Learning Visual N-Grams from Web Data

Understand

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.
• This paper explores the training of image-recognition systems on large numbers of images and associated user comments.
• In particular, we develop visual n-gram models that can predict arbitrary phrases that are relevant to the content of an image.