WebVision Challenge: Visual Learning and Understanding With Web Data

Abstract

We present the 2017 WebVision Challenge, a public image recognition challenge designed for deep learning based on web images without instance-level human annotation. Following the spirit of previous vision challenges, such as ILSVRC, Places2 and PASCAL VOC, which have played critical roles in the development of computer vision by contributing to the community with large scale annotated data for model designing and standardized benchmarking, we contribute with this challenge a large scale web images dataset, and a public competition with a workshop co-located with CVPR 2017. The WebVision dataset 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 is also included. A validation set and test set containing human annotated images are also provided to facilitate algorithmic development. The 2017 WebVision challenge consists of two tracks, the image classification task on WebVision test set, and the transfer learning task on PASCAL VOC 2012 dataset. In this paper, we describe the details of data collection and annotation, highlight the characteristics of the dataset, and introduce the evaluation metrics.

project page: http://www.vision.ee.ethz.ch/webvision/

References in corpus (8)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Two-Stream Convolutional Networks for Action Recognition in Videos
• Unsupervised Visual Representation Learning by Context Prediction
• Unsupervised Learning of Visual Representations using Videos
• Places: An Image Database for Deep Scene Understanding
• Learning to See by Moving
• Webly Supervised Learning of Convolutional Networks

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