604,747 research outputs found
Appearance-and-Relation Networks for Video Classification
Spatiotemporal feature learning in videos is a fundamental problem in
computer vision. This paper presents a new architecture, termed as
Appearance-and-Relation Network (ARTNet), to learn video representation in an
end-to-end manner. ARTNets are constructed by stacking multiple generic
building blocks, called as SMART, whose goal is to simultaneously model
appearance and relation from RGB input in a separate and explicit manner.
Specifically, SMART blocks decouple the spatiotemporal learning module into an
appearance branch for spatial modeling and a relation branch for temporal
modeling. The appearance branch is implemented based on the linear combination
of pixels or filter responses in each frame, while the relation branch is
designed based on the multiplicative interactions between pixels or filter
responses across multiple frames. We perform experiments on three action
recognition benchmarks: Kinetics, UCF101, and HMDB51, demonstrating that SMART
blocks obtain an evident improvement over 3D convolutions for spatiotemporal
feature learning. Under the same training setting, ARTNets achieve superior
performance on these three datasets to the existing state-of-the-art methods.Comment: CVPR18 camera-ready version. Code & models available at
https://github.com/wanglimin/ARTNe
A General Framework for Anytime Approximation in Probabilistic Databases
Anytime approximation algorithms that compute the probabilities of queries
over probabilistic databases can be of great use to statistical learning tasks.
Those approaches have been based so far on either (i) sampling or (ii)
branch-and-bound with model-based bounds. We present here a more general
branch-and-bound framework that extends the possible bounds by using
'dissociation', which yields tighter bounds.Comment: 3 pages, 2 figures, submitted to StarAI 2018 Worksho
Hybrid Models for Learning to Branch
A recent Graph Neural Network (GNN) approach for learning to branch has been
shown to successfully reduce the running time of branch-and-bound algorithms
for Mixed Integer Linear Programming (MILP). While the GNN relies on a GPU for
inference, MILP solvers are purely CPU-based. This severely limits its
application as many practitioners may not have access to high-end GPUs. In this
work, we ask two key questions. First, in a more realistic setting where only a
CPU is available, is the GNN model still competitive? Second, can we devise an
alternate computationally inexpensive model that retains the predictive power
of the GNN architecture? We answer the first question in the negative, and
address the second question by proposing a new hybrid architecture for
efficient branching on CPU machines. The proposed architecture combines the
expressive power of GNNs with computationally inexpensive multi-linear
perceptrons (MLP) for branching. We evaluate our methods on four classes of
MILP problems, and show that they lead to up to 26% reduction in solver running
time compared to state-of-the-art methods without a GPU, while extrapolating to
harder problems than it was trained on.Comment: Preprint. Under revie
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