Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors
including composition, color, style, and high-level semantics. Existing image aesthetic …

As smartphones become people's primary cameras to take photos, the quality of their
cameras and the associated computational photography modules has become a de facto …

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

Challenges in image aesthetics assessment (IAA) arise from that images of different themes
correspond to different evaluation criteria, and learning aesthetics directly from images while …

Abstract Joint Multi-modal Aspect-based Sentiment Analysis (JMASA) is a challenging task
that seeks to identify all aspect-sentiment pairs from multimodal data. Current JMASA …

The pervasive view of the mobile crowd bridges various real-world scenes and people's
perceptions with the gathering of distributed crowdsensing photos. To elaborate informative …

Image aesthetics assessment (IAA) is a challenging task due to its highly subjective nature.
Most of the current studies rely on large-scale datasets (eg, AVA and AADB) to learn a …

Federated Learning is a novel framework that allows multiple devices or institutions to train a
machine learning model collaboratively while preserving their data private. This …