Transportation activity recognition (TAR) provides valuable support for intelligent
transportation applications, such as urban transportation planning, driving behavior …

The ability to understand and predict the flows of people in cities is crucial for the planning of
transportation systems and other urban infrastructures. Deep-learning approaches are …

Diffusion models are widely used in image generation because they can generate high-
quality and realistic samples. This is in contrast to generative adversarial networks (GANs) …

Human motion generation has paramount importance in computer animation. It is a
challenging generative temporal modelling task due to the vast possibilities of human …

Urban mobility stands as a fundamental element worthy of consideration by both society and
its leaders. Often, decisions in this realm are made by governing figures without duly …

Normalizing Flows (NFs) have gained popularity among deep generative models due to
their ability to provide exact likelihood estimation and efficient sampling. However, a crucial …

With the Earth's temperature rising and abnormal weather events becoming frequent, the
mechanisms of precipitation formation have become increasingly complex, leading to more …

The class of recurrent mixture density networks is an important class of probabilistic models
used extensively in sequence modeling and sequence-to-sequence mapping applications …

The class of recurrent mixture density networks is an important class of probabilistic models
used extensively in sequence modeling and sequence-to-sequence mapping applications …