Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data
(eg, images, texts, or data collected from different sensors), feature engineering (eg …

Abstract We present Multiscale Vision Transformers (MViT) for video and image recognition,
by connecting the seminal idea of multiscale feature hierarchies with transformer models …

The availability of large-scale image captioning and visual question answering datasets has
contributed significantly to recent successes in vision-and-language pre-training. However …

Attention mechanisms are now widely used in image captioning models. However, most
attention models only focus on visual features. When generating syntax related words, little …

A two-dimensional cross-mode hyperchaotic map based on logistic and sine maps (2D-
CLSS) is presented. The hyperchaotic map consists of a logistic map and two sine maps …

Recently, vision transformers (ViTs) have been investigated in fine-grained visual
recognition (FGVC) and are now considered state of the art. However, most ViT-based works …

An intelligent transportation system (ITS) plays an important role in public transport
management, security and other issues. Traffic flow detection is an important part of the ITS …

Brain tumor represents one of the most fatal cancers around the world. It is common cancer
in adults and children. It has the lowest survival rate and various types depending on their …

Modeling feature interactions is of crucial significance to high-quality feature engineering on
multifiled sparse data. At present, a series of state-of-the-art methods extract cross features …