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Object Detection and Localization Using Local and Global Features.

, , , and . Toward Category-Level Object Recognition, volume 4170 of Lecture Notes in Computer Science, page 382-400. Springer, (2006)

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Markov and Hidden Markov Models. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)Machine learning : a probabilistic perspective. MIT Press, Cambridge, Mass. u.a., (2013)An introduction to graphical model. Intel Research Technical Report, (2001)Generative models for discrete data. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)Bayesian statistics. Machine Learning: A Probabilistic Approach, MIT Press, Cambridge, MA, USA, (2012)Modeling changing dependency structure in multivariate time series., and . ICML, volume 227 of ACM International Conference Proceeding Series, page 1055-1062. ACM, (2007)A coupled HMM for audio-visual speech recognition., , , , , and . ICASSP, page 2013-2016. IEEE, (2002)Accelerated training of conditional random fields with stochastic gradient methods., , , and . ICML, volume 148 of ACM International Conference Proceeding Series, page 969-976. ACM, (2006)Object Detection and Localization Using Local and Global Features., , , and . Toward Category-Level Object Recognition, volume 4170 of Lecture Notes in Computer Science, page 382-400. Springer, (2006)The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction., , , , and . CoRR, (2019)