Current state-of-the-art methods for image captioning employ region-based features, as they
provide object-level information that is essential to describe the content of images; they are …

Transformer-based architectures with grid features represent the state-of-the-art in visual
and language reasoning tasks, such as visual question answering and image-text matching …

Image captioning means generate descriptive sentences from a query image automatically.
It has recently received widespread attention from the computer vision and natural language …

Video captioning focuses on generating natural language descriptions according to the
video content. Existing works mainly explore this multimodal learning with the paired source …

Image captioning (IC), bringing vision to language, has drawn extensive attention. Precisely
describing visual relations between image objects is a key challenge in IC. We argue that …

Image captioning is a multi-modal task to describe an image into natural language. Many
state-of-the-art methods generally take the encoder–decoder architecture, encode an image …

Image captioning is a core challenge in computer vision, attracting significant attention.
Traditional methods prioritize caption quality, often overlooking style control. Our research …

Transformer-based frameworks have shown superiorities in image captioning. However,
such frameworks are strenuous to consider geometric interrelations among visual contents …

The existing approaches to image captioning tend to adopt Transformer-based architectures
with grid features, which represent the state-of-the-art. However, the strategies are prone to …

Image captioning (IC), bringing vision to language, has drawn extensive attention. A crucial
aspect of IC is the accurate depiction of visual relations among image objects. Visual …