5,782 research outputs found
Joint Object and Part Segmentation using Deep Learned Potentials
Segmenting semantic objects from images and parsing them into their
respective semantic parts are fundamental steps towards detailed object
understanding in computer vision. In this paper, we propose a joint solution
that tackles semantic object and part segmentation simultaneously, in which
higher object-level context is provided to guide part segmentation, and more
detailed part-level localization is utilized to refine object segmentation.
Specifically, we first introduce the concept of semantic compositional parts
(SCP) in which similar semantic parts are grouped and shared among different
objects. A two-channel fully convolutional network (FCN) is then trained to
provide the SCP and object potentials at each pixel. At the same time, a
compact set of segments can also be obtained from the SCP predictions of the
network. Given the potentials and the generated segments, in order to explore
long-range context, we finally construct an efficient fully connected
conditional random field (FCRF) to jointly predict the final object and part
labels. Extensive evaluation on three different datasets shows that our
approach can mutually enhance the performance of object and part segmentation,
and outperforms the current state-of-the-art on both tasks
Iterative Instance Segmentation
Existing methods for pixel-wise labelling tasks generally disregard the
underlying structure of labellings, often leading to predictions that are
visually implausible. While incorporating structure into the model should
improve prediction quality, doing so is challenging - manually specifying the
form of structural constraints may be impractical and inference often becomes
intractable even if structural constraints are given. We sidestep this problem
by reducing structured prediction to a sequence of unconstrained prediction
problems and demonstrate that this approach is capable of automatically
discovering priors on shape, contiguity of region predictions and smoothness of
region contours from data without any a priori specification. On the instance
segmentation task, this method outperforms the state-of-the-art, achieving a
mean of 63.6% at 50% overlap and 43.3% at 70% overlap.Comment: 13 pages, 10 figures; IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
Scoring and Classifying with Gated Auto-encoders
Auto-encoders are perhaps the best-known non-probabilistic methods for
representation learning. They are conceptually simple and easy to train. Recent
theoretical work has shed light on their ability to capture manifold structure,
and drawn connections to density modelling. This has motivated researchers to
seek ways of auto-encoder scoring, which has furthered their use in
classification. Gated auto-encoders (GAEs) are an interesting and flexible
extension of auto-encoders which can learn transformations among different
images or pixel covariances within images. However, they have been much less
studied, theoretically or empirically. In this work, we apply a dynamical
systems view to GAEs, deriving a scoring function, and drawing connections to
Restricted Boltzmann Machines. On a set of deep learning benchmarks, we also
demonstrate their effectiveness for single and multi-label classification
Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond
The acceleration race of digital computing technologies seems to be steering
toward impasses -- technological, economical and environmental -- a condition
that has spurred research efforts in alternative, "neuromorphic" (brain-like)
computing technologies. Furthermore, since decades the idea of exploiting
nonlinear physical phenomena "directly" for non-digital computing has been
explored under names like "unconventional computing", "natural computing",
"physical computing", or "in-materio computing". This has been taking place in
niches which are small compared to other sectors of computer science. In this
paper I stake out the grounds of how a general concept of "computing" can be
developed which comprises digital, neuromorphic, unconventional and possible
future "computing" paradigms. The main contribution of this paper is a
wide-scope survey of existing formal conceptualizations of "computing". The
survey inspects approaches rooted in three different kinds of background
mathematics: discrete-symbolic formalisms, probabilistic modeling, and
dynamical-systems oriented views. It turns out that different choices of
background mathematics lead to decisively different understandings of what
"computing" is. Across all of this diversity, a unifying coordinate system for
theorizing about "computing" can be distilled. Within these coordinates I
locate anchor points for a foundational formal theory of a future
computing-engineering discipline that includes, but will reach beyond, digital
and neuromorphic computing.Comment: An extended and carefully revised version of this manuscript has now
(March 2021) been published as "Toward a generalized theory comprising
digital, neuromorphic, and unconventional computing" in the new open-access
journal Neuromorphic Computing and Engineerin
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