6,071 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
Discriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for
effective image restoration, achieving convincing trade-off between image
quality and computational efficiency. However, these methods require separate
training for each restoration task (e.g., denoising, deblurring, demosaicing)
and problem condition (e.g., noise level of input images). This makes it
time-consuming and difficult to encompass all tasks and conditions during
training. In this paper, we propose a discriminative transfer learning method
that incorporates formal proximal optimization and discriminative learning for
general image restoration. The method requires a single-pass training and
allows for reuse across various problems and conditions while achieving an
efficiency comparable to previous discriminative approaches. Furthermore, after
being trained, our model can be easily transferred to new likelihood terms to
solve untrained tasks, or be combined with existing priors to further improve
image restoration quality
Understanding and Diagnosing Visual Tracking Systems
Several benchmark datasets for visual tracking research have been proposed in
recent years. Despite their usefulness, whether they are sufficient for
understanding and diagnosing the strengths and weaknesses of different trackers
remains questionable. To address this issue, we propose a framework by breaking
a tracker down into five constituent parts, namely, motion model, feature
extractor, observation model, model updater, and ensemble post-processor. We
then conduct ablative experiments on each component to study how it affects the
overall result. Surprisingly, our findings are discrepant with some common
beliefs in the visual tracking research community. We find that the feature
extractor plays the most important role in a tracker. On the other hand,
although the observation model is the focus of many studies, we find that it
often brings no significant improvement. Moreover, the motion model and model
updater contain many details that could affect the result. Also, the ensemble
post-processor can improve the result substantially when the constituent
trackers have high diversity. Based on our findings, we put together some very
elementary building blocks to give a basic tracker which is competitive in
performance to the state-of-the-art trackers. We believe our framework can
provide a solid baseline when conducting controlled experiments for visual
tracking research
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