Human crowds have become hotspot research, particularly in crowd analysis to ensure
human safety. Adaptations of machine learning (ML) approaches, especially deep learning …

For a globally recognized plant breeding organization, manually recorded field observation
data is crucial for plant breeding decision making. However, certain phenotypic traits such …

Context information, which plays a crucial role in many computer vision tasks, can benefit
deep networks to construct better cognitive competence from more comprehensive …

Sequential recommendation has become a popular and indispensable component of
various online services, which aims to predict the next interested item based on the …

Crowd counting in adverse hazy weather is inevitable and significant to the scene
understanding in real-world application. For the crucial and challenging hazy-weather crowd …

To reduce the interference of the arbitrary crowd distribution and complex background on
the counting accuracy in the unconstrained scene, this paper presents a novel RGB-T crowd …

A dense crowd counting method based on self-attention mechanism with dual-branch fusion
network is proposed in this paper. Our method aims to address the problems of large …

Crowd counting is a significant task in computer vision, which aims to estimate the total
number of people appeared in images or videos. However, it is still very challenging due to …

Crowd counting is still a practical and challenging problem owing to scale variations and
information loss. Most existing methods based on the straightforward fusion of different …

Generalized counting has recently emerged to count novel-class objects within a query
image, leveraging limited exemplars. Although methods based on exemplar-query pairs …