This paper introduces a large-scale, multi-label and multitask video dataset named Scenes-
Objects-Actions (SOA). Most prior video datasets are based on a predened taxonomy, which …

Recent methods for video action recognition have reached outstanding performances on
existing benchmarks. However, they tend to leverage context such as scenes or objects …

There are multiple cues in an image which reveal what action a person is performing. For
example, a jogger has a pose that is characteristic for jogging, but the scene (eg road, trail) …

Attentive video modeling is essential for action recognition in unconstrained videos due to
their rich yet redundant information over space and time. However, introducing attention in a …

Abstract Video-based Unsupervised Domain Adaptation (VUDA) method improves the
generalization of the video model enabling it to be applied to action recognition tasks in …

In recent years, a number of approaches based on 2D or 3D convolutional neural networks
(CNN) have emerged for video action recognition, achieving state-of-the-art results on …

Spatiotemporal and motion features are two complementary and crucial information for
video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn …

Recently, deep convolutional neural networks have demonstrated great effectiveness in
action recognition with both RGB and optical flow in the past decade. However, existing …

In this paper, we revive the use of old-fashioned handcrafted video representations for
action recognition and put new life into these techniques via a CNN-based hallucination …

Human activity recognition plays a central role in the development of intelligent systems for
video surveillance, public security, health care and home monitoring, where detection and …