Convolutional neural networks (CNNs) have significantly advanced computational
modelling for saliency prediction. However, accurately simulating the mechanisms of visual …

Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this
way, camouflage acts as a key defence mechanism across species that is critical to survival …

While user interfaces (UIs) display elements such as images and text in a grid-based layout,
UI types differ significantly in the number of elements and how they are displayed. For …

The human visual system effectively analyzes scenes based on local, global and semantic
properties. Deep learning-based saliency prediction models adopted two-stream networks …

Existing salient object detection methods are capable of predicting binary maps that
highlight visually salient regions. However, these methods are limited in their ability to …

Fixation prediction aims to simulate human visual selection mechanism and estimate the
visual saliency degree of regions in a scene. In semantically rich scenes, there are generally …

The mass adoption of display devices calls for advanced power-reduction techniques.
Power-constrained contrast enhancement (PCCE) gains many improvements in recent …

Predicting driver's attention in complex driving scenarios is becoming a hot topic due to it
helps the design of some autonomous driving tasks, optimizing visual scene understanding …

We present a novel approach for saliency prediction in images, leveraging parallel decoding
in transformers to learn saliency solely from fixation maps. Models typically rely on …

Compared with the annotated data for the 2D image saliency prediction task, the annotated
data for training omnidirectional image (or 360° image) saliency prediction models are not …