16,251 research outputs found
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips
Short internet video clips like vines present a significantly wild
distribution compared to traditional video datasets. In this paper, we focus on
the problem of unsupervised action classification in wild vines using
traditional labeled datasets. To this end, we use a data augmentation based
simple domain adaptation strategy. We utilise semantic word2vec space as a
common subspace to embed video features from both, labeled source domain and
unlablled target domain. Our method incrementally augments the labeled source
with target samples and iteratively modifies the embedding function to bring
the source and target distributions together. Additionally, we utilise a
multi-modal representation that incorporates noisy semantic information
available in form of hash-tags. We show the effectiveness of this simple
adaptation technique on a test set of vines and achieve notable improvements in
performance.Comment: 9 pages, GCPR, 201
Waltz - An exploratory visualization tool for volume data, using multiform abstract displays
Although, visualization is now widely used, misinterpretations still occur. There are three primary solutions intended to aid a user interpret data correctly. These are: displaying the data in different forms (Multiform visualization); simplifying (or abstracting) the structure of the viewed information; and linking objects and views together (allowing corresponding objects to be jointly manipulated and interrogated). These well-known visualization techniques, provide an emphasis towards the visualization display. We believe however that current visualization systems do not effectively utilise the display, for example, often placing it at the end of a long visualization process. Our visualization system, based on an adapted visualization model, allows a display method to be used throughout the visualization process, in which the user operates a 'Display (correlate) and Refine' visualization cycle. This display integration provides a useful exploration environment, where objects and Views may be directly manipulated; a set of 'portions of interest' can be selected to generate a specialized dataset. This may subsequently be further displayed, manipulated and filtered
Cyber-Virtual Systems: Simulation, Validation & Visualization
We describe our ongoing work and view on simulation, validation and
visualization of cyber-physical systems in industrial automation during
development, operation and maintenance. System models may represent an existing
physical part - for example an existing robot installation - and a software
simulated part - for example a possible future extension. We call such systems
cyber-virtual systems.
In this paper, we present the existing VITELab infrastructure for
visualization tasks in industrial automation. The new methodology for
simulation and validation motivated in this paper integrates this
infrastructure. We are targeting scenarios, where industrial sites which may be
in remote locations are modeled and visualized from different sites anywhere in
the world.
Complementing the visualization work, here, we are also concentrating on
software modeling challenges related to cyber-virtual systems and simulation,
testing, validation and verification techniques for them. Software models of
industrial sites require behavioural models of the components of the industrial
sites such as models for tools, robots, workpieces and other machinery as well
as communication and sensor facilities. Furthermore, collaboration between
sites is an important goal of our work.Comment: Preprint, 9th International Conference on Evaluation of Novel
Approaches to Software Engineering (ENASE 2014
Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks
A major challenge for the realization of intelligent robots is to supply them
with cognitive abilities in order to allow ordinary users to program them
easily and intuitively. One way of such programming is teaching work tasks by
interactive demonstration. To make this effective and convenient for the user,
the machine must be capable to establish a common focus of attention and be
able to use and integrate spoken instructions, visual perceptions, and
non-verbal clues like gestural commands. We report progress in building a
hybrid architecture that combines statistical methods, neural networks, and
finite state machines into an integrated system for instructing grasping tasks
by man-machine interaction. The system combines the GRAVIS-robot for visual
attention and gestural instruction with an intelligent interface for speech
recognition and linguistic interpretation, and an modality fusion module to
allow multi-modal task-oriented man-machine communication with respect to
dextrous robot manipulation of objects.Comment: 7 pages, 8 figure
- …