Publications

Found 132 results
Author Title Type [ Year(Asc)]
Filters: Author is Trevor Darrell  [Clear All Filters]
2015
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.
Zhang, N., Shelhamer E., Gao Y., & Darrell T. (2015).  Fine-grained pose prediction, normalization, and recognition. CoRR. abs/1511.07063,
Long, J., Shelhamer E., & Darrell T. (2015).  Fully Convolutional Networks for Semantic Segmentation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3431-3440.
Song, H. Oh, Girshick R., Zickler S., Geyer C., Felzenszwalb P., & Darrell T. (2015).  Generalized Sparselet Models for Real-Time Multiclass Object Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37, 1001-1012.
Feng, J., & Darrell T. (2015).  Learning The Structure of Deep Convolutional Networks. The IEEE International Conference on Computer Vision (ICCV).
Song, H. Oh, Fritz M., Göhring D., & Darrell T. (2015).  Learning to Detect Visual Grasp Affordance.
Finn, C., Tan X. Yu, Duan Y., Darrell T., Levine S., & Abbeel P. (2015).  Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders. arXiv.
Donahue, J., Hendricks L. Anne, Guadarrama S., Rohrbach M., Venugopalan S., Saenko K., et al. (2015).  Long-Term Recurrent Convolutional Networks for Visual Recognition and Description.
Darrell, T., Kloft M., Pontil M., Rätsch G., & Rodner E. (2015).  Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). Dagstuhl Reports. 5, 18–55.
Narihira, T., Borth D., Yu S. X., Ni K., & Darrell T. (2015).  Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets.
Fisher, J., Darrell T., Galup L., How J., Krause A., & Soatto S. (2015).  Nonparametric Representations for Integrated Inference, Control, and Sensing.
Beijbom, O., Hoffman J., Yao E., Darrell T., Rodriguez-Ramirez A., Gonzalez-Rivero M., et al. (2015).  Quantification in-the-wild: data-sets and baselines. CoRR. abs/1510.04811,
Chu, V., McMahon I., Riano L., McDonald C. G., He Q., Perez-Tejada J. Martinez, et al. (2015).  Robotic Learning of Haptic Adjectives Through Physical Interaction. Robot. Auton. Syst.. 63(P3), 279–292.
Shelhamer, E., Barron J. T., & Darrell T. (2015).  Scene Intrinsics and Depth From a Single Image. The IEEE International Conference on Computer Vision (ICCV) Workshops.
Venugopalan, S., Rohrbach M., Donahue J., Mooney R., Darrell T., & Saenko K. (2015).  Sequence to Sequence - Video to Text. The IEEE International Conference on Computer Vision (ICCV).
Tzeng, E., Hoffman J., Darrell T., & Saenko K. (2015).  Simultaneous Deep Transfer Across Domains and Tasks. The IEEE International Conference on Computer Vision (ICCV). 4068-4076.
Mrowca, D., Rohrbach M., Hoffman J., Hu R., Saenko K., & Darrell T. (2015).  Spatial Semantic Regularisation for Large Scale Object Detection. The IEEE International Conference on Computer Vision (ICCV).
Tzeng, E., Devin C., Hoffman J., Finn C., Peng X., Levine S., et al. (2015).  Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments. CoRR. abs/1511.07111,
Guadarrama, S., Rodner E., Saenko K., & Darrell T. (2015).  Understanding object descriptions in robotics by open-vocabulary object retrieval and detection. The International Journal of Robotics Research. 35(1-3), 265-280.
Garg, A., Krishnan S., Murali A., Pokorny F. T., Abbeel P., Darrell T., et al. (2015).  On Visual Feature Representations for Transition State Learning in Robotic Task Demonstrations. 44,
2014
Karayev, S., Fritz M., & Darrell T. (2014).  Anytime Recognition of Objects and Scenes.
Jia, Y., Shelhamer E., Donahue J., Karayev S., Long J., Girshick R., et al. (2014).  Caffe: Convolutional Architecture for Fast Feature Embedding. 675-678.
Hoffman, J., Darrell T., & Saenko K. (2014).  Continuous Manifold Based Adaptation for Evolving Visual Domains.
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.
Iandola, F., Moskewicz M., Karayev S., Girshick R., Darrell T., & Keutzer K. (2014).  DenseNet: Implementing Efficient ConvNet Descriptor Pyramids.

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