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 …

Object class detection, also known as category-level object detection, has become one of
the most focused areas in computer vision in the new century. This article attempts to …

The field of object detection has made significant advances riding on the wave of region-
based ConvNets, but their training procedure still includes many heuristics and …

Almost all of the current top-performing object detection networks employ region proposals
to guide the search for object instances. State-of-the-art region proposal methods usually …

In this paper, we study the problem of unsupervised object detection from 3D point clouds in
self-driving scenes. We present a simple yet effective method that exploits (i) point clustering …

In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for
many-task vehicle analysis from a given image. A robust convolutional network is introduced …

The semantic image segmentation task presents a trade-off between test time accuracy and
training time annotation cost. Detailed per-pixel annotations enable training accurate …

Low-level saliency cues or priors do not produce good enough saliency detection results
especially when the salient object presents in a low-contrast background with confusing …

We introduce an unsupervised, geodesic distance based, salient video object segmentation
method. Unlike traditional methods, our method incorporates saliency as prior for object via …

We aim to detect all instances of a category in an image and, for each instance, mark the
pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS) …