Image instance segmentation involves labeling pixels of images with classes and instances,
which is one of the pivotal technologies in many domains, such as natural scenes …

At present, with the advance of satellite image processing technology, remote sensing
images are becoming more widely used in real scenes. However, due to the limitations of …

In this paper, we study the context aggregation problem in semantic segmentation.
Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we …

The feed-forward architectures of recently proposed deep super-resolution networks learn
representations of low-resolution inputs, and the non-linear mapping from those to high …

Segmenting highly-overlapping objects is challenging, because typically no distinction is
made between real object contours and occlusion boundaries. Unlike previous two-stage …

We present a model-agnostic post-processing scheme to improve the boundary quality for
the segmentation result that is generated by any existing segmentation model. Motivated by …

We present the first fully convolutional end-to-end solution for instance-aware semantic
segmentation task. It inherits all the merits of FCNs for semantic segmentation and instance …

Incorporation of prior knowledge about organ shape and location is key to improve
performance of image analysis approaches. In particular, priors can be useful in cases …

Manually labeling datasets with object masks is extremely time consuming. In this work, we
follow the idea of Polygon-RNN to produce polygonal annotations of objects interactively …

In this paper we formulate structure from motion as a learning problem. We train a
convolutional network end-to-end to compute depth and camera motion from successive …