Multi-label learning is a rapidly growing research area that aims to predict multiple labels
from a single input data point. In the era of big data, tasks involving multi-label classification …

Effective road maintenance can not only achieve a balance between limited resources and
long-term high-efficiency performance of road but also reduce the loss of life and property …

To combat cybercrimes and maintain financial security for the blockchain ecosystem,“know
your customer”(KYC) is an essential and also challenging process due to the pseudonymity …

With the increasing automation and integration of equipment, it is urgent to carry out
anomaly detection (AD) for the large-scale system to ensure security, in virtue of Industrial …

Automated multimodal emotion recognition has become an emerging but challenging
research topic in the fields of affective learning and sentiment analysis. The existing works …

Existing fine-grained image recognition works have attempted to dig into low-level details for
emphasizing subtle discrepancies among sub-categories. However, a potential limitation of …

Abstract Residual Network (ResNet) achieves deeper and wider networks with high-
performance gains, representing a powerful convolutional neural network architecture. In …

Multi-label image classification is a fundamental and practical task, which aims to assign
multiple possible labels to an image. In recent years, many deep convolutional neural …

Multi-Label Image Classification (MLIC) is a fundamental yet challenging task which aims to
recognize multiple labels from given images. The key to solve MLIC lies in how to accurately …

Recently, surveillance video diagnosis has attracted increasing interest for generating real-
time alarms related to camera failure in video surveillance systems. The existing …