Publications

Found 132 results
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D
Hoffman, J., Saenko K., Kulis B., & Darrell T. (2012).  Discovering Latent Domains for Multisource Domain Adaptation. 702-715.
Hoffman, J., Pathak D., Darrell T., & Saenko K. (2015).  Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2883-2891.
Althoff, T., Song H. Oh, & Darrell T. (2012).  Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition.
Iandola, F., Moskewicz M., Karayev S., Girshick R., Darrell T., & Keutzer K. (2014).  DenseNet: Implementing Efficient ConvNet Descriptor Pyramids.
Girshick, R., Iandola F., Darrell T., & Malik J. (2015).  Deformable Part Models are Convolutional Neural Networks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 437-446.
Zhang, N., Farrell R., Iandola F., & Darrell T. (2013).  Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction.
Finn, C., Tan X. Yu, Duan Y., Darrell T., Levine S., & Abbeel P. (2016).  Deep spatial autoencoders for visuomotor learning. IEEE International Conference on Robotics and Automation (ICRA). 512-519.
Gao, Y., Hendricks L. Anne, Kuchenbecker K. J., & Darrell T. (2016).  Deep learning for tactile understanding from visual and haptic data. IEEE International Conference on Robotics and Automation (ICRA). 536-543.
Andreas, J., Rohrbach M., Darrell T., & Klein D. (2016).  Deep compositional question answering with neural module networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Hendricks, L. Anne, Venugopalan S., Rohrbach M., Mooney R., Saenko K., & Darrell T. (2016).  Deep Compositional Captioning: Describing Novel Object Categories Without Paired Training Data. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1-10.
Donahue, J., Jia Y., Vinyals O., Hoffman J., Zhang N., Tzeng E., et al. (2014).  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.
Krahenbuhl, P., Doersch C., Donahue J., & Darrell T. (2015).  Data-dependent Initializations of Convolutional Neural Networks. CoRR. abs/1511.06856,
C
Hoffman, J., Gupta S., Leong J., Guadarrama S., & Darrell T. (2016).  Cross-modal adaptation for RGB-D detection. IEEE International Conference on Robotics and Automation (ICRA). 5032-5039.
C. Christoudias, M., Urtasun R., Kapoor A., & Darrell T. (2009).  Co-Training with Noisy Perceptual Observations. 2844-2851.
C. Christoudias, M., Urtasun R., Kapoor A., & Darrell T. (2009).  Co-Training with Noisy Perceptual Observations.
Hoffman, J., Darrell T., & Saenko K. (2014).  Continuous Manifold Based Adaptation for Evolving Visual Domains.
Pathak, D., Krahenbuhl P., Donahue J., Darrell T., & Efros A. A. (2016).  Context Encoders: Feature Learning by Inpainting. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2536-2544.
Pathak, D., Kraehenbuehl P., Yu S. X., & Darrell T. (2015).  Constrained Structured Regression with Convolutional Neural Networks. CoRR. abs/1511.07497,
Pathak, D., Krahenbuhl P., & Darrell T. (2015).  Constrained Convolutional Neural Networks for Weakly Supervised Segmentation. The IEEE International Conference on Computer Vision (ICCV). 1796-1804.
Jia, Y., Vinyals O., & Darrell T. (2013).  On Compact Codes for Spatially Pooled Features.
Gao, Y., Beijbom O., Zhang N., & Darrell T. (2016).  Compact Bilinear Pooling. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 317-326.
Janoch, A., Karayev S., Jia Y., Barron J. T., Fritz M., Saenko K., et al. (2011).  A Category-Level 3-D Object Dataset: Putting the Kinect to Work. 1168-1174.
Jia, Y., Shelhamer E., Donahue J., Karayev S., Long J., Girshick R., et al. (2014).  Caffe: Convolutional Architecture for Fast Feature Embedding. 675-678.

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