publications
ICML
·
DECAF: A deep convolutional activation feature for generic
visual recognition; Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning
Zhang, Eric Tzeng, Trevor Darrell
·
On learning to localize objects with minimal supervision; Hyun Oh
Song, Ross Girshick, Stefanie Jegelka,
Julien Mairal, Zaid Harchaoui,
Trevor Darrell
CVPR
· PANDA: Pose Aligned Networks for Deep Attribute Modeling; Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, Lubomir Bourdev
· Rich feature hierarchies for accurate object detection and semantic segmentation; Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
· Anytime Recognition of Objects and Scenes; Sergey Karayev, Mario Fritz, Trevor Darrell
· Learning Scalable Discriminative Dictionary with Sample Relatedness; Jiashi Feng, Stefanie Jegelka, Shuicheng Yan, Trevor Darrell
· Continuous Manifold Based Adaptation for Evolving Visual Domains; Judy Hoffman, Trevor Darrell, Kate Saenko
ICRA
· Interactive Adaptation of Real-Time Object Detectors; Daniel Göhring and Judy Hoffman and Erik Rodner and Kate Saenko, and Trevor Darrell
RSS
· Open-vocabulary Object Retrieval; Sergio Guadarrama, Erik Rodner, Kate Saenko, Ning Zhang, Ryan Farrell, Jeff Donahue, Trevor Darrell
BMVC
· Recognizing Image Style; Sergey Karayev, Aaron Hertzmann, Holger Winnemoeller, Aseem Agarwala, Trevor Darrell
ECCV
·
Part-based R-CNNs for
Fine-grained Category Detection, Ning Zhang, Jeff Donahue,
Ross Girshick, Trevor Darrell, UC Berkeley
MM
· CAFFE: Convolutional Architecture for Fast Feature Embedding; Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell
PAMI
· Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images; Ying Xiong, Daniel Scharstein, Ayan Chakrabarti, Trevor Darrell, Baochen Sun, Kate Saenko, Todd Zickler
NIPS
·
Large
Scale Detector Adaptation; Judy Hoffman, Sergio Guadarrama, Eric Tzeng, Jeff
Donahue, Ross Girshick, Trevor Darrell, Kate Saenko
· Weakly-supervised Discovery of Visual Pattern Configurations; Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell
· Do Convnets Learn Correspondence? Jon Long, Ning Zhang, Trevor Darrell
arXiv
· DenseNet: Implementing Efficient ConvNet Descriptor Pyramids; Forrest Iandola, Matt Moskewicz, Sergey Karayev, Ross Girshick, Trevor Darrell, Kurt Keutzer
· One-Shot Adaptation of Supervised Deep Convolutional Models; Judy Hoffman, Eric Tzeng, Jeff Donahue, Yangqing Jia, Kate Saenko, Trevor Darrell
Current Teaching
CS294: Object and Activity Recognition Seminar
(2011 - present)
CS280: Computer Vision (Fall
2009)
CS294: Object and Activity
Recognition Seminar (Spring 2009)
Recent Presentations
Jan 2014 NSF Workshop Talk (pdf)
Sept 2012 BAVM Invited Talk (pdf) (pptx)
Publications
2013 and Prior:
S. Guadarrama, N. Krishnamoorthy, G. Malkarnenkar,
S. Venugopalan, R. Mooney, T. Darrell, K. Saenko, YouTube2Text: Recognizing
and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-shot
Recognition, ICCV 2013
Y. Jia and T. Darrell, Latent Task Adaptation
with Large-Scale Hierarchies,
ICCV 2013
N. Zhang, R. Farrell, F. Iandola,
T. Darrell, Deformable part
descriptors for fine-grained recognition and attribute prediction, ICCV 2013
Y. Jia, J T Abbott, J. Austerweil, T. Griffiths, T. Darrell, Visual Concept Learning:
Combining Machine Vision and Bayesian Generalization on Concept Hierarchies, NIPS 2013
Y. Jia, O. Vinyals, and T.
Darrell. On Compact Codes for
Spatially Pooled Features.
ICML 2013
H. O. Song, R. Girshick, and T.
Darrell, Discriminatively Activated Sparselets,
ICML 2013
J. Donahue, J. Hoffman, E. Rodner,
K. Saenko, and T. Darrell, Semi-Supervised Domain
Adaptation with Instance Constraints, CVPR 2013
V. Chu, I. McMahon, L. Riano, C.
G. McDonald, Q. He, J. Perez-Tejada, M.
Arrigo, N. Fitter, J. Nappo, T. Darrell, and K. J.
