Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …

Videos represent the primary source of information for surveillance applications. Video
material is often available in large quantities but in most cases it contains little or no …

For most deep learning practitioners, sequence modeling is synonymous with recurrent
networks. Yet recent results indicate that convolutional architectures can outperform …

Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various
approaches exist, ranging from physically based over conceptual to fully data-driven …

In single image deblurring, the``coarse-to-fine''scheme, ie gradually restoring the sharp
image on different resolutions in a pyramid, is very successful in both traditional optimization …

Anomaly detection in videos refers to the identification of events that do not conform to
expected behavior. However, almost all existing methods tackle the problem by minimizing …

This paper addresses the task of detecting and recognizing human-object interactions (HOI)
in images and videos. We introduce the Graph Parsing Neural Network (GPNN), a …

Learning long-term spatial-temporal features are critical for many video analysis tasks.
However, existing video segmentation methods predominantly rely on static image …

Time series are widely used as signals in many classification/regression tasks. It is
ubiquitous that time series contains many missing values. Given multiple correlated time …

Learning long-term spatial-temporal features are critical for many video analysis tasks.
However, existing video segmentation methods predominantly rely on static image …