1,509 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
Visual Dynamics: Stochastic Future Generation via Layered 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 that have tackled this
problem in a deterministic or non-parametric way, we propose to model 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. To synthesize realistic movement of objects, we propose a novel network
structure, namely a Cross Convolutional Network; this network 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, and on real-world video frames. We present analyses of
the learned network representations, showing it is implicitly learning a
compact encoding of object appearance and motion. We also demonstrate a few of
its applications, including visual analogy-making and video extrapolation.Comment: Journal preprint of arXiv:1607.02586 (IEEE TPAMI, 2019). The first
two authors contributed equally to this work. Project page:
http://visualdynamics.csail.mit.ed
Generative Plug and Play: Posterior Sampling for Inverse Problems
Over the past decade, Plug-and-Play (PnP) has become a popular method for
reconstructing images using a modular framework consisting of a forward and
prior model. The great strength of PnP is that an image denoiser can be used as
a prior model while the forward model can be implemented using more traditional
physics-based approaches. However, a limitation of PnP is that it reconstructs
only a single deterministic image.
In this paper, we introduce Generative Plug-and-Play (GPnP), a generalization
of PnP to sample from the posterior distribution. As with PnP, GPnP has a
modular framework using a physics-based forward model and an image denoising
prior model. However, in GPnP these models are extended to become proximal
generators, which sample from associated distributions. GPnP applies these
proximal generators in alternation to produce samples from the posterior. We
present experimental simulations using the well-known BM3D denoiser. Our
results demonstrate that the GPnP method is robust, easy to implement, and
produces intuitively reasonable samples from the posterior for sparse
interpolation and tomographic reconstruction. Code to accompany this paper is
available at https://github.com/gbuzzard/generative-pnp-allerton .Comment: 8 pages, submitted to 2023 IEEE Allerton Conferenc
Effects of fallow replacement green manuring with annual legumes on soil nitrogen availability
Non-Peer ReviewedLegume green manures are used in crop rotations to enhance soil nitrogen availability and to provide ground cover for soil conservation. The annual legumes Black Lentil, Chickling Vetch, Tangier Flatpea, and a
Feedpea were grown with tall stubble snow trapping in rotation with spring wheat to determine their suitability for fallow replacement in wheat production systems in the Brown soil zone. The field experiment, conducted at Swift Current Research Station from 1984 to 1991, was designed to assess the different legume types and green manure management practices (inoculation, incorporation, and desiccation). The RCB included fallow-wheat and continuous wheat as controls. Only continuous wheat
received nitrogen (N) fertilizer. Soil N availability was measured by analyzing five soil segments down to 120 cm depth for exchangeable ammonium and nitrate up to four times each year. The topsoil was also analyzed by an incubation/leaching technique to determine the potential N mineralization after three cycles of the rotation in 1990. Soil nitrate showed large treatment related variation. Nitrate values for legume green manuring were consistently higher after incorporation than after desiccation. In all treatments, topsoil nitrate was high in spring and lowest in July. In fall and spring following fallow and green manuring nitrate was high for the fallow-wheat and the legume-wheat rotations and low for continuous wheat. The initial potential rate of nitrogen mineralization reveals a lower nitrogen supplying power of the topsoil for fallow wheat than for legume/wheat and for fertilized continuous wheat
Efficient Bayesian Computational Imaging with a Surrogate Score-Based Prior
We propose a surrogate function for efficient use of score-based priors for
Bayesian inverse imaging. Recent work turned score-based diffusion models into
probabilistic priors for solving ill-posed imaging problems by appealing to an
ODE-based log-probability function. However, evaluating this function is
computationally inefficient and inhibits posterior estimation of
high-dimensional images. Our proposed surrogate prior is based on the evidence
lower-bound of a score-based diffusion model. We demonstrate the surrogate
prior on variational inference for efficient approximate posterior sampling of
large images. Compared to the exact prior in previous work, our surrogate prior
accelerates optimization of the variational image distribution by at least two
orders of magnitude. We also find that our principled approach achieves
higher-fidelity images than non-Bayesian baselines that involve
hyperparameter-tuning at inference. Our work establishes a practical path
forward for using score-based diffusion models as general-purpose priors for
imaging
Primary Production: Sensitivity to Surface Irradiance and Implications for Archiving Data
An equation is derived to express the sensitivity of daily, watercolumn production by phytoplankton in the ocean to variations in irradiance at the sea surface. Assuming no spectral effects, and a vertically uniform chlorophyll profile, the sensitivity is a function only of the dimensionless irradiance. Spectral effects can be accounted for as a function of the chlorophyll concentration. At the global scale, the relative reduction in daily production consequent on halving the surface irradiance (representing the expected scope for variation in surface irradiance under natural conditions) is found to be from 30 to 40%. Choice of data source for irradiance may incur a further systematic error of up to 15%. Given that local irradiance (the principal forcing for primary production) may vary from day to day, the issue of how to archive production data for the most generality is discussed and recommendations made in this regard
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