Learning Visual N-Grams from Web Data

ICCV 2017 Ang LiAllan JabriArmand JoulinLaurens van der Maaten

Real-world image recognition systems need to recognize tens of thousands of classes that constitute a plethora of visual concepts. The traditional approach of annotating thousands of images per class for training is infeasible in such a scenario, prompting the use of webly supervised data... (read more)

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