Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …

We propose a unified approach for bottom-up hierarchical image segmentation and object
candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For …

Current top performing object detectors employ detection proposals to guide the search for
objects, thereby avoiding exhaustive sliding window search across images. Despite the …

We propose a unified approach for bottom-up hierarchical image segmentation and object
proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For …

We develop a deep learning algorithm for contour detection with a fully convolutional
encoder-decoder network. Different from previous low-level edge detection, our algorithm …

We propose a semi-automatic method to obtain foreground object masks for a large set of
related images. We develop a stagewise active approach to propagation: in each stage, we …

Instance segmentation aims to detect and segment individual objects in a scene. Most
existing methods rely on precise mask annotations of every category. However, it is difficult …

Building a complete 3D model of a scene, given only a single depth image, is
underconstrained. To gain a full volumetric model, one needs either multiple views, or a …

This work proposes a method to interpret a scene by assigning a semantic label at every
pixel and inferring the spatial extent of individual object instances together with their …

Popular figure-ground segmentation algorithms generate a pool of boundary-aligned
segment proposals that can be used in subsequent object recognition engines. These …