In this paper, we present a novel end-to-end learning neural network, ie, MATNet, for zero-
shot video object segmentation (ZVOS). Motivated by the human visual attention behavior …

Video saliency detection (VSD) aims at fast locating the most attractive
objects/things/patterns in a given video clip. Existing VSD-related works have mainly relied …

Event cameras, offering extremely high temporal resolution and high dynamic range, have
brought a new perspective to addressing common object detection challenges (eg, motion …

The human attention mechanism can be understood and simulated by closely associating
the saliency prediction task to neuroscience and psychology. Furthermore, saliency …

Visual saliency modeling for images and videos is treated as two independent tasks in
recent computer vision literature. While image saliency modeling is a well-studied problem …

Recently, video saliency prediction has attracted increasing attention, yet the improvement
of its accuracy is still subject to the insufficient use of multi-scale spatiotemporal features. To …

Existing LiDAR-based 3D object detectors usually focus on the single-frame detection, while
ignoring the spatiotemporal information in consecutive point cloud frames. In this paper, we …

Driver attention prediction is becoming an essential research problem in human-like driving
systems. This work makes an attempt to predict the driver attention in driving accident …

Most cutting-edge video saliency prediction models rely on spatiotemporal features
extracted by 3D convolutions due to its local contextual cues acquirement ability. However …

Video saliency prediction (VSP) aims to imitate eye fixations of humans. However, the
potential of this task has not been fully exploited since existing VSP methods only focus on …