Kuchenbecker. Using
robotic exploratory procedures to learn the meaning of haptic adjectives. ICRA 2013. Best Cognitive Robotics Paper Award
J. Hoffman, E. Rodner, J.
Donahue, T. Darrell, K. Saenko, Efficient Learning of
Domain-invariant Image Representations, ICLR 2013 Conference
O. Vinyals, Y. Jia, T. Darrell, Why Size Matters: Feature
Coding as Nystrom Sampling,
ICLR 2013 Workshop
S. Karayev, M. Fritz, T. Darrell,
Timely Object Recognition, NIPS 2012
O. Vinyals, Y. Jia, L. Deng, T.
Darrell, Learning with Recursive
Perceptual Representations,
NIPS 2012
S. Chung, C. Christoudias, T.
Darrell, S. Ziniel, L. Kalish,
A Novel Image Based Tool to
Reunite Children with Their Families after Disasters, AEMJ
T. Althoff, H. O. Song, T. Darrell,
Detection Bank: An Object
Detection Based Video Representation for Multimedia Event Recognition, ACM MM 2012
J. Hoffman, B. Kulis, T. Darrell, K. Saenko, Discovering Latent Domains
for Multisource Domain Adaptation,
ECCV 2012
H. O. Song, S. Zickler, T. Althoff, R. Girshick, M. Fritz,
C. Geyer, P. Felzenszwalb, T. Darrell, Sparselet Models for Efficient
Multiclass Object Detection,
ECCV 2012
S. Miller, J. Van Den Berg, M. Fritz, T. Darrell, K.
Goldberg, P. Abbeel, A geometric approach to robotic laundry folding, IJRR 2012
S. Virtanen, Y.
Jia, A. Klami, T. Darrell. Factorized Multi-modal Topic
Model. UAI 2012
Y. Xiong, K. Saenko,
T. Zickler, T. Darrell, From Pixels to Physics:
Probabilistic Color De-rendering,
CVPR 2012.
N. Zhang, R. Farrell, T. Darrell. Pose Pooling Kernels for
Sub-category Recognition.
CVPR 2012
Y. Jia, C. Huang, T. Darrell. Beyond Spatial Pyramids:
Receptive Field Learning for Pooled Image Features, CVPR 2012
Y. Jia and T. Darrell, Heavy-tailed Distances for
Gradient Based Image Descriptors,
NIPS 2011
R. Farrell, O. Oza, N. Zhang, V. Morariu, T. Darrell, and L. Davis, Birdlets: Subordinate Categorization
Using Volumetric Primitives and Pose-Normalized Appearance, ICCV 2011.
T. Tuytelaars, M. Fritz, K. Saenko, and T. Darrell, The NBNN Kernel, ICCV 2011.
Y. Jia, M. Salzmann, and T.
Darrell, Learning Cross-modality Similarity for
Multinomial Data,
ICCV 2011.
S. Karayev, A. Janoch, Y. Jia, J. Barron, M.
Fritz, K. Saenko, and T. Darrell, A Category-level 3-D Database: Putting the Kinect
to Work, in ICCV
2011 Workshop on Consumer Depth Cameras for Computer Vision, 2011.
H. O. Song, M. Fritz, C. Gu and T. Darrell, Visual Grasp Affordances From
Appearance-Based Cues,
ICCV Workshop on Challenges and Opportunities in Robot Perception, 2011
P. Wang, S. Miller, M. Fritz, T. Darrell, and P. Abbeel, Perception for the
Manipulation of Socks, IROS
2011.
K. Saenko, Y. Jia,
M. Fritz, J. Long, A. Janoch, A. Shyr,
S. Karayev and T. Darrell, Practical 3-D Object
Detection Using Category and Instance-Level Appearance Models, IROS 2011
S. Miller, M. Fritz, T. Darrell, and P. Abbeel, Parametrized Shape Models for
Clothing, ICRA
2011.
S. Karayev, M. Fritz, S. Fidler, and T. Darrell, A Probabilistic Model for
Recursive Factorized Image Features,
CVPR 2011.
B. Kulis, K. Saenko, and T.
Darrell, What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel
Transforms, CVPR
2011.
A. Shyr, T. Darrell, M. Jordan,
and R. Urtasun, Supervised Hierarchical
Pitman-Yor Process for Natural Scene Segmentation, CVPR 2011.
T. Owens, K. Saenko, A. Chakrabarti, Y. Xiong, T. Zickler, and T. Darrell.
Learning Object Color Models
from Multi-view Constraints,
CVPR 2011.
A. Eden, M. Christoudias, and T.
Darrell, Finding Lost Children, POV 2011.
Y. Jia, M. Salzmann, and T.
Darrell, Factorized Latent Spaces with
Structured Sparsity,
NIPS 2010.
M. Fritz, K. Saenko, and T.
Darrell, Size Matters: Metric Visual
Search Constraints from Monocular Metadata,
NIPS 2010.
G. Friedland, O. Vinyals, and T.
Darrell, Multimodal Location
Estimation. ACM
Multimedia 2010.
K. Saenko, B. Kulis, M. Fritz,
and T. Darrell, Adapting Visual Category
Models to New Domains,
ECCV 2010.
M. Christoudias,
R. Urtasun, M. Salzmann, and T. Darrell, Learning to Recognize Objects
from Unseen Modalities,
ECCV 2010.
