text
stringlengths 29
3.31k
| label
listlengths 1
11
|
|---|---|
We devise and analyze algorithms for the empirical policy evaluation problem
in reinforcement learning. Our algorithms explore backward from high-cost
states to find high-value ones, in contrast to forward approaches that work
forward from all states. While several papers have demonstrated the utility of
backward exploration empirically, we conduct rigorous analyses which show that
our algorithms can reduce average-case sample complexity from $O(S \log S)$ to
as low as $O(\log S)$. | [
"cs.LG",
"math.OC",
"stat.ML"
]
|
We propose a novel algorithm for supervised dimensionality reduction named
Manifold Partition Discriminant Analysis (MPDA). It aims to find a linear
embedding space where the within-class similarity is achieved along the
direction that is consistent with the local variation of the data manifold,
while nearby data belonging to different classes are well separated. By
partitioning the data manifold into a number of linear subspaces and utilizing
the first-order Taylor expansion, MPDA explicitly parameterizes the connections
of tangent spaces and represents the data manifold in a piecewise manner. While
graph Laplacian methods capture only the pairwise interaction between data
points, our method capture both pairwise and higher order interactions (using
regional consistency) between data points. This manifold representation can
help to improve the measure of within-class similarity, which further leads to
improved performance of dimensionality reduction. Experimental results on
multiple real-world data sets demonstrate the effectiveness of the proposed
method. | [
"cs.LG",
"cs.AI"
]
|
By chaining a sequence of differentiable invertible transformations,
normalizing flows (NF) provide an expressive method of posterior approximation,
exact density evaluation, and sampling. The trend in normalizing flow
literature has been to devise deeper, more complex transformations to achieve
greater flexibility. We propose an alternative: Gradient Boosted Normalizing
Flows (GBNF) model a density by successively adding new NF components with
gradient boosting. Under the boosting framework, each new NF component
optimizes a sample weighted likelihood objective, resulting in new components
that are fit to the residuals of the previously trained components. The GBNF
formulation results in a mixture model structure, whose flexibility increases
as more components are added. Moreover, GBNFs offer a wider, as opposed to
strictly deeper, approach that improves existing NFs at the cost of additional
training---not more complex transformations. We demonstrate the effectiveness
of this technique for density estimation and, by coupling GBNF with a
variational autoencoder, generative modeling of images. Our results show that
GBNFs outperform their non-boosted analog, and, in some cases, produce better
results with smaller, simpler flows. | [
"cs.LG",
"cs.CV",
"stat.ML"
]
|
We present a generic framework for parallel coordinate descent (CD)
algorithms that includes, as special cases, the original sequential algorithms
Cyclic CD and Stochastic CD, as well as the recent parallel Shotgun algorithm.
We introduce two novel parallel algorithms that are also special
cases---Thread-Greedy CD and Coloring-Based CD---and give performance
measurements for an OpenMP implementation of these. | [
"cs.LG",
"cs.DC",
"stat.ML"
]
|
The de-facto approach to many vision tasks is to start from pretrained visual
representations, typically learned via supervised training on ImageNet. Recent
methods have explored unsupervised pretraining to scale to vast quantities of
unlabeled images. In contrast, we aim to learn high-quality visual
representations from fewer images. To this end, we revisit supervised
pretraining, and seek data-efficient alternatives to classification-based
pretraining. We propose VirTex -- a pretraining approach using semantically
dense captions to learn visual representations. We train convolutional networks
from scratch on COCO Captions, and transfer them to downstream recognition
tasks including image classification, object detection, and instance
segmentation. On all tasks, VirTex yields features that match or exceed those
learned on ImageNet -- supervised or unsupervised -- despite using up to ten
times fewer images. | [
"cs.CV",
"cs.CL"
]
|
Contrary to the ongoing trend in automotive applications towards usage of
more diverse and more sensors, this work tries to solve the complex scene flow
problem under a monocular camera setup, i.e. using a single sensor. Towards
this end, we exploit the latest achievements in single image depth estimation,
optical flow, and sparse-to-dense interpolation and propose a monocular
combination approach (MonoComb) to compute dense scene flow. MonoComb uses
optical flow to relate reconstructed 3D positions over time and interpolates
occluded areas. This way, existing monocular methods are outperformed in
dynamic foreground regions which leads to the second best result among the
competitors on the challenging KITTI 2015 scene flow benchmark. | [
"cs.CV"
]
|
Modern change detection (CD) has achieved remarkable success by the powerful
discriminative ability of deep convolutions. However, high-resolution remote
sensing CD remains challenging due to the complexity of objects in the scene.
