2,293 research outputs found
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
We address the unsupervised learning of several interconnected problems in
low-level vision: single view depth prediction, camera motion estimation,
optical flow, and segmentation of a video into the static scene and moving
regions. Our key insight is that these four fundamental vision problems are
coupled through geometric constraints. Consequently, learning to solve them
together simplifies the problem because the solutions can reinforce each other.
We go beyond previous work by exploiting geometry more explicitly and
segmenting the scene into static and moving regions. To that end, we introduce
Competitive Collaboration, a framework that facilitates the coordinated
training of multiple specialized neural networks to solve complex problems.
Competitive Collaboration works much like expectation-maximization, but with
neural networks that act as both competitors to explain pixels that correspond
to static or moving regions, and as collaborators through a moderator that
assigns pixels to be either static or independently moving. Our novel method
integrates all these problems in a common framework and simultaneously reasons
about the segmentation of the scene into moving objects and the static
background, the camera motion, depth of the static scene structure, and the
optical flow of moving objects. Our model is trained without any supervision
and achieves state-of-the-art performance among joint unsupervised methods on
all sub-problems.Comment: CVPR 201
Unsupervised Learning for Robust Fitting:A Reinforcement Learning Approach
Robust model fitting is a core algorithm in a large number of computer vision
applications. Solving this problem efficiently for datasets highly contaminated
with outliers is, however, still challenging due to the underlying
computational complexity. Recent literature has focused on learning-based
algorithms. However, most approaches are supervised which require a large
amount of labelled training data. In this paper, we introduce a novel
unsupervised learning framework that learns to directly solve robust model
fitting. Unlike other methods, our work is agnostic to the underlying input
features, and can be easily generalized to a wide variety of LP-type problems
with quasi-convex residuals. We empirically show that our method outperforms
existing unsupervised learning approaches, and achieves competitive results
compared to traditional methods on several important computer vision problems.Comment: The preprint of paper accepted to CVPR 202
Plane-extraction from depth-data using a Gaussian mixture regression model
We propose a novel algorithm for unsupervised extraction of piecewise planar
models from depth-data. Among other applications, such models are a good way of
enabling autonomous agents (robots, cars, drones, etc.) to effectively perceive
their surroundings and to navigate in three dimensions. We propose to do this
by fitting the data with a piecewise-linear Gaussian mixture regression model
whose components are skewed over planes, making them flat in appearance rather
than being ellipsoidal, by embedding an outlier-trimming process that is
formally incorporated into the proposed expectation-maximization algorithm, and
by selectively fusing contiguous, coplanar components. Part of our motivation
is an attempt to estimate more accurate plane-extraction by allowing each model
component to make use of all available data through probabilistic clustering.
The algorithm is thoroughly evaluated against a standard benchmark and is shown
to rank among the best of the existing state-of-the-art methods.Comment: 11 pages, 2 figures, 1 tabl
A Revisit to the Normalized Eight-Point Algorithm and A Self-Supervised Deep Solution
The Normalized Eight-Point algorithm has been widely viewed as the
cornerstone in two-view geometry computation, where the seminal Hartley's
normalization greatly improves the performance of the direct linear
transformation (DLT) algorithm. A natural question is, whether there exists and
how to find other normalization methods that may further improve the
performance as per each input sample. In this paper, we provide a novel
perspective and make two contributions towards this fundamental problem: 1) We
revisit the normalized eight-point algorithm and make a theoretical
contribution by showing the existence of different and better normalization
algorithms; 2) We present a deep convolutional neural network with a
self-supervised learning strategy to the normalization. Given eight pairs of
correspondences, our network directly predicts the normalization matrices, thus
learning to normalize each input sample. Our learning-based normalization
module could be integrated with both traditional (e.g., RANSAC) and deep
learning framework (affording good interpretability) with minimal efforts.
Extensive experiments on both synthetic and real images show the effectiveness
of our proposed approach.Comment: 12 pages, 7 figures, A preliminary versio
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Reconstructing continuous distributions of 3D protein structure from cryo-EM images
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining
the structure of proteins and other macromolecular complexes at near-atomic
resolution. In single particle cryo-EM, the central problem is to reconstruct
the three-dimensional structure of a macromolecule from noisy and
randomly oriented two-dimensional projections. However, the imaged protein
complexes may exhibit structural variability, which complicates reconstruction
and is typically addressed using discrete clustering approaches that fail to
capture the full range of protein dynamics. Here, we introduce a novel method
for cryo-EM reconstruction that extends naturally to modeling continuous
generative factors of structural heterogeneity. This method encodes structures
in Fourier space using coordinate-based deep neural networks, and trains these
networks from unlabeled 2D cryo-EM images by combining exact inference over
image orientation with variational inference for structural heterogeneity. We
demonstrate that the proposed method, termed cryoDRGN, can perform ab initio
reconstruction of 3D protein complexes from simulated and real 2D cryo-EM image
data. To our knowledge, cryoDRGN is the first neural network-based approach for
cryo-EM reconstruction and the first end-to-end method for directly
reconstructing continuous ensembles of protein structures from cryo-EM images
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