5,702 research outputs found
CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering
Intrinsic image decomposition is a challenging, long-standing computer vision
problem for which ground truth data is very difficult to acquire. We explore
the use of synthetic data for training CNN-based intrinsic image decomposition
models, then applying these learned models to real-world images. To that end,
we present \ICG, a new, large-scale dataset of physically-based rendered images
of scenes with full ground truth decompositions. The rendering process we use
is carefully designed to yield high-quality, realistic images, which we find to
be crucial for this problem domain. We also propose a new end-to-end training
method that learns better decompositions by leveraging \ICG, and optionally IIW
and SAW, two recent datasets of sparse annotations on real-world images.
Surprisingly, we find that a decomposition network trained solely on our
synthetic data outperforms the state-of-the-art on both IIW and SAW, and
performance improves even further when IIW and SAW data is added during
training. Our work demonstrates the suprising effectiveness of
carefully-rendered synthetic data for the intrinsic images task.Comment: Paper for 'CGIntrinsics: Better Intrinsic Image Decomposition through
Physically-Based Rendering' published in ECCV, 201
Scalable Full Flow with Learned Binary Descriptors
We propose a method for large displacement optical flow in which local
matching costs are learned by a convolutional neural network (CNN) and a
smoothness prior is imposed by a conditional random field (CRF). We tackle the
computation- and memory-intensive operations on the 4D cost volume by a
min-projection which reduces memory complexity from quadratic to linear and
binary descriptors for efficient matching. This enables evaluation of the cost
on the fly and allows to perform learning and CRF inference on high resolution
images without ever storing the 4D cost volume. To address the problem of
learning binary descriptors we propose a new hybrid learning scheme. In
contrast to current state of the art approaches for learning binary CNNs we can
compute the exact non-zero gradient within our model. We compare several
methods for training binary descriptors and show results on public available
benchmarks.Comment: GCPR 201
Learning to Extract Motion from Videos in Convolutional Neural Networks
This paper shows how to extract dense optical flow from videos with a
convolutional neural network (CNN). The proposed model constitutes a potential
building block for deeper architectures to allow using motion without resorting
to an external algorithm, \eg for recognition in videos. We derive our network
architecture from signal processing principles to provide desired invariances
to image contrast, phase and texture. We constrain weights within the network
to enforce strict rotation invariance and substantially reduce the number of
parameters to learn. We demonstrate end-to-end training on only 8 sequences of
the Middlebury dataset, orders of magnitude less than competing CNN-based
motion estimation methods, and obtain comparable performance to classical
methods on the Middlebury benchmark. Importantly, our method outputs a
distributed representation of motion that allows representing multiple,
transparent motions, and dynamic textures. Our contributions on network design
and rotation invariance offer insights nonspecific to motion estimation
A multicopper oxidase (Cj1516) and a CopA homologue (Cj1161) are major components of the copper homeostasis system of Campylobacter jejuni
© American Society for Microbiology, 2008. Post-print version of article deposited in accordance with SHERPA RoMEO guidelines.Metal ion homeostasis mechanisms in the food-borne human pathogen Campylobacter jejuni are poorly understood. The Cj1516 gene product is homologous to the multicopper oxidase CueO, which is known to contribute to copper tolerance in Escherichia coli. Here we show, by optical absorbance and electron paramagnetic resonance spectroscopy, that purified recombinant Cj1516 contains both T1 and trinuclear copper centers, which are characteristic of multicopper oxidases. Inductively coupled plasma mass spectrometry revealed that the protein contained approximately six copper atoms per polypeptide. The presence of an N-terminal "twin arginine" signal sequence suggested a periplasmic location for Cj1516, which was confirmed by the presence of p-phenylenediamine (p-PD) oxidase activity in periplasmic fractions of wild-type but not Cj1516 mutant cells. Kinetic studies showed that the pure protein exhibited p-PD, ferroxidase, and cuprous oxidase activities and was able to oxidize an analogue of the bacterial siderophore anthrachelin (3,4-dihydroxybenzoate), although no iron uptake impairment was observed in a Cj1516 mutant. However, this mutant was very sensitive to increased copper levels in minimal media, suggesting a role in copper tolerance. This was supported by increased expression of the Cj1516 gene in copper-rich media. A mutation in a second gene, the Cj1161c gene, encoding a putative CopA homologue, was also found to result in copper hypersensitivity, and a Cj1516 Cj1161c double mutant was found to be more copper sensitive than either single mutant. These observations and the apparent lack of alternative copper tolerance systems suggest that Cj1516 (CueO) and Cj1161 (CopA) are major proteins involved in copper homeostasis in C. jejuni
