The past decade has witnessed a rapid proliferation of video cameras in all walks of life and
has resulted in a tremendous explosion of video content. Several applications such as …

Attention networks in multimodal learning provide an efficient way to utilize given visual
information selectively. However, the computational cost to learn attention distributions for …

Pooling second-order local feature statistics to form a high-dimensional bilinear feature has
been shown to achieve state-of-the-art performance on a variety of fine-grained …

This paper solves the speed bottleneck of deformable part model (DPM), while maintaining
the accuracy in detection on challenging datasets. Three prohibitive steps in cascade …

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine
learning for classification and regression problems. It relies on vectorized input data …

This paper addresses a spatiotemporal pattern recognition problem. The main purpose of
this study is to find a right representation and matching of action video volumes for …

In many classification problems such as electroencephalogram (EEG) classification and
image classification, the input features are naturally represented as matrices rather than …

In this paper, we exploit the advantages of tensorial representations and propose several
tensor learning models for regression. The model is based on the canonical/parallel-factor …

We describe an algorithm for learning bilinear SVMs. Bilinear classifiers are a discriminative
variant of bilinear models, which capture the dependence of data on multiple factors. Such …

Electroencephalogram (EEG) signals are of complex structure and can be naturally
represented as matrices. Classification is one of the most important steps for EEG signal …