7,703 research outputs found
Few-Cost Salient Object Detection with Adversarial-Paced Learning
Detecting and segmenting salient objects from given image scenes has received
great attention in recent years. A fundamental challenge in training the
existing deep saliency detection models is the requirement of large amounts of
annotated data. While gathering large quantities of training data becomes cheap
and easy, annotating the data is an expensive process in terms of time, labor
and human expertise. To address this problem, this paper proposes to learn the
effective salient object detection model based on the manual annotation on a
few training images only, thus dramatically alleviating human labor in training
models. To this end, we name this task as the few-cost salient object detection
and propose an adversarial-paced learning (APL)-based framework to facilitate
the few-cost learning scenario. Essentially, APL is derived from the self-paced
learning (SPL) regime but it infers the robust learning pace through the
data-driven adversarial learning mechanism rather than the heuristic design of
the learning regularizer. Comprehensive experiments on four widely-used
benchmark datasets demonstrate that the proposed method can effectively
approach to the existing supervised deep salient object detection models with
only 1k human-annotated training images. The project page is available at
https://github.com/hb-stone/FC-SOD
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Human Supervised Semi-Autonomous Approach for the DARPA Robotics Challenge Door Task
As the field of autonomous robots continue to advance, there is still a tremendous benefit to research human-supervised robot systems for fielding them in practical applications. The DRC inspired by the Fukushima nuclear power plant disaster has been a major research and development program for the past three years, to advance the field of human supervised control of robots for responding to natural and man-made disasters. The overall goal of the research presented in this thesis is to realise a new approach for semi-autonomous control of the Atlas humanoid robot under discrete commands from the human operator. A combination of autonomous and semi-autonomous perception and manipulation techniques to accomplish the task of detecting, opening and walking through a door are presented. The methods are validated in various different scenarios relevant to DRC door task
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