Visual Learning and Recognition: Difference between revisions

Visual Learning and Recognition (view source)

819 bytes added , 8 December 2020

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 ===Flow-based Models===
 Flow-based models minimize the negative log-likelihood.
+==Attribute-based Representation==
+;Motivation
+Typically in recognition, we only predict the class of the image.
+From the category, we can guess the attributes but the category provides only limited information.
+The network cannot perform prediction on unseen new classes.
+This problem used to be called ''graceful degradation''.
+;Goal
+Learn intermediate structure with object categories.
+;Should we care about attributes in DL?
+;Why is attributes not simply supervised recognition?
+;Benefits
+* Dealing with inevitable failure.
+* We can infer things about unseen categories.
+* We can make comparison between objects or categories.
+;Datasets
+* a-Pascal
+* a-Yahoo
+* CORE
+* COCO Attributes
+Deep networks should be able to learn attributes implicitly.
+However, you don't know if it has actually learned them.
 ==Will be on the exam==