Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

The performance of a computer vision model depends on the size and quality of its training
data. Recent studies have unveiled previously-unknown composition biases in common …

As one of the most important affective signals, facial affect analysis (FAA) is essential for
develo** human-computer interaction systems. Early methods focus on extracting …

Capturing the dependencies among different facial action units (AU) is extremely important
for the AU detection task. Many studies have employed graph-based deep learning methods …

Message passing neural network has been an effective method to represent dependencies
among nodes by propagating messages. However, most of message passing algorithms …

Multi-label classification aims to learn classification models from instances associated with
multiple labels. It is pivotal to learn and utilize the label dependence among multiple labels …

Abstract Tunnel Boring Machine (TBM) construction, particularly with closed-face TBMs,
faces uncertainties due to the inability of the operator to directly observe the ground ahead …

This paper proposes a robust diagnosis method of photovoltaic (PV) array faults considering
label errors in training data. First, the online data of PV systems, including the sequences of …

Deep models for facial expression recognition achieve high performance by training on
large-scale labeled data. However, publicly available datasets contain uncertain facial …

As a new programming paradigm, deep learning (DL) has achieved impressive performance
in areas such as image processing and speech recognition, and has expanded its …