Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

The explosive increase and ubiquitous accessibility of visual data on the Web have led to
the prosperity of research activity in image search or retrieval. With the ignorance of visual …

More and more users are getting used to posting images and text on social networks to
share their emotions or opinions. Accordingly, multimodal sentiment analysis has become a …

Images can convey rich semantics and induce various emotions in viewers. Recently, with
the rapid advancement of emotional intelligence and the explosive growth of visual data …

Emotions can be evoked in humans by images. Most previous works on image emotion
analysis mainly used the elements-of-art-based low-level visual features. However, these …

Social media users are increasingly using both images and text to express their opinions
and share their experiences, instead of only using text in the conventional social media …

Automatic assessment of sentiment from visual content has gained considerable attention
with the increasing tendency of expressing opinions via images and videos online. This …

Many graph-based semi-supervised learning methods for large datasets have been
proposed to cope with the rapidly increasing size of data, such as Anchor Graph …

Text-driven sentiment analysis has been widely studied in the past decade, on both random
and benchmark textual Twitter datasets. Few pertinent studies have also reported visual …

Previous works on image emotion analysis mainly focused on predicting the dominant
emotion category or the average dimension values of an image for affective image …