4,112 research outputs found
VideoGraph: Recognizing Minutes-Long Human Activities in Videos
Many human activities take minutes to unfold. To represent them, related
works opt for statistical pooling, which neglects the temporal structure.
Others opt for convolutional methods, as CNN and Non-Local. While successful in
learning temporal concepts, they are short of modeling minutes-long temporal
dependencies. We propose VideoGraph, a method to achieve the best of two
worlds: represent minutes-long human activities and learn their underlying
temporal structure. VideoGraph learns a graph-based representation for human
activities. The graph, its nodes and edges are learned entirely from video
datasets, making VideoGraph applicable to problems without node-level
annotation. The result is improvements over related works on benchmarks:
Epic-Kitchen and Breakfast. Besides, we demonstrate that VideoGraph is able to
learn the temporal structure of human activities in minutes-long videos
Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
Event detection in unconstrained videos is conceived as a content-based video
retrieval with two modalities: textual and visual. Given a text describing a
novel event, the goal is to rank related videos accordingly. This task is
zero-exemplar, no video examples are given to the novel event.
Related works train a bank of concept detectors on external data sources.
These detectors predict confidence scores for test videos, which are ranked and
retrieved accordingly. In contrast, we learn a joint space in which the visual
and textual representations are embedded. The space casts a novel event as a
probability of pre-defined events. Also, it learns to measure the distance
between an event and its related videos.
Our model is trained end-to-end on publicly available EventNet. When applied
to TRECVID Multimedia Event Detection dataset, it outperforms the
state-of-the-art by a considerable margin.Comment: IEEE CVPR 201
A study of local approximation for polarization potentials
We discuss the derivation of an equivalent \textit{l}-independent
polarization potential for use in the optical Schr\"{o}dinger equation that
describes the elastic scattering of heavy ions. Three diffferent methods are
used for this purpose. Application of our theory to the low energy scattering
of the halo nucleus Li from a C target is made. It is found that
the notion of \textit{l}-independent polarization potential has some validity
but can not be a good substitute for the \textit{l}-dependent local equivalent
Feshbach polarization potential.Comment: 8 pages, 4 figure
Nonlinear Schrodinger equation with chaotic, random, and nonperiodic nonlinearity
In this paper we deal with a nonlinear Schr\"{o}dinger equation with chaotic,
random, and nonperiodic cubic nonlinearity. Our goal is to study the soliton
evolution, with the strength of the nonlinearity perturbed in the space and
time coordinates and to check its robustness under these conditions. Comparing
with a real system, the perturbation can be related to, e.g., impurities in
crystalline structures, or coupling to a thermal reservoir which, on the
average, enhances the nonlinearity. We also discuss the relevance of such
random perturbations to the dynamics of Bose-Einstein Condensates and their
collective excitations and transport.Comment: 4 pages, 6 figure
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