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
We propose a novel method for learning convolutional neural image
representations without manual supervision. We use motion cues in the form of
optical flow, to supervise representations of static images. The obvious
approach of training a network to predict flow from a single image can be
needlessly difficult due to intrinsic ambiguities in this prediction task. We
instead propose a much simpler learning goal: embed pixels such that the
similarity between their embeddings matches that between their optical flow
vectors. At test time, the learned deep network can be used without access to
video or flow information and transferred to tasks such as image
classification, detection, and segmentation. Our method, which significantly
simplifies previous attempts at using motion for self-supervision, achieves
state-of-the-art results in self-supervision using motion cues, competitive
results for self-supervision in general, and is overall state of the art in
self-supervised pretraining for semantic image segmentation, as demonstrated on
standard benchmarks
BodyNet: Volumetric Inference of 3D Human Body Shapes
Human shape estimation is an important task for video editing, animation and
fashion industry. Predicting 3D human body shape from natural images, however,
is highly challenging due to factors such as variation in human bodies,
clothing and viewpoint. Prior methods addressing this problem typically attempt
to fit parametric body models with certain priors on pose and shape. In this
work we argue for an alternative representation and propose BodyNet, a neural
network for direct inference of volumetric body shape from a single image.
BodyNet is an end-to-end trainable network that benefits from (i) a volumetric
3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate
supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them
results in performance improvement as demonstrated by our experiments. To
evaluate the method, we fit the SMPL model to our network output and show
state-of-the-art results on the SURREAL and Unite the People datasets,
outperforming recent approaches. Besides achieving state-of-the-art
performance, our method also enables volumetric body-part segmentation.Comment: Appears in: European Conference on Computer Vision 2018 (ECCV 2018).
27 page
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filters (DCF) have demonstrated excellent
performance for visual object tracking. The key to their success is the ability
to efficiently exploit available negative data by including all shifted
versions of a training sample. However, the underlying DCF formulation is
restricted to single-resolution feature maps, significantly limiting its
potential. In this paper, we go beyond the conventional DCF framework and
introduce a novel formulation for training continuous convolution filters. We
employ an implicit interpolation model to pose the learning problem in the
continuous spatial domain. Our proposed formulation enables efficient
integration of multi-resolution deep feature maps, leading to superior results
on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color
(+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate).
Additionally, our approach is capable of sub-pixel localization, crucial for
the task of accurate feature point tracking. We also demonstrate the
effectiveness of our learning formulation in extensive feature point tracking
experiments. Code and supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201
Joint Learning of Intrinsic Images and Semantic Segmentation
Semantic segmentation of outdoor scenes is problematic when there are
variations in imaging conditions. It is known that albedo (reflectance) is
invariant to all kinds of illumination effects. Thus, using reflectance images
for semantic segmentation task can be favorable. Additionally, not only
segmentation may benefit from reflectance, but also segmentation may be useful
for reflectance computation. Therefore, in this paper, the tasks of semantic
segmentation and intrinsic image decomposition are considered as a combined
process by exploring their mutual relationship in a joint fashion. To that end,
we propose a supervised end-to-end CNN architecture to jointly learn intrinsic
image decomposition and semantic segmentation. We analyze the gains of
addressing those two problems jointly. Moreover, new cascade CNN architectures
for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as
single tasks. Furthermore, a dataset of 35K synthetic images of natural
environments is created with corresponding albedo and shading (intrinsics), as
well as semantic labels (segmentation) assigned to each object/scene. The
experiments show that joint learning of intrinsic image decomposition and
semantic segmentation is beneficial for both tasks for natural scenes. Dataset
and models are available at: https://ivi.fnwi.uva.nl/cv/intrinsegComment: ECCV 201
- …