As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for developing new …

This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

The canonical approach to video-and-language learning (eg, video question answering)
dictates a neural model to learn from offline-extracted dense video features from vision …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

Carcinoma is a primary source of morbidity in women globally, with metastatic disease
accounting for most deaths. Its early discovery and diagnosis may significantly increase the …

In this paper, we introduce a novel problem of audio-visual event localization in
unconstrained videos. We define an audio-visual event as an event that is both visible and …

Automatic diagnosing lung cancer from computed tomography scans involves two steps:
detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary …

Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …

Accurate identification and localization of abnormalities from radiology images play an
integral part in clinical diagnosis and treatment planning. Building a highly accurate …

Abstract In conventional Magnetic Resonance (MR) image based methods, two stages are
often involved to capture brain structural information for disease diagnosis, ie, 1) manually …