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Models of object vision have been of great interest in computer vision andvisual neuroscience. During the last decades, several models have beendeveloped to extract visual features from images for object recognition tasks.Some of these were inspired by the hierarchical structure of primate visualsystem, and some others were engineered models. The models are varied inseveral aspects: models that are trained by supervision, models trained withoutsupervision, and models (e.g. feature extractors) that are fully hard-wired anddo not need training. Some of the models come with a deep hierarchicalstructure consisting of several layers, and some others are shallow and comewith only one or two layers of processing. More recently, new models have beendeveloped that are not hand-tuned but trained using millions of images, throughwhich they learn how to extract informative task-related features. Here I willsurvey all these different models and provide the reader with an intuitive, aswell as a more detailed, understanding of the underlying computations in eachof the models.

What you need to know about the state-of-the-art computational models of object-vision: A tour through the models