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Recent successes in visual recognition can be primarily attributed to featurerepresentation, learning algorithms, and the ever-increasing size of labeledtraining data. Extensive research has been devoted to the first two, but muchless attention has been paid to the third. Due to the high cost of manuallabeling, the size of recent efforts such as ImageNet is still relatively smallin respect to daily applications. In this work, we mainly focus on how toautomatically generate identifying image data for a given visual concept on avast scale. With the generated image data, we can train a robust recognitionmodel for the given concept. We evaluate the proposed webly supervised approachon the benchmark Pascal VOC 2007 dataset and the results demonstrates thesuperiority of our proposed approach in image data collection.

Extracting Visual Knowledge from the Internet: Making Sense of Image Data