Objects with the same semantic concept may show distinct spectral
characteristics at different times and spatial locations. Most recent CD
pipelines using pure convolutions are still struggling to relate long-range
concepts in space-time. Non-local self-attention approaches show promising
performance via modeling dense relations among pixels, yet are computationally
inefficient. Here, we propose a bitemporal image transformer (BIT) to
efficiently and effectively model contexts within the spatial-temporal domain.
Our intuition is that the high-level concepts of the change of interest can be
represented by a few visual words, i.e., semantic tokens. To achieve this, we
express the bitemporal image into a few tokens, and use a transformer encoder
to model contexts in the compact token-based space-time. The learned
context-rich tokens are then feedback to the pixel-space for refining the
original features via a transformer decoder. We incorporate BIT in a deep
feature differencing-based CD framework. Extensive experiments on three CD
datasets demonstrate the effectiveness and efficiency of the proposed method.
Notably, our BIT-based model significantly outperforms the purely convolutional
baseline using only 3 times lower computational costs and model parameters.
Based on a naive backbone (ResNet18) without sophisticated structures (e.g.,
FPN, UNet), our model surpasses several state-of-the-art CD methods, including
better than four recent attention-based methods in terms of efficiency and
accuracy. Our code is available at https://github.com/justchenhao/BIT\_CD. | [
"cs.CV"
]
|
Generative adversarial networks (GANs) implicitly learn the probability
distribution of a dataset and can draw samples from the distribution. This
paper presents, Tabular GAN (TGAN), a generative adversarial network which can
generate tabular data like medical or educational records. Using the power of
deep neural networks, TGAN generates high-quality and fully synthetic tables
while simultaneously generating discrete and continuous variables. When we
evaluate our model on three datasets, we find that TGAN outperforms
conventional statistical generative models in both capturing the correlation
between columns and scaling up for large datasets. | [
"cs.LG",
"stat.ML"
]
|
Deep reinforcement learning (RL) methods generally engage in exploratory
behavior through noise injection in the action space. An alternative is to add
noise directly to the agent's parameters, which can lead to more consistent
exploration and a richer set of behaviors. Methods such as evolutionary
strategies use parameter perturbations, but discard all temporal structure in
the process and require significantly more samples. Combining parameter noise
with traditional RL methods allows to combine the best of both worlds. We
demonstrate that both off- and on-policy methods benefit from this approach
through experimental comparison of DQN, DDPG, and TRPO on high-dimensional
discrete action environments as well as continuous control tasks. Our results
show that RL with parameter noise learns more efficiently than traditional RL
with action space noise and evolutionary strategies individually. | [
"cs.LG",
"cs.AI",
"cs.NE",
"cs.RO",
"stat.ML"
]
|
Two of the most popular modelling paradigms in computer vision are
feed-forward neural networks (FFNs) and probabilistic graphical models (GMs).
Various connections between the two have been studied in recent works, such as
e.g. expressing mean-field based inference in a GM as an FFN. This paper
establishes a new connection between FFNs and GMs. Our key observation is that
any FFN implements a certain approximation of a corresponding Bayesian network
(BN). We characterize various benefits of having this connection. In
particular, it results in a new learning algorithm for BNs. We validate the
proposed methods for a classification problem on CIFAR-10 dataset and for
binary image segmentation on Weizmann Horse dataset. We show that statistically
learned BNs improve performance, having at the same time essentially better
generalization capability, than their FFN counterparts. | [
"stat.ML",
"cs.CV",
"cs.LG"
]
|
Deep learning is a group of exciting new technologies for neural networks.
