107 research outputs found
SOFTWARE GENERATION BASED ON ATTRIBUTE GRAMMARS
In this paper a short overview is given of a software generator tool based on attribute
grammars and the experiences are summarized with the use of this system for generating
different types of software
Attribute Learning for Image/Video Understanding
PhDFor the past decade computer vision research has achieved increasing success in visual recognition
including object detection and video classification. Nevertheless, these achievements still
cannot meet the urgent needs of image and video understanding. The recently rapid development
of social media sharing has created a huge demand for automatic media classification and annotation
techniques. In particular, these types of media data usually contain very complex social
activities of a group of people (e.g. YouTube video of a wedding reception) and are captured
by consumer devices with poor visual quality. Thus it is extremely challenging to automatically
understand such a high number of complex image and video categories, especially when these
categories have never been seen before.
One way to understand categories with no or few examples is by transfer learning which
transfers knowledge across related domains, tasks, or distributions. In particular, recently lifelong
learning has become popular which aims at transferring information to tasks without any
observed data. In computer vision, transfer learning often takes the form of attribute learning.
The key underpinning idea of attribute learning is to exploit transfer learning via an intermediatelevel
semantic representations – attributes. The semantic attributes are most commonly used as a
semantically meaningful bridge between low feature data and higher level class concepts, since
they can be used both descriptively (e.g., ’has legs’) and discriminatively (e.g., ’cats have it but
dogs do not’). Previous works propose many different attribute learning models for image and
video understanding. However, there are several intrinsic limitations and problems that exist in
previous attribute learning work. Such limitations discussed in this thesis include limitations of
user-defined attributes, projection domain-shift problems, prototype sparsity problems, inability
to combine multiple semantic representations and noisy annotations of relative attributes. To
tackle these limitations, this thesis explores attribute learning on image and video understanding
from the following three aspects.
Firstly to break the limitations of user-defined attributes, a framework for learning latent
attributes is present for automatic classification and annotation of unstructured group social activity
in videos, which enables the tasks of attribute learning for understanding complex multimedia
data with sparse and incomplete labels. We investigate the learning of latent attributes
for content-based understanding, which aims to model and predict classes and tags relevant to
objects, sounds and events – anything likely to be used by humans to describe or search for
media. Secondly, we propose the framework of transductive multi-view embedding hypergraph
label propagation and solve three inherent limitations of most previous attribute learning work,
i.e., the projection domain shift problems, the prototype sparsity problems and the inability to
combine multiple semantic representations. We explore the manifold structure of the data distributions
of different views projected onto the same embedding space via label propagation on
a graph. Thirdly a novel framework for robust learning is presented to effectively learn relative
attributes from the extremely noisy and sparse annotations. Relative attributes are increasingly
learned from pairwise comparisons collected via crowdsourcing tools which are more economic
and scalable than the conventional laboratory based data annotation. However, a major challenge
for taking a crowdsourcing strategy is the detection and pruning of outliers. We thus propose
a principled way to identify annotation outliers by formulating the relative attribute prediction
task as a unified robust learning to rank problem, tackling both the outlier detection and relative
attribute prediction tasks jointly.
In summary, this thesis studies and solves the key challenges and limitations of attribute
learning in image/video understanding. We show the benefits of solving these challenges and
limitations in our approach which thus achieves better performance than previous methods
AdaNET phase 0 support for the AdaNET Dynamic Software Inventory (DSI) management system prototype. Catalog of available reusable software components
The Ada Software Repository is a public-domain collection of Ada software and information. The Ada Software Repository is one of several repositories located on the SIMTEL20 Defense Data Network host computer at White Sands Missile Range, and available to any host computer on the network since 26 November 1984. This repository provides a free source for Ada programs and information. The Ada Software Repository is divided into several subdirectories. These directories are organized by topic, and their names and a brief overview of their topics are contained. The Ada Software Repository on SIMTEL20 serves two basic roles: to promote the exchange and use (reusability) of Ada programs and tools (including components) and to promote Ada education
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