Predicting where people look in static scenes, aka visual saliency, has received significant
research interest recently. However, relatively less effort has been spent in understanding …

This paper proposes a novel residual attentive learning network architecture for predicting
dynamic eye-fixation maps. The proposed model emphasizes two essential issues, ie …

Visual saliency has been shown to depend on the unpredictability of the visual stimulus
given its surround. Various previous works have advocated the equivalence between …

In this paper, we propose a novel two-stream neural network for video saliency prediction.
Unlike some traditional methods based on hand-crafted feature extraction and integration …

The mismatch between increasingly large video resolution and constrained screen size of
mobile devices has led to the proposal of zoomable video systems based on tiled video. In …

In this paper we present two compressed-domain features that are highly indicative of
saliency in natural video. We demonstrate the potential of these two features to indicate …

Active studies have been conducted on video saliency prediction, which predicts human
visual attention toward videos. Most deep learning-based video saliency prediction models …

Computational modeling of visual saliency has become an important research problem in
recent years, with applications in video quality estimation, video compression, object …

This paper presents a novel compressed domain saliency estimation method based on
analyzing block motion vectors and transform residuals extracted from the bitstream of H …

The ability to create super high-resolution video is becoming relative easy to do either
through a single high-definition video camera or panoramic video that automatically stitches …