Through a combination of advanced training techniques and neural network
architectural components, it is now possible to create neural networks that can
handle tabular data, images, text, and audio as both input and output. Deep
learning allows a neural network to learn hierarchies of information in a way
that is like the function of the human brain. This course will introduce the
student to classic neural network structures, Convolution Neural Networks
(CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU),
General Adversarial Networks (GAN), and reinforcement learning. Application of
these architectures to computer vision, time series, security, natural language
processing (NLP), and data generation will be covered. High-Performance
Computing (HPC) aspects will demonstrate how deep learning can be leveraged
both on graphical processing units (GPUs), as well as grids. Focus is primarily
upon the application of deep learning to problems, with some introduction to
mathematical foundations. Readers will use the Python programming language to
implement deep learning using Google TensorFlow and Keras. It is not necessary
to know Python prior to this book; however, familiarity with at least one
programming language is assumed. | [
"cs.LG",
"cs.AI",
"I.2"
]
|
Building performance discrepancies between building design and operation are
one of the causes that lead many new designs fail to achieve their goals and
objectives. One of main factors contributing to the discrepancy is occupant
behaviors. Occupants responding to a new design are influenced by several
factors. Existing building performance models (BPMs) ignore or partially
address those factors (called contextual factors) while developing BPMs. To
potentially reduce the discrepancies and improve the prediction accuracy of
BPMs, this paper proposes a computational framework for learning mixture models
by using Generative Adversarial Networks (GANs) that appropriately combining
existing BPMs with knowledge on occupant behaviors to contextual factors in new
designs. Immersive virtual environments (IVEs) experiments are used to acquire
data on such behaviors. Performance targets are used to guide appropriate
combination of existing BPMs with knowledge on occupant behaviors. The
resulting model obtained is called an augmented BPM. Two different experiments
related to occupant lighting behaviors are shown as case study. The results
reveal that augmented BPMs significantly outperformed existing BPMs with
respect to achieving specified performance targets. The case study confirms the
potential of the computational framework for improving prediction accuracy of
BPMs during design. | [
"cs.LG",
"stat.ML",
"68T05"
]
|
Convolution Neural Network (CNN) recently have been adopted in several
neuroimaging studies for diagnosis capturing disease-specific changes in the
brain. While many of these methods are designed to work with images in $\mathbb
R^n$ exploiting regular structure of the domain, they are not well-suited to
analyze data with irregular structure such as brain connectivity. As there is
significant interest in understanding the altered interactions between
different brain regions that lead to neuro-disorders, it is important to
develop data-driven methods that work with a population of graph data for
traditional prediction tasks. In this regime, we propose a novel CNN-based
framework with adaptive graph transforms to learn the most disease-relevant
connectome feature maps which have the highest discrimination power across
diagnostic categories. The backbone of our framework is a multi-resolution
representation of the graph matrix which is steered by a set of wavelet-like
graph transforms. In this context, our supervised graph learning framework
outperforms conventional graph methods that predict diagnostic label only based
on the underlying individual graph. Our extensive experiments on two real
datasets of functional and structural brain networks show that our
multi-resolution framework achieves significantly higher accuracy, precision
and recall in predicting diagnostic labels and identifying disease-specific
brain connectivities that are associated with brain disorders such as
Attention-Deficit/Hyperactivity Disorder (ADHD) and Alzheimer's Disease (AD). | [
"cs.CV",
"cs.LG"
]
|
Previous studies have found that an adversary attacker can often infer
unintended input information from intermediate-layer features. We study the
possibility of preventing such adversarial inference, yet without too much
accuracy degradation. We propose a generic method to revise the neural network
to boost the challenge of inferring input attributes from features, while
maintaining highly accurate outputs. In particular, the method transforms
real-valued features into complex-valued ones, in which the input is hidden in
a randomized phase of the transformed features. The knowledge of the phase acts
like a key, with which any party can easily recover the output from the
processing result, but without which the party can neither recover the output
nor distinguish the original input. Preliminary experiments on various datasets
and network structures have shown that our method significantly diminishes the
adversary's ability in inferring about the input while largely preserves the
