7,328 research outputs found
Semantic Graph for Zero-Shot Learning
Zero-shot learning aims to classify visual objects without any training data
via knowledge transfer between seen and unseen classes. This is typically
achieved by exploring a semantic embedding space where the seen and unseen
classes can be related. Previous works differ in what embedding space is used
and how different classes and a test image can be related. In this paper, we
utilize the annotation-free semantic word space for the former and focus on
solving the latter issue of modeling relatedness. Specifically, in contrast to
previous work which ignores the semantic relationships between seen classes and
focus merely on those between seen and unseen classes, in this paper a novel
approach based on a semantic graph is proposed to represent the relationships
between all the seen and unseen class in a semantic word space. Based on this
semantic graph, we design a special absorbing Markov chain process, in which
each unseen class is viewed as an absorbing state. After incorporating one test
image into the semantic graph, the absorbing probabilities from the test data
to each unseen class can be effectively computed; and zero-shot classification
can be achieved by finding the class label with the highest absorbing
probability. The proposed model has a closed-form solution which is linear with
respect to the number of test images. We demonstrate the effectiveness and
computational efficiency of the proposed method over the state-of-the-arts on
the AwA (animals with attributes) dataset.Comment: 9 pages, 5 figure
Universal Time Scale for Thermalization in Two-dimensional Systems
The Fermi-Pasta-Ulam-Tsingou problem, i.e., the problem of energy
equipartition among normal modes in a weakly nonlinear lattice, is here studied
in two types of two-dimensional (2D) lattices, more precisely in lattices with
square cell and triangular cell. We apply the wave-turbulence approach to
describe the dynamics and find multi-wave resonances play a major role in the
transfer of energy among the normal modes. We show that, in general, the
thermalization time in 2D systems is inversely proportional to the squared
perturbation strength in the thermodynamic limit. Numerical simulations confirm
that the results are consistent with the theoretical prediction no matter
systems are translation-invariant or not. It leads to the conclusion that such
systems can always be thermalized by arbitrarily weak many-body interactions.
Moreover, the validity for disordered lattices implies that the localized
states are unstable.Comment: 6 pages, 4 figure
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