Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to
compensate for their inherent limitations. However, existing MML methods often optimize a …

Classifying videos according to content semantics is an important problem with a wide range
of applications. In this paper, we propose a hybrid deep learning framework for video …

In this paper, we study the challenging problem of categorizing videos according to high-
level semantics such as the existence of a particular human action or a complex event …

We present a method for gesture detection and localisation based on multi-scale and multi-
modal deep learning. Each visual modality captures spatial information at a particular spatial …

Pooling plays an important role in generating a discriminative video representation. In this
paper, we propose a new semantic pooling approach for challenging event analysis tasks …

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 …

This paper aims at learning to score the figure skating sports videos. To address this task,
we propose a deep architecture that includes two complementary components, ie, Self …

Most traditional approaches classify hyperspectral image (HSI) pixels relying only on the
spectral values of the input channels. However, the spatial context around a pixel is also …

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 …

Videos contain very rich semantics and are intrinsically multimodal. In this paper, we study
the challenging task of classifying videos according to their high-level semantics such as …