resulting accuracy. | [
"cs.LG",
"cs.CR",
"stat.ML"
]
|
Autonomous cars need continuously updated depth information. Thus far, depth
is mostly estimated independently for a single frame at a time, even if the
method starts from video input. Our method produces a time series of depth
maps, which makes it an ideal candidate for online learning approaches. In
particular, we put three different types of depth estimation (supervised depth
prediction, self-supervised depth prediction, and self-supervised depth
completion) into a common framework. We integrate the corresponding networks
with a ConvLSTM such that the spatiotemporal structures of depth across frames
can be exploited to yield a more accurate depth estimation. Our method is
flexible. It can be applied to monocular videos only or be combined with
different types of sparse depth patterns. We carefully study the architecture
of the recurrent network and its training strategy. We are first to
successfully exploit recurrent networks for real-time self-supervised monocular
depth estimation and completion. Extensive experiments show that our recurrent
method outperforms its image-based counterpart consistently and significantly
in both self-supervised scenarios. It also outperforms previous depth
estimation methods of the three popular groups. Please refer to
https://www.trace.ethz.ch/publications/2020/rec_depth_estimation/ for details. | [
"cs.CV",
"cs.LG",
"cs.RO",
"eess.IV"
]
|
Acquisition-to-acquisition signal intensity variations (non-standardness) are
inherent in MR images. Standardization is a post processing method for
correcting inter-subject intensity variations through transforming all images
from the given image gray scale into a standard gray scale wherein similar
intensities achieve similar tissue meanings. The lack of a standard image
intensity scale in MRI leads to many difficulties in tissue characterizability,
image display, and analysis, including image segmentation. This phenomenon has
been documented well; however, effects of standardization on medical image
registration have not been studied yet. In this paper, we investigate the
influence of intensity standardization in registration tasks with systematic
and analytic evaluations involving clinical MR images. We conducted nearly
20,000 clinical MR image registration experiments and evaluated the quality of
registrations both quantitatively and qualitatively. The evaluations show that
intensity variations between images degrades the accuracy of registration
performance. The results imply that the accuracy of image registration not only
depends on spatial and geometric similarity but also on the similarity of the
intensity values for the same tissues in different images. | [
"cs.CV"
]
|
Domain adaptation for semantic image segmentation is very necessary since
manually labeling large datasets with pixel-level labels is expensive and time
consuming. Existing domain adaptation techniques either work on limited
datasets, or yield not so good performance compared with supervised learning.
In this paper, we propose a novel bidirectional learning framework for domain
adaptation of segmentation. Using the bidirectional learning, the image
translation model and the segmentation adaptation model can be learned
alternatively and promote to each other. Furthermore, we propose a
self-supervised learning algorithm to learn a better segmentation adaptation
model and in return improve the image translation model. Experiments show that
our method is superior to the state-of-the-art methods in domain adaptation of
segmentation with a big margin. The source code is available at
https://github.com/liyunsheng13/BDL. | [
"cs.CV"
]
|
The self-attention mechanism has attracted wide publicity for its most
important advantage of modeling long dependency, and its variations in computer
vision tasks, the non-local block tries to model the global dependency of the
input feature maps. Gathering global contextual information will inevitably
need a tremendous amount of memory and computing resources, which has been
extensively studied in the past several years. However, there is a further
problem with the self-attention scheme: is all information gathered from the
global scope helpful for the contextual modelling? To our knowledge, few
studies have focused on the problem. Aimed at both questions this paper
proposes the salient positions-based attention scheme SPANet, which is inspired
by some interesting observations on the attention maps and affinity matrices
generated in self-attention scheme. We believe these observations are
beneficial for better understanding of the self-attention. SPANet uses the
salient positions selection algorithm to select only a limited amount of
salient points to attend in the attention map computing. This approach will not
only spare a lot of memory and computing resources, but also try to distill the
positive information from the transformation of the input feature maps. In the
implementation, considering the feature maps with channel high dimensions,
which are completely different from the general visual image, we take the
squared power of the feature maps along the channel dimension as the saliency
metric of the positions. In general, different from the non-local block method,
