In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for
regression. Current architectures of GCNs are limited to the small receptive field of …

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 …

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 …

A novel algorithm to segment a primary object in a video sequence is proposed in this work.
First, we generate candidate regions for the primary object using both color and motion …

This paper introduces self-taught object localization, a novel approach that leverages deep
convolutional networks trained for whole-image recognition to localize objects in images …

Abstract Development of region proposal algorithms has rapidly become one of the most
critical research areas over recent years. The perfect accuracy of region-based recognition …

Object proposals have quickly become the de-facto pre-processing step in a number of
vision pipelines (for object detection, object discovery, and other tasks). Their performance is …

Object proposal is one of the most key pre-processing steps for nighttime vehicle detection
systems in intelligent transportation systems. However, most current object proposal …

We are motivated by the need for a generic object proposal generation algorithm which
achieves good balance between object detection recall, proposal localization quality and …

A novel algorithm to segment a primary object in a video sequence is proposed in this work.
First, we generate candidate regions for the primary object using both color and motion …