We address temporal action localization in untrimmed long videos. This is important
because videos in real applications are usually unconstrained and contain multiple action …

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 …

This paper introduces WILDCAT, a deep learning method which jointly aims at aligning
image regions for gaining spatial invariance and learning strongly localized features. Our …

In this paper, we focus on complex event detection in internet videos while also providing
the key evidences of the detection results. Convolutional Neural Networks (CNNs) have …

This paper studies deep network architectures to address the problem of video classification.
A multi-stream framework is proposed to fully utilize the rich multimodal information in …

In this paper, we introduce a novel framework for WEakly supervised Learning of Deep
cOnvolutional neural Networks (WELDON). Our method is dedicated to automatically …

Highlight detection has the potential to significantly ease video browsing, but existing
methods often suffer from expensive supervision requirements, where human viewers must …

Research on event-based processing and analysis of media is receiving an increasing
attention from the scientific community due to its relevance for an abundance of applications …

Corporate bankruptcy prediction is an interesting and important research topic that can be
conceived in many practical applications. Recently, machine learning based methods have …

Videos are inherently multimodal. This paper studies the problem of exploiting the abundant
multimodal clues for improved video classification performance. We introduce a novel hybrid …