His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Segmentation. His Artificial intelligence study frequently links to adjacent areas such as Natural language processing. His Pattern recognition study combines topics from a wide range of disciplines, such as Object detection and Feature.
The Machine learning study combines topics in areas such as Adversarial system, Training set and Domain. His biological study spans a wide range of topics, including Pixel, Image, Pascal and Feature learning. His Pascal research incorporates themes from Layer and Inference.
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Object. As a part of the same scientific study, he usually deals with the Artificial intelligence, concentrating on Natural language processing and frequently concerns with Closed captioning. His Pattern recognition research incorporates elements of Contextual image classification and Feature.
He has researched Computer vision in several fields, including Robot and Representation. The various areas that he examines in his Machine learning study include Training set and Inference. His Object detection study integrates concerns from other disciplines, such as Pascal and Minimum bounding box.
His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Sequence and Benchmark. His Artificial intelligence study combines topics in areas such as Computer vision and Natural language processing. His Machine learning research is multidisciplinary, relying on both Generalization and Baseline.
Trevor Darrell interconnects Domain adaptation, Entropy and Inference in the investigation of issues within Pattern recognition. His Benchmark study incorporates themes from Natural language and Set. His Segmentation research focuses on subjects like Structure, which are linked to Representation.
Trevor Darrell mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Classifier and Visualization. Artificial intelligence and Computer vision are frequently intertwined in his study. As part of one scientific family, Trevor Darrell deals mainly with the area of Machine learning, narrowing it down to issues related to the Generalization, and often Robotic arm, Link, Modularity, Baseline and Classifier.
The study incorporates disciplines such as Cognitive neuroscience of visual object recognition, Receptive field and Closed captioning in addition to Pattern recognition. Trevor Darrell focuses mostly in the field of Classifier, narrowing it down to matters related to Relational reasoning and, in some cases, Knowledge extraction and Theoretical computer science. His Visualization research integrates issues from Vocabulary, Pooling and Transformer.
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Fully convolutional networks for semantic segmentation
Jonathan Long;Evan Shelhamer;Trevor Darrell.
computer vision and pattern recognition (2015)
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Ross Girshick;Jeff Donahue;Trevor Darrell;Jitendra Malik.
computer vision and pattern recognition (2014)
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia;Evan Shelhamer;Jeff Donahue;Sergey Karayev.
acm multimedia (2014)
Pfinder: real-time tracking of the human body
C.R. Wren;A. Azarbayejani;T. Darrell;A.P. Pentland.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)
Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue;Lisa Anne Hendricks;Sergio Guadarrama;Marcus Rohrbach.
computer vision and pattern recognition (2015)
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue;Yangqing Jia;Oriol Vinyals;Judy Hoffman.
international conference on machine learning (2014)
Fully Convolutional Networks for Semantic Segmentation
Evan Shelhamer;Jonathan Long;Trevor Darrell.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
The pyramid match kernel: discriminative classification with sets of image features
K. Grauman;T. Darrell.
international conference on computer vision (2005)
End-to-end training of deep visuomotor policies
Sergey Levine;Chelsea Finn;Trevor Darrell;Pieter Abbeel.
Journal of Machine Learning Research (2016)
Adapting visual category models to new domains
Kate Saenko;Brian Kulis;Mario Fritz;Trevor Darrell.
european conference on computer vision (2010)
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