This paper investigates a novel Bilinear attention (Bi-attention) block, which discovers and
uses second order statistical information in an input feature map, for the purpose of person …

Object tracking belongs to active research areas in computer vision. We are interested in
matching-based trackers exploiting deep machine learning known as Siamese trackers …

In recent years, rapid advancements in both hardware and software technologies have
resulted in the ability to execute artificial intelligence (AI) algorithms on low-resource …

The classifiers that return compact acceptance regions are crucial for the success in
anomaly detection and open set recognition settings since we have to determine and reject …

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 …

Class label noise is a critical component of data quality that directly inhibits the predictive
performance of machine learning algorithms. While many data-level and algorithm-level …

Person re-identification (re-id) addresses the problem of whether “a query image
corresponds to an identity in the database” and is believed to play a fundamental role in …

This paper generalizes the Attention in Attention (AiA) mechanism, in P. Fang et al., 2019 by
employing explicit mapping in reproducing kernel Hilbert spaces to generate attention …

Exploration into quantum machine learning has grown tremendously in recent years due to
the ability of quantum computers to speed up classical programs. However, these ef-forts …

In this paper, we propose a new deep neural network classifier that simultaneously
maximizes the inter-class separation and minimizes the intra-class variation by using the …