# Extracting Visual Knowledge from the Internet: Making Sense of Image Data Source: [Extracting Visual Knowledge from the Internet: Making Sense of Image Data]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:38:30.146Z Authors: Yazhou Yao, Jian Zhang, Xiansheng Hua, Fumin Shen, Zhenmin Tang Published: 2019-06-07T16:35:33.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()