1,755 research outputs found
Active Collaborative Ensemble Tracking
A discriminative ensemble tracker employs multiple classifiers, each of which
casts a vote on all of the obtained samples. The votes are then aggregated in
an attempt to localize the target object. Such method relies on collective
competence and the diversity of the ensemble to approach the target/non-target
classification task from different views. However, by updating all of the
ensemble using a shared set of samples and their final labels, such diversity
is lost or reduced to the diversity provided by the underlying features or
internal classifiers' dynamics. Additionally, the classifiers do not exchange
information with each other while striving to serve the collective goal, i.e.,
better classification. In this study, we propose an active collaborative
information exchange scheme for ensemble tracking. This, not only orchestrates
different classifier towards a common goal but also provides an intelligent
update mechanism to keep the diversity of classifiers and to mitigate the
shortcomings of one with the others. The data exchange is optimized with regard
to an ensemble uncertainty utility function, and the ensemble is updated via
co-training. The evaluations demonstrate promising results realized by the
proposed algorithm for the real-world online tracking.Comment: AVSS 2017 Submissio
Universum Prescription: Regularization using Unlabeled Data
This paper shows that simply prescribing "none of the above" labels to
unlabeled data has a beneficial regularization effect to supervised learning.
We call it universum prescription by the fact that the prescribed labels cannot
be one of the supervised labels. In spite of its simplicity, universum
prescription obtained competitive results in training deep convolutional
networks for CIFAR-10, CIFAR-100, STL-10 and ImageNet datasets. A qualitative
justification of these approaches using Rademacher complexity is presented. The
effect of a regularization parameter -- probability of sampling from unlabeled
data -- is also studied empirically.Comment: 7 pages for article, 3 pages for supplemental material. To appear in
AAAI-1
Exploiting Data and Human Knowledge for Predicting Wildlife Poaching
Poaching continues to be a significant threat to the conservation of wildlife
and the associated ecosystem. Estimating and predicting where the poachers have
committed or would commit crimes is essential to more effective allocation of
patrolling resources. The real-world data in this domain is often sparse, noisy
and incomplete, consisting of a small number of positive data (poaching signs),
a large number of negative data with label uncertainty, and an even larger
number of unlabeled data. Fortunately, domain experts such as rangers can
provide complementary information about poaching activity patterns. However,
this kind of human knowledge has rarely been used in previous approaches. In
this paper, we contribute new solutions to the predictive analysis of poaching
patterns by exploiting both very limited data and human knowledge. We propose
an approach to elicit quantitative information from domain experts through a
questionnaire built upon a clustering-based division of the conservation area.
In addition, we propose algorithms that exploit qualitative and quantitative
information provided by the domain experts to augment the dataset and improve
learning. In collaboration with World Wild Fund for Nature, we show that
incorporating human knowledge leads to better predictions in a conservation
area in Northeastern China where the charismatic species is Siberian Tiger. The
results show the importance of exploiting human knowledge when learning from
limited data.Comment: COMPASS 201
Progressive Ensemble Networks for Zero-Shot Recognition
Despite the advancement of supervised image recognition algorithms, their
dependence on the availability of labeled data and the rapid expansion of image
categories raise the significant challenge of zero-shot learning. Zero-shot
learning (ZSL) aims to transfer knowledge from labeled classes into unlabeled
classes to reduce human labeling effort. In this paper, we propose a novel
progressive ensemble network model with multiple projected label embeddings to
address zero-shot image recognition. The ensemble network is built by learning
multiple image classification functions with a shared feature extraction
network but different label embedding representations, which enhance the
diversity of the classifiers and facilitate information transfer to unlabeled
classes. A progressive training framework is then deployed to gradually label
the most confident images in each unlabeled class with predicted pseudo-labels
and update the ensemble network with the training data augmented by the
pseudo-labels. The proposed model performs training on both labeled and
unlabeled data. It can naturally bridge the domain shift problem in visual
appearances and be extended to the generalized zero-shot learning scenario. We
conduct experiments on multiple ZSL datasets and the empirical results
demonstrate the efficacy of the proposed model.Comment: CVPR1
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