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 …

Exploiting multi-scale representations is critical to improve edge detection for objects at
different scales. To extract edges at dramatically different scales, we propose a Bi …

Instance segmentation in 3D scenes is fundamental in many applications of scene
understanding. It is yet challenging due to the compound factors of data irregularity and …

Superpixels group perceptually similar pixels to create visually meaningful entities while
heavily reducing the number of primitives for subsequent processing steps. As of these …

The use of object proposals is an effective recent approach for increasing the computational
efficiency of object detection. We propose a novel method for generating object bounding …

Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding
catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The …

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 …

Training a generic objectness measure to produce a small set of candidate object windows,
has been shown to speed up the classical sliding window object detection paradigm. We …

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 …