WebVision Database: Visual Learning and Understanding from Web Data

TLDR

The study investigates training visual recognition models using large‑scale noisy web data. The authors construct WebVision, a 2.4‑million‑image dataset crawled from the web using 1,000 ILSVRC 2012 concepts, including associated metadata and human‑annotated validation and test sets. Experiments show that noisy web images can train competitive CNNs, and models from WebVision generalize as well or better than those trained on ILSVRC 2012 when transferred, revealing a dataset‑bias domain‑adaptation challenge and positioning WebVision as a large benchmark for visual domain adaptation.

Abstract

In this paper, we present a study on learning visual recognition models from large scale noisy web data. We build a new database called WebVision, which contains more than $2.4$ million web images crawled from the Internet by using queries generated from the 1,000 semantic concepts of the benchmark ILSVRC 2012 dataset. Meta information along with those web images (e.g., title, description, tags, etc.) are also crawled. A validation set and test set containing human annotated images are also provided to facilitate algorithmic development. Based on our new database, we obtain a few interesting observations: 1) the noisy web images are sufficient for training a good deep CNN model for visual recognition; 2) the model learnt from our WebVision database exhibits comparable or even better generalization ability than the one trained from the ILSVRC 2012 dataset when being transferred to new datasets and tasks; 3) a domain adaptation issue (a.k.a., dataset bias) is observed, which means the dataset can be used as the largest benchmark dataset for visual domain adaptation. Our new WebVision database and relevant studies in this work would benefit the advance of learning state-of-the-art visual models with minimum supervision based on web data.