Recent years have witnessed the resurgence of knowledge engineering which is featured
by the fast growth of knowledge graphs. However, most of existing knowledge graphs are …

Abstract Convolutional Neural Networks (CNNs) are commonly assumed to be invariant to
small image transformations: either because of the convolutional architecture or because …

We present an approach to utilize large amounts of web data for learning CNNs. Specifically
inspired by curriculum learning, we present a two-step approach for CNN training. First, we …

This paper investigates the problem of modeling Internet images and associated text or tags
for tasks such as image-to-image search, tag-to-image search, and image-to-tag search …

Abstract We propose NEIL (Never Ending Image Learner), a computer program that runs 24
hours per day and 7 days per week to automatically extract visual knowledge from Internet …

Image clustering has been a critical preprocessing step for vision tasks, eg, visual concept
discovery, content-based image retrieval. Conventional image clustering methods use …

This paper addresses the problem of clustering a very large number of photos (ie hundreds
of millions a day) in a stream into millions of clusters. This is particularly important as the …

We demonstrate that is it possible to automatically find representative example images of a
specified object category. These canonical examples are perhaps the kind of images that …

The field of Computer Vision (CV) is increasingly shifting towards``high-level''visual
sensemaking tasks, yet the exact nature of these tasks remains unclear and tacit. This …

This article presents an approach for modeling landmarks based on large-scale, heavily
contaminated image collections gathered from the Internet. Our system efficiently combines …