575 research outputs found
Multi-view constrained clustering with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propose a multi-view algorithm based on constrained clustering that
can operate with an incomplete mapping. Given a set of pairwise constraints in
each view, our approach propagates these constraints using a local similarity
measure to those instances that can be mapped to the other views, allowing the
propagated constraints to be transferred across views via the partial mapping.
It uses co-EM to iteratively estimate the propagation within each view based on
the current clustering model, transfer the constraints across views, and then
update the clustering model. By alternating the learning process between views,
this approach produces a unified clustering model that is consistent with all
views. We show that this approach significantly improves clustering performance
over several other methods for transferring constraints and allows multi-view
clustering to be reliably applied when given a limited mapping between the
views. Our evaluation reveals that the propagated constraints have high
precision with respect to the true clusters in the data, explaining their
benefit to clustering performance in both single- and multi-view learning
scenarios
Sparse PointPillars: Exploiting Sparsity in Birds-Eye-View Object Detection
Bird's Eye View (BEV) is a popular representation for processing 3D point
clouds, and by its nature is fundamentally sparse. Motivated by the
computational limitations of mobile robot platforms, we take a fast
high-performance BEV 3D object detector - PointPillars - and modify its
backbone to exploit this sparsity, leading to decreased runtimes. We present
preliminary results demonstrating decreased runtimes with either the same
performance or a modest decrease in performance, which we anticipate will be
remedied by model specific hyperparameter tuning. Our work is a first step
towards a new class of 3D object detectors that exploit sparsity throughout
their entire pipeline in order to reduce runtime and resource usage while
maintaining good detection performance
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