Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …

During the long history of computer vision, one of the grand challenges has been semantic
segmentation which is the ability to segment an unknown image into different parts and …

Recent deep networks achieved state of the art performanceon a variety of semantic
segmentation tasks. Despite such progress, thesemodels often face challenges in real world …

Semantic segmentation requires both rich spatial information and sizeable receptive field.
However, modern approaches usually compromise spatial resolution to achieve real-time …

Clustering is a class of unsupervised learning methods that has been extensively applied
and studied in computer vision. Little work has been done to adapt it to the end-to-end …

We notice information flow in convolutional neural networks is restricted inside local
neighborhood regions due to the physical design of convolutional filters, which limits the …

Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and
detect objects inside, while also identifying the textures and surfaces of the objects along …

The U-Net was presented in 2015. With its straight-forward and successful architecture it
quickly evolved to a commonly used benchmark in medical image segmentation. The …

Recent advances in deep neural networks have been developed via architecture search for
stronger representational power. In this work, we focus on the effect of attention in general …

Semantic image segmentation is a basic street scene understanding task in autonomous
driving, where each pixel in a high resolution image is categorized into a set of semantic …