Understanding emotion in context is a rising hotspot in the computer vision community.
Existing methods lack reliable context semantics to mitigate uncertainty in expressing …

Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue
machines with the ability to comprehend and respond to human emotions. Central to this …

Facial expression recognition has become a hot issue in the field of artificial intelligence. So,
we collect literature on facial expression recognition. First, methods based on machine …

Due to the lack of properly annotated medical data, exploring the generalization capability of
the deep model is becoming a public concern. Zero-shot learning (ZSL) has emerged in …

Retrieving video moments from an untrimmed video given a natural language as the query
is a challenging task in both academia and industry. Although much effort has been made to …

In the field of facial expression recognition, deep learning is extensively used. However,
insufficient and unbalanced facial training data in available public databases is a major …

One of the main challenges in facial expression recognition (FER) is to address the
disturbance caused by various disturbing factors, including common ones (such as identity …

Nodule segmentation plays a vital role in the detection and diagnosis for lung cancer.
Nevertheless, manual segmentation by radiologists can be time-consuming and labor …

Human emotion synthesis is a crucial aspect of affective computing. It involves using
computational methods to mimic and convey human emotions through various modalities …

Performing high-accuracy object detection under low-illumination conditions is a difficult
task. The fusion of infrared (IR) and visible images can provide a great reference value for …