18,878 research outputs found
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
We study the problem of synthesizing a number of likely future frames from a
single input image. In contrast to traditional methods, which have tackled this
problem in a deterministic or non-parametric way, we propose a novel approach
that models future frames in a probabilistic manner. Our probabilistic model
makes it possible for us to sample and synthesize many possible future frames
from a single input image. Future frame synthesis is challenging, as it
involves low- and high-level image and motion understanding. We propose a novel
network structure, namely a Cross Convolutional Network to aid in synthesizing
future frames; this network structure encodes image and motion information as
feature maps and convolutional kernels, respectively. In experiments, our model
performs well on synthetic data, such as 2D shapes and animated game sprites,
as well as on real-wold videos. We also show that our model can be applied to
tasks such as visual analogy-making, and present an analysis of the learned
network representations.Comment: The first two authors contributed equally to this wor
Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by
watching how they move; they can then predict how each part moves in the
future. In this paper, we propose a novel formulation that simultaneously
learns a hierarchical, disentangled object representation and a dynamics model
for object parts from unlabeled videos. Our Parts, Structure, and Dynamics
(PSD) model learns to, first, recognize the object parts via a layered image
representation; second, predict hierarchy via a structural descriptor that
composes low-level concepts into a hierarchical structure; and third, model the
system dynamics by predicting the future. Experiments on multiple real and
synthetic datasets demonstrate that our PSD model works well on all three
tasks: segmenting object parts, building their hierarchical structure, and
capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor
A Cognitive Model of an Epistemic Community: Mapping the Dynamics of Shallow Lake Ecosystems
We used fuzzy cognitive mapping (FCM) to develop a generic shallow lake
ecosystem model by augmenting the individual cognitive maps drawn by 8
scientists working in the area of shallow lake ecology. We calculated graph
theoretical indices of the individual cognitive maps and the collective
cognitive map produced by augmentation. The graph theoretical indices revealed
internal cycles showing non-linear dynamics in the shallow lake ecosystem. The
ecological processes were organized democratically without a top-down
hierarchical structure. The steady state condition of the generic model was a
characteristic turbid shallow lake ecosystem since there were no dynamic
environmental changes that could cause shifts between a turbid and a clearwater
state, and the generic model indicated that only a dynamic disturbance regime
could maintain the clearwater state. The model developed herein captured the
empirical behavior of shallow lakes, and contained the basic model of the
Alternative Stable States Theory. In addition, our model expanded the basic
model by quantifying the relative effects of connections and by extending it.
In our expanded model we ran 4 simulations: harvesting submerged plants,
nutrient reduction, fish removal without nutrient reduction, and
biomanipulation. Only biomanipulation, which included fish removal and nutrient
reduction, had the potential to shift the turbid state into clearwater state.
The structure and relationships in the generic model as well as the outcomes of
the management simulations were supported by actual field studies in shallow
lake ecosystems. Thus, fuzzy cognitive mapping methodology enabled us to
understand the complex structure of shallow lake ecosystems as a whole and
obtain a valid generic model based on tacit knowledge of experts in the field.Comment: 24 pages, 5 Figure
- …