Peijie Yin

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—In recent years, the interdisciplinary research between information science and neuroscience has been a hotspot. Many biologically inspired visual and motor computational models have been proposed for visual recognition tasks and visuomotor coordination tasks. In this paper, based on recent biological findings, we proposed a new model to mimic visual(More)
—Integration between biology and information science benefits both fields. Many related models have been proposed, such as computational visual cognition models, computational motor control models, integrations of both and so on. In general, the robustness and precision of recognition is one of the key problems for object recognition models. In this paper,(More)
An object often has many distinct manifestations in computer vision, which brings a great challenge to utilizing more comprehensive information. Inspired by some biological researches about edge sensitivity and global structure priority, our key insight is to establish unified transfer classification network with shared contour information. Combining two(More)
Generalization ability is widely acknowledged as one of the most important criteria to evaluate the quality of unsupervised models. The objective of our research is to find a better dropout method to improve the generalization ability of convolutional deep belief network (CDBN), an unsupervised learning model for vision tasks. In this paper, the phenomenon(More)
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