Convolutional neural network (CNN) and Transformer have achieved great success in
multimedia applications. However, little effort has been made to effectively and efficiently …

Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse
visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) …

Abstract Grounded Situation Recognition (GSR) is capable of recognizing and interpreting
visual scenes in a contextually intuitive way, yielding salient activities (verbs) and the …

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated
remarkable success in low-level vision tasks, such as image super-resolution, deraining …

Video captioning aims to generate natural language sentences that describe the given video
accurately. Existing methods obtain favorable generation by exploring richer visual …

Not all semantics become confusing when deploying a semantic segmentation model for
real-world scene understanding of adverse weather. The true semantics of most pixels have …

Mapping cultural significance of heritage properties in urban environment from the
perspective of the public has become an increasingly relevant process, as highlighted by the …

Frequent object counting at a specific location (FOC@ Loc) is becoming a newly emerging
but highly demanded task since the evolution of human activities can provide crucial …

Learning-based multi-view stereo (MVS) is gaining prominence as a method for 3D
reconstruction. However, existing methods in the process of feature learning fail to focus on …

As artificial intelligence (AI) has already seen numerous successful applications, the
upcoming challenge lies in how to realize artificial general intelligence (AGI). Self-learning …