10,843 research outputs found
Pairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample
sizes, along with significant intra-class variation and inter-class similarity.
While prior work has addressed intra-class variation using localization and
segmentation techniques, inter-class similarity may also affect feature
learning and reduce classification performance. In this work, we address this
problem using a novel optimization procedure for the end-to-end neural network
training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces
overfitting by intentionally {introducing confusion} in the activations. With
PC regularization, we obtain state-of-the-art performance on six of the most
widely-used FGVC datasets and demonstrate improved localization ability. {PC}
is easy to implement, does not need excessive hyperparameter tuning during
training, and does not add significant overhead during test time.Comment: Camera-Ready version for ECCV 201
Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors
Robot awareness of human actions is an essential research problem in robotics
with many important real-world applications, including human-robot
collaboration and teaming. Over the past few years, depth sensors have become a
standard device widely used by intelligent robots for 3D perception, which can
also offer human skeletal data in 3D space. Several methods based on skeletal
data were designed to enable robot awareness of human actions with satisfactory
accuracy. However, previous methods treated all body parts and features equally
important, without the capability to identify discriminative body parts and
features. In this paper, we propose a novel simultaneous Feature And Body-part
Learning (FABL) approach that simultaneously identifies discriminative body
parts and features, and efficiently integrates all available information
together to enable real-time robot awareness of human behaviors. We formulate
FABL as a regression-like optimization problem with structured
sparsity-inducing norms to model interrelationships of body parts and features.
We also develop an optimization algorithm to solve the formulated problem,
which possesses a theoretical guarantee to find the optimal solution. To
evaluate FABL, three experiments were performed using public benchmark
datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter
robot in practical assistive living applications. Experimental results show
that our FABL approach obtains a high recognition accuracy with a processing
speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method
to enable real-time robot awareness of human behaviors in practical robotics
applications.Comment: 8 pages, 6 figures, accepted by ICRA'1
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Automatic classification of epileptic seizure types in electroencephalograms
(EEGs) data can enable more precise diagnosis and efficient management of the
disease. This task is challenging due to factors such as low signal-to-noise
ratios, signal artefacts, high variance in seizure semiology among epileptic
patients, and limited availability of clinical data. To overcome these
challenges, in this paper, we present SeizureNet, a deep learning framework
which learns multi-spectral feature embeddings using an ensemble architecture
for cross-patient seizure type classification. We used the recently released
TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of
SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of
up to 0.94 for seizure-wise cross validation and 0.59 for patient-wise cross
validation for scalp EEG based multi-class seizure type classification. We also
show that the high-level feature embeddings learnt by SeizureNet considerably
improve the accuracy of smaller networks through knowledge distillation for
applications with low-memory constraints
Teaching Categories to Human Learners with Visual Explanations
We study the problem of computer-assisted teaching with explanations.
Conventional approaches for machine teaching typically only provide feedback at
the instance level e.g., the category or label of the instance. However, it is
intuitive that clear explanations from a knowledgeable teacher can
significantly improve a student's ability to learn a new concept. To address
these existing limitations, we propose a teaching framework that provides
interpretable explanations as feedback and models how the learner incorporates
this additional information. In the case of images, we show that we can
automatically generate explanations that highlight the parts of the image that
are responsible for the class label. Experiments on human learners illustrate
that, on average, participants achieve better test set performance on
challenging categorization tasks when taught with our interpretable approach
compared to existing methods
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