As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

We present a novel unsupervised deep learning framework for anomalous event detection
in complex video scenes. While most existing works merely use hand-crafted appearance …

We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for
3D Dense Head Alignment in-the-wild. It contains annotations of over 3.5 K landmarks that …

Attribute recognition, particularly facial, extracts many labels for each image. While some
multi-task vision problems can be decomposed into separate tasks and stages, eg, training …

The development of smartphones equipped with accelerometers gives a promising way for
researchers to accurately recognize an individual's physical activity in order to better …

We present an end-to-end head-pose estimation network designed to predict Euler angles
through the full range head yaws from a single RGB image. Existing methods perform well …

This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …

Deep learning for video classification and captioning Page 1 IPART MULTIMEDIA
CONTENT ANALYSIS Page 2 Page 3 1Deep Learning for Video Classification and …

Robust action recognition under viewpoint changes has received considerable attention
recently. To this end, self-similarity matrices (SSMs) have been found to be effective view …