Convolutional neural network (CNN)-based encoder-decoder models have profoundly
inspired recent works in the field of salient object detection (SOD). With the rapid …

Detection and localization of regions of images that attract immediate human visual attention
is currently an intensive area of research in computer vision. The capability of automatic …

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique
that has been increasingly used to image the retinal vasculature at capillary level resolution …

The demand of applying semantic segmentation model on mobile devices has been
increasing rapidly. Current state-of-the-art networks have enormous amount of parameters …

Effective integration of contextual information is crucial for salient object detection. To
achieve this, most existing methods based on'skip'architecture mainly focus on how to …

This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs
piecewise planar regions from a single RGB image. PlaneRCNN employs a variant of Mask …

Many existing scene parsing methods adopt Convolutional Neural Networks with fixed-size
receptive fields, which frequently result in inconsistent predictions of large objects and …

Video scene parsing is challenging due to the following two reasons: firstly, it is non-trivial to
learn meaningful video representations for producing the temporally consistent labeling …

Unpaired image-to-image translation problem aims to model the mapping from one domain
to another with unpaired training data. Current works like the well-acknowledged Cycle GAN …

Recent efforts have shown the importance of context on deep convolutional neural network
based semantic segmentation. Among others, the predicted segmentation map (p-map) itself …