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Mitchell, J. F. & Leopold, D. A. The marmoset monkey as a model for visual neuroscience.
The third one is to propose an objective model for visual discomfort.
This research goal is to design a model for visual feedback controller.
We then propose an explicitly compositional model for visual question answering that internally converts questions to functional programs, and executes these programs by composing neural modules.
We then propose an explicitly compositional model for visual question an- swering that internally converts questions to functional programs, and executes these programs by composing neural modules.
We therefore used multisite extracellular recordings to define the repertoire of visual features represented in the LGN of mouse, an emerging model for visual processing.
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Learning generic prior models for visual computation.
This approach does not require intermediate representations, planning procedures, or training different models for visual and language reasoning.
Implications for the development of models for visual information retrieval, and for the design of Web search engines are discussed.
Researchers at ICSI and UC Berkeley are developing new representation learning models for visual detection, leveraging advances in discriminatively trained convolutional neural networks.
In target tracking, reliable trajectories which are obtained by incremental linking of detection responses frame by frame are used to build the appearance models for visual tracking.
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