A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell, Gaussian Processes for Object
Categorization,
IJCV, 2010.
M. Salzmann, C. H. Ek, R. Urtasun, and T. Darrell. Factorized
orthogonal latent spaces. UAI 2010.
M. Fritz, M. Black, G. Bradski,
and T. Darrell, An Additive Latent Feature
Model for Transparent Object Recognition, NIPS 2009.
Z. Stone, T. Zickler, and T.
Darrell, Toward Large-Scale Face
Recognition Using Social Network Context, Proc. of the IEEE 2010
B. Kulis and T. Darrell, Learning to Hash with Binary Reconstructive Embeddings, NIPS 2009.
K. Saenko and T. Darrell, Filtering Abstract Senses From Image Search Results, NIPS 2009.
A. Quattoni, X. Carreras, M. Collins, and T. Darrell, An Efficient Projection for L-1/L-Infinity Regularization, ICML 2009.
T. Yeh and T. Darrell, Fast Concurrent Object Localization and Recognition, CVPR 2009.
M. Christoudias, R. Urtasun, A. Kapoor and T. Darrell, Co-training with Noisy Perceptual Observations, CVPR 2009.
A. Geiger, R. Urtasun, and T. Darrell, Rank Priors for Continuous Non-Linear Dimensionality Reduction, CVPR 2009.
M. Frampton, R. Fernandez, P. Ehlen,
M. Christoudias, T. Darrell, and S. Peters (2009) Who is ``You? Combining
Linguistic and Gaze Features to Resolve Second-Person References in Dialogue, EACL 2009.
K. Saenko, K. Livescu,
J. Glass, and T. Darrell, Multistream Articulatory Feature-Based
Models for Visual Speech Recognition, TPAMI 2009
K. Saenko, and T. Darrell, Unsupervised Learning of Visual Sense Models for Polysemous Word, NIPS 2008.
T. Yeh, J. Lee, and T. Darrell, Photo-based Question Answering, ACM Multimedia 2008.
T. Yeh, and T. Darrell, Multimodal Question Answering for Mobile Devices, IUI 2008.
M. Christoudias, R. Urtasun and T. Darrell, Multi-View Learning in the Presence of View Disagreement, UAI 2008.
R. Urtasun, D. J. Fleet, A. Geiger, J. Popovic, T. Darrell and N. D. Lawrence, Topologically-Constrained Latent Variable Models, ICML 2008.
M. Christoudias, R. Urtasun and T. Darrell, Unsupervised Feature Selection via Distributed Coding for Multi-view Object Recognition, CVPR 2008.
A. Quattoni, M. Collins, and T. Darrell, Transfer Learning for Image Classification with Sparse Prototype Representations, CVPR 2008.
T. Yeh, J. Lee, and T. Darrell, Dynamic Visual Category Learning, CVPR 2008.
R. Urtasun, and T. Darrell, Sparse probabilistic regression for activity-independent human pose inference, CVPR 2008.
T. Yeh, J. Lee, and T. Darrell, Scalable classifiers for Internet vision tasks, IEEE Workshop on Internet Vision 2008.
Z. Stone, T. Zickler, and T. Darrell, Autotagging Facebook: Social Network
Context Improves Photo Annotation, IEEE Workshop on Internet Vision 2008.
Older publications: http://www.eecs.berkeley.edu/~trevor/Publications.htm
Older projects, classes, and meetings organized
Darrell Group (UCB/ICSI) – (2013
Group Spotlight Presentations)
Postdocs:
Dr. Stefanie Jegelka (Joint with
Mike Jordan)
Dr. Sergio Guardarrama
Dr. Jiashi Feng
Dr. Marcus Rohrback
PhD Students:
Eric
Tzeng
Lisa
Hendricks
Previous Postdocs (not including former students)
Dr. Eric Rodner
Dr.
Ryan Farrell
Dr. Lorenzo Riano
Dr. Daniel Goehring
Dr. Brian
Kulis
Dr. David Demirdjian
Dr. Raquel Urtasun
Dr. Mario Fritz
Dr. Matthieu
Salzmann
Dr. Sanja Fidler
(visiting)
Dr. Nicholas Cebron
Dr. Carl Ek
Dr. Peer Stelldinger
Graduated PhD. Students:
Alex Shyr,
Incorporating Supervision for Visual Recognition and Segmentation, Sept 2011 [Startup]
Ashley Eden, Finding Lost Children, Dec 2010 [Dreamworks]
Kate Saenko, Image Sense Disambiguation: A Multimodal Approach,
Aug 2009 [Faculty UML]
Tom Yeh,
Interacting with Computers using Images for Search and Automation, May 2009
[Postdoc, UMD]
Leonid Taycher, Statistical methods for dynamic visual processing,
Aug 2006 [Google Boston]
Graduated MS Students:
Previous Graduate Visitors:
Tim Althoff
Tobias Baumgartner