Kris M. Kitani mainly focuses on Artificial intelligence, Computer vision, Machine learning, Feature extraction and Trajectory. The study incorporates disciplines such as Human–computer interaction and Pattern recognition in addition to Artificial intelligence. His study focuses on the intersection of Human–computer interaction and fields such as Deep learning with connections in the field of Image quality and Visualization.
His work on Object detection and Video tracking as part of general Computer vision research is frequently linked to Set and Detector, bridging the gap between disciplines. His studies examine the connections between Machine learning and genetics, as well as such issues in Optimal control, with regards to Smoothing, Kernel and Bellman equation. The various areas that Kris M. Kitani examines in his Feature extraction study include Contextual image classification, Histogram and Feature.
His main research concerns Artificial intelligence, Computer vision, Human–computer interaction, Machine learning and Pattern recognition. His research on Artificial intelligence frequently links to adjacent areas such as Context. His studies deal with areas such as Overfitting and Benchmark as well as Context.
Much of his study explores Computer vision relationship to Wearable computer. His Human–computer interaction research incorporates themes from Transfer of learning and Personalization. His work on Leverage as part of general Machine learning research is often related to Trajectory, thus linking different fields of science.
His primary areas of study are Artificial intelligence, Computer vision, Feature, Machine learning and Pattern recognition. His research in Artificial intelligence tackles topics such as Context which are related to areas like Benchmark. His work in Computer vision addresses subjects such as Robot, which are connected to disciplines such as Robotic arm and Eye tracking.
His research integrates issues of Artificial neural network, Feature extraction and Image description in his study of Feature. His study looks at the intersection of Pattern recognition and topics like Generative model with Regret. His Video tracking study incorporates themes from Kalman filter and Pipeline.
Kris M. Kitani focuses on Artificial intelligence, Feature learning, Feature, Pattern recognition and Machine learning. His Artificial intelligence research is multidisciplinary, relying on both Context and Computer vision. His research in Context intersects with topics in Representation and Benchmark.
His Human motion and Motion prediction study, which is part of a larger body of work in Computer vision, is frequently linked to Spec# and Sequence, bridging the gap between disciplines. His study in Feature is interdisciplinary in nature, drawing from both Feature extraction and Feature vector. His work carried out in the field of Machine learning brings together such families of science as Kalman filter and State.
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The Visual Object Tracking VOT2017 Challenge Results
Matej Kristan;Ales Leonardis;Jiri Matas;Michael Felsberg.
international conference on computer vision (2017)
Kris M. Kitani;Brian D. Ziebart;James Andrew Bagnell;Martial Hebert.
european conference on computer vision (2012)
Pixel-Level Hand Detection in Ego-centric Videos
Cheng Li;Kris M. Kitani.
computer vision and pattern recognition (2013)
Fast unsupervised ego-action learning for first-person sports videos
Kris M. Kitani;Takahiro Okabe;Yoichi Sato;Akihiro Sugimoto.
computer vision and pattern recognition (2011)
Going Deeper into First-Person Activity Recognition
Minghuang Ma;Haoqi Fan;Kris M. Kitani.
computer vision and pattern recognition (2016)
NavCog: a navigational cognitive assistant for the blind
Dragan Ahmetovic;Cole Gleason;Chengxiong Ruan;Kris Kitani.
human computer interaction with mobile devices and services (2016)
R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting
Nicholas Rhinehart;Kris M. Kitani;Paul Vernaza.
european conference on computer vision (2018)
Learning scene-specific pedestrian detectors without real data
Hironori Hattori;Vishnu Naresh Boddeti;Kris Kitani;Takeo Kanade.
computer vision and pattern recognition (2015)
PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings
Nicholas Rhinehart;Rowan Mcallister;Kris Kitani;Sergey Levine.
international conference on computer vision (2019)
Deep Supervised Hashing with Triplet Labels
Xiaofang Wang;Yi Shi;Kris M. Kitani.
asian conference on computer vision (2016)
Profile was last updated on December 6th, 2021.
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