SPANet models the contextual information using only the selected positions
instead of all, along the channel dimension instead of space dimension. Our
source code is available at https://github.com/likyoo/SPANet. | [
"cs.CV"
]
|
In this paper, we propose the distributed tree kernels (DTK) as a novel
method to reduce time and space complexity of tree kernels. Using a linear
complexity algorithm to compute vectors for trees, we embed feature spaces of
tree fragments in low-dimensional spaces where the kernel computation is
directly done with dot product. We show that DTKs are faster, correlate with
tree kernels, and obtain a statistically similar performance in two natural
language processing tasks. | [
"cs.LG",
"stat.ML"
]
|
We present a neural transducer model with visual attention that learns to
generate LaTeX markup of a real-world math formula given its image. Applying
sequence modeling and transduction techniques that have been very successful
across modalities such as natural language, image, handwriting, speech and
audio; we construct an image-to-markup model that learns to produce
syntactically and semantically correct LaTeX markup code over 150 words long
and achieves a BLEU score of 89%; improving upon the previous state-of-art for
the Im2Latex problem. We also demonstrate with heat-map visualization how
attention helps in interpreting the model and can pinpoint (detect and
localize) symbols on the image accurately despite having been trained without
any bounding box data. | [
"cs.LG",
"cs.CL",
"cs.CV",
"cs.NE"
]
|
We use methods from Riemann geometry to investigate transformations between
the color spaces of color-normal and color weak observers. The two main
applications are the simulation of the perception of a color weak observer for
a color normal observer and the compensation of color images in a way that a
color weak observer has approximately the same perception as a color normal
observer. The metrics in the color spaces of interest are characterized with
the help of ellipsoids defined by the just-noticable-differences between color
which are measured with the help of color-matching experiments. The constructed
mappings are isometries of Riemann spaces that preserve the perceived
color-differences for both observers. Among the two approaches to build such an
isometry, we introduce normal coordinates in Riemann spaces as a tool to
construct a global color-weak compensation map. Compared to previously used
methods this method is free from approximation errors due to local
linearizations and it avoids the problem of shifting locations of the origin of
the local coordinate system. We analyse the variations of the Riemann metrics
for different observers obtained from new color matching experiments and
describe three variations of the basic method. The performance of the methods
is evaluated with the help of semantic differential (SD) tests. | [
"cs.CV",
"I.2.10; I.4.8; I.5"
]
|
We present multiresolution tree-structured networks to process point clouds
for 3D shape understanding and generation tasks. Our network represents a 3D
shape as a set of locality-preserving 1D ordered list of points at multiple
resolutions. This allows efficient feed-forward processing through 1D
convolutions, coarse-to-fine analysis through a multi-grid architecture, and it
leads to faster convergence and small memory footprint during training. The
proposed tree-structured encoders can be used to classify shapes and outperform
existing point-based architectures on shape classification benchmarks, while
tree-structured decoders can be used for generating point clouds directly and
they outperform existing approaches for image-to-shape inference tasks learned
using the ShapeNet dataset. Our model also allows unsupervised learning of
point-cloud based shapes by using a variational autoencoder, leading to
higher-quality generated shapes. | [
"cs.CV",
"cs.GR",
"cs.LG"
]
|
Online nonnegative matrix factorization (ONMF) is a matrix factorization
technique in the online setting where data are acquired in a streaming fashion
and the matrix factors are updated each time. This enables factor analysis to
be performed concurrently with the arrival of new data samples. In this
article, we demonstrate how one can use online nonnegative matrix factorization
algorithms to learn joint dictionary atoms from an ensemble of correlated data
sets. We propose a temporal dictionary learning scheme for time-series data
sets, based on ONMF algorithms. We demonstrate our dictionary learning
technique in the application contexts of historical temperature data, video
frames, and color images. | [
"cs.LG"
]
|
Point cloud upsampling aims to generate dense point clouds from given sparse
ones, which is a challenging task due to the irregular and unordered nature of
point sets. To address this issue, we present a novel deep learning-based
model, called PU-Flow,which incorporates normalizing flows and feature
interpolation techniques to produce dense points uniformly distributed on the
underlying surface. Specifically, we formulate the upsampling process as point
interpolation in a latent space, where the interpolation weights are adaptively
learned from local geometric context, and exploit the invertible
characteristics of normalizing flows to transform points between Euclidean and
