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

## TL;DR

This paper proposes a webly supervised method to automatically generate large-scale image datasets for visual concepts, improving recognition models without extensive manual labeling, and demonstrates its effectiveness on Pascal VOC 2007.

## Contribution

It introduces a novel approach for automatic image data collection from the web to enhance visual recognition models, addressing the data scarcity issue.

## Key findings

- Outperforms existing data collection methods on Pascal VOC 2007
- Enables training robust recognition models with less manual labeling
- Shows the effectiveness of webly supervised data in visual recognition

## 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.