Publications

Found 132 results
Author Title [ Type(Desc)] Year
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Journal Article
Kapoor, A., Grauman K., Urtasun R., & Darrell T. (2010).  Gaussian Processes for Object Categorization. International Journal of Computer Vision. 88(2), 169-188.
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.
Miller, S., van den Berg J., Fritz M., Darrell T., Goldberg K., & Abbeel P. (2012).  A Geometric Approach to Robotic Laundry Folding. International Journal of Robotics Research. 31(2), 249-267.
Hoffman, J., Pathak D., Tzeng E., Long J., Guadarrama S., Darrell T., et al. (2016).  Large Scale Visual Recognition Through Adaptation Using Joint Representation and Multiple Instance Learning. J. Mach. Learn. Res.. 17, 4954–4984.
Andreas, J., Rohrbach M., Darrell T., & Klein D. (2016).  Learning to Compose Neural Networks for Question Answering. CoRR. abs/1601.01705,
Song, H. Oh, Fritz M., Göhring D., & Darrell T. (2015).  Learning to Detect Visual Grasp Affordance.
Song, H. Oh, Fritz M., Goehring D., & Darrell T. (2016).  Learning to Detect Visual Grasp Affordance. IEEE Transactions on Automation Science and Engineering. 13(2), 798-809.
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.
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.
Xiong, Y., Scharstein D., Chakrabarti A., Darrell T., Sun B., Saenko K., et al. (2014).  Modeling Radiometric Uncertainty for Vision with Tone-Mapped Color Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(11), 2185-2198.
Fukui, A., Park D. Huk, Yang D., Rohrbach A., Darrell T., & Rohrbach M. (2016).  Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding. CoRR. abs/1606.01847,
Saenko, K., Livescu K., Glass J. R., & Darrell T. (2009).  Multistream Articulatory Feature-Based Models for Visual Speech Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31,
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,
Girshick, R., Donahue J., Darrell T., & Malik J. (2016).  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 38(1), 142-158.
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.
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,

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