9,423 research outputs found
Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos
Wearable cameras stand out as one of the most promising devices for the
upcoming years, and as a consequence, the demand of computer algorithms to
automatically understand the videos recorded with them is increasing quickly.
An automatic understanding of these videos is not an easy task, and its mobile
nature implies important challenges to be faced, such as the changing light
conditions and the unrestricted locations recorded. This paper proposes an
unsupervised strategy based on global features and manifold learning to endow
wearable cameras with contextual information regarding the light conditions and
the location captured. Results show that non-linear manifold methods can
capture contextual patterns from global features without compromising large
computational resources. The proposed strategy is used, as an application case,
as a switching mechanism to improve the hand-detection problem in egocentric
videos.Comment: Submitted for publicatio
Unsupervised Learning with Self-Organizing Spiking Neural Networks
We present a system comprising a hybridization of self-organized map (SOM)
properties with spiking neural networks (SNNs) that retain many of the features
of SOMs. Networks are trained in an unsupervised manner to learn a
self-organized lattice of filters via excitatory-inhibitory interactions among
populations of neurons. We develop and test various inhibition strategies, such
as growing with inter-neuron distance and two distinct levels of inhibition.
The quality of the unsupervised learning algorithm is evaluated using examples
with known labels. Several biologically-inspired classification tools are
proposed and compared, including population-level confidence rating, and
n-grams using spike motif algorithm. Using the optimal choice of parameters,
our approach produces improvements over state-of-art spiking neural networks
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
Neural Nets and Star/Galaxy Separation in Wide Field Astronomical Images
One of the most relevant problems in the extraction of scientifically useful
information from wide field astronomical images (both photographic plates and
CCD frames) is the recognition of the objects against a noisy background and
their classification in unresolved (star-like) and resolved (galaxies) sources.
In this paper we present a neural network based method capable to perform both
tasks and discuss in detail the performance of object detection in a
representative celestial field. The performance of our method is compared to
that of other methodologies often used within the astronomical community.Comment: 6 pages, to appear in the proceedings of IJCNN 99, IEEE Press, 199
- …