  Recent successes in visual recognition can be primarily attributed to feature
representation, learning algorithms, and the ever-increasing size of labeled
training data. Extensive research has been devoted to the first two, but much
less attention has been paid to the third. Due to the high cost of manual
labeling, the size of recent efforts such as ImageNet is still relatively small
in respect to daily applications. In this work, we mainly focus on how to
automatically generate identifying image data for a given visual concept on a
vast scale. With the generated image data, we can train a robust recognition
model for the given concept. We evaluate the proposed webly supervised approach
on the benchmark Pascal VOC 2007 dataset and the results demonstrates the
superiority of our proposed approach in image data collection.
