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• W2089823622 abstract "We present Max-Margin Boltzmann Machines (MMBMs) for object segmentation. MMBMs are essentially a class of Conditional Boltzmann Machines that model the joint distribution of hidden variables and output labels conditioned on input observations. In addition to image-to-label connections, we build direct image-to-hidden connections to facilitate global shape prediction, and thus derive a simple Iterated Conditional Modes algorithm for efficient maximum a posteriori inference. We formulate a max-margin objective function for discriminative training, and analyze the effects of different margin functions on learning. We evaluate MMBMs using three datasets against state-of-the-art methods to demonstrate the strength of the proposed algorithms." @default.
• W2089823622 created "2016-06-24" @default.
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• W2089823622 date "2014-06-01" @default.
• W2089823622 modified "2023-09-25" @default.
• W2089823622 title "Max-Margin Boltzmann Machines for Object Segmentation" @default.
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• W2089823622 doi "https://doi.org/10.1109/cvpr.2014.48" @default.
• W2089823622 hasPublicationYear "2014" @default.
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