latent spaces. We evaluate PU-Flow on a wide range of 3D models with sharp
features and high-frequency details. Qualitative and quantitative results show
that our method outperforms state-of-the-art deep learning-based approaches in
terms of reconstruction quality, proximity-to-surface accuracy, and computation
efficiency. | [
"cs.CV"
]
|
Deep neural networks (DNNs) have been widely used in the fields such as
natural language processing, computer vision and image recognition. But several
studies have been shown that deep neural networks can be easily fooled by
artificial examples with some perturbations, which are widely known as
adversarial examples. Adversarial examples can be used to attack deep neural
networks or to improve the robustness of deep neural networks. A common way of
generating adversarial examples is to first generate some noises and then add
them into original examples. In practice, different examples have different
noise-sensitive. To generate an effective adversarial example, it may be
necessary to add a lot of noise to low noise-sensitive example, which may make
the adversarial example meaningless. In this paper, we propose a
noise-sensitivity-analysis-based test prioritization technique to pick out
examples by their noise sensitivity. We construct an experiment to validate our
approach on four image sets and two DNN models, which shows that examples are
sensitive to noise and our method can effectively pick out examples by their
noise sensitivity. | [
"cs.CV",
"cs.LG",
"stat.ML"
]
|
Learning a joint language-visual embedding has a number of very appealing
properties and can result in variety of practical application, including
natural language image/video annotation and search. In this work, we study
three different joint language-visual neural network model architectures. We
evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1)
Standard Ranking for video annotation and retrieval 2) Our proposed movie
multiple-choice test. This test facilitate automatic evaluation of
visual-language models for natural language video annotation based on human
activities. In addition to original Audio Description (AD) captions, provided
as part of LSMDC16, we collected and will make available a) manually generated
re-phrasings of those captions obtained using Amazon MTurk b) automatically
generated human activity elements in "Predicate + Object" (PO) phrases based on
"Knowlywood", an activity knowledge mining model. Our best model archives
Recall@10 of 19.2% on annotation and 18.9% on video retrieval tasks for subset
of 1000 samples. For multiple-choice test, our best model achieve accuracy
58.11% over whole LSMDC16 public test-set. | [
"cs.CV"
]
|
As an important and challenging problem in computer vision, learning based
optical flow estimation aims to discover the intrinsic correspondence structure
between two adjacent video frames through statistical learning. Therefore, a
key issue to solve in this area is how to effectively model the multi-scale
correspondence structure properties in an adaptive end-to-end learning fashion.
Motivated by this observation, we propose an end-to-end multi-scale
correspondence structure learning (MSCSL) approach for optical flow estimation.
In principle, the proposed MSCSL approach is capable of effectively capturing
the multi-scale inter-image-correlation correspondence structures within a
multi-level feature space from deep learning. Moreover, the proposed MSCSL
approach builds a spatial Conv-GRU neural network model to adaptively model the
intrinsic dependency relationships among these multi-scale correspondence
structures. Finally, the above procedures for correspondence structure learning
and multi-scale dependency modeling are implemented in a unified end-to-end
deep learning framework. Experimental results on several benchmark datasets
demonstrate the effectiveness of the proposed approach. | [
"cs.CV"
]
|
We demonstrate the first reinforcement-learning application for robots
equipped with an event camera. Because of the considerably lower latency of the
event camera, it is possible to achieve much faster control of robots compared
with the existing vision-based reinforcement-learning applications using
standard cameras. To handle a stream of events for reinforcement learning, we
introduced an image-like feature and demonstrated the feasibility of training
an agent in a simulator for two tasks: fast collision avoidance and obstacle
tracking. Finally, we set up a robot with an event camera in the real world and
then transferred the agent trained in the simulator, resulting in successful
fast avoidance of randomly thrown objects. Incorporating event camera into
reinforcement learning opens new possibilities for various robotics
applications that require swift control, such as autonomous vehicles and
drones, through end-to-end learning approaches. | [
"cs.LG",
"cs.AI",
"cs.RO"
]
|
Visual question answering (Visual QA) has attracted significant attention
these years. While a variety of algorithms have been proposed, most of them are
built upon different combinations of image and language features as well as
multi-modal attention and fusion. In this paper, we investigate an alternative
approach inspired by conventional QA systems that operate on knowledge graphs.
