Due to object detection's close relationship with video analysis and image understanding, it
has attracted much research attention in recent years. Traditional object detection methods …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Abstract The recent Segment Anything Model (SAM) represents a big leap in scaling up
segmentation models, allowing for powerful zero-shot capabilities and flexible prompting …

Representing features at multiple scales is of great importance for numerous vision tasks.
Recent advances in backbone convolutional neural networks (CNNs) continually …

Existing state-of-the-art saliency detection methods heavily rely on CNN-based
architectures. Alternatively, we rethink this task from a convolution-free sequence-to …

Deep neural networks with applications from computer vision to medical diagnosis,,,–are
commonly implemented using clock-based processors,,,,,,,–, in which computation speed is …

Fully convolutional neural networks (FCNs) have shown their advantages in the salient
object detection task. However, most existing FCNs-based methods still suffer from coarse …

We solve the problem of salient object detection by investigating how to expand the role of
pooling in convolutional neural networks. Based on the U-shape architecture, we first build a …

Existing state-of-the-art salient object detection networks rely on aggregating multi-level
features of pre-trained convolutional neural networks (CNNs). However, compared to high …

Foreground map evaluation is crucial for gauging the progress of object segmentation
algorithms, in particular in the filed of salient object detection where the purpose is to …