Specifically, we investigate the use of scene graphs derived from images for
Visual QA: an image is abstractly represented by a graph with nodes
corresponding to object entities and edges to object relationships. We adapt
the recently proposed graph network (GN) to encode the scene graph and perform
structured reasoning according to the input question. Our empirical studies
demonstrate that scene graphs can already capture essential information of
images and graph networks have the potential to outperform state-of-the-art
Visual QA algorithms but with a much cleaner architecture. By analyzing the
features generated by GNs we can further interpret the reasoning process,
suggesting a promising direction towards explainable Visual QA. | [
"cs.CV",
"cs.CL"
]
|
Despite the recent development in the topic of explainable AI/ML for image
and text data, the majority of current solutions are not suitable to explain
the prediction of neural network models when the datasets are tabular and their
features are in high-dimensional vectorized formats. To mitigate this
limitation, therefore, we borrow two notable ideas (i.e., "explanation by
intervention" from causality and "explanation are contrastive" from philosophy)
and propose a novel solution, named as GRACE, that better explains neural
network models' predictions for tabular datasets. In particular, given a
model's prediction as label X, GRACE intervenes and generates a
minimally-modified contrastive sample to be classified as Y, with an intuitive
textual explanation, answering the question of "Why X rather than Y?" We carry
out comprehensive experiments using eleven public datasets of different scales
and domains (e.g., # of features ranges from 5 to 216) and compare GRACE with
competing baselines on different measures: fidelity, conciseness, info-gain,
and influence. The user-studies show that our generated explanation is not only
more intuitive and easy-to-understand but also facilitates end-users to make as
much as 60% more accurate post-explanation decisions than that of Lime. | [
"cs.LG",
"cs.AI",
"stat.ML"
]
|
Prior domain knowledge can greatly help to learn generative models. However,
it is often too costly to hard-code prior knowledge as a specific model
architecture, so we often have to use general-purpose models. In this paper, we
propose a method to incorporate prior knowledge of feature relations into the
learning of general-purpose generative models. To this end, we formulate a
regularizer that makes the marginals of a generative model to follow prescribed
relative dependence of features. It can be incorporated into off-the-shelf
learning methods of many generative models, including variational autoencoders
and generative adversarial networks, as its gradients can be computed using
standard backpropagation techniques. We show the effectiveness of the proposed
method with experiments on multiple types of datasets and generative models. | [
"cs.LG",
"stat.ML"
]
|
For most of the object detectors based on multi-scale feature maps, the
shallow layers are rich in fine spatial information and thus mainly responsible
for small object detection. The performance of small object detection, however,
is still less than satisfactory because of the deficiency of semantic
information on shallow feature maps. In this paper, we design a Multi-scale
Deconvolutional Single Shot Detector (MDSSD), especially for small object
detection. In MDSSD, multiple high-level feature maps at different scales are
upsampled simultaneously to increase the spatial resolution. Afterwards, we
implement the skip connections with low-level feature maps via Fusion Block.
The fusion feature maps, named Fusion Module, are of strong feature
representational power of small instances. It is noteworthy that these
high-level feature maps utilized in Fusion Block preserve both strong semantic
information and some fine details of small instances, rather than the top-most
layer where the representation of fine details for small objects are
potentially wiped out. The proposed framework achieves 77.6% mAP for small
object detection on the challenging dataset TT100K with 512 x 512 input,
outperforming other detectors with a large margin. Moreover, it can also
achieve state-of-the-art results for general object detection on PASCAL VOC2007
test and MS COCO test-dev2015, especially achieving 2 to 5 points improvement
on small object categories. | [
"cs.CV"
]
|
Predicting a scene graph that captures visual entities and their interactions
in an image has been considered a crucial step towards full scene
comprehension. Recent scene graph generation (SGG) models have shown their
capability of capturing the most frequent relations among visual entities.
However, the state-of-the-art results are still far from satisfactory, e.g.
models can obtain 31% in overall recall R@100, whereas the likewise important
mean class-wise recall mR@100 is only around 8% on Visual Genome (VG). The
discrepancy between R and mR results urges to shift the focus from pursuing a
high R to a high mR with a still competitive R. We suspect that the observed
discrepancy stems from both the annotation bias and sparse annotations in VG,
in which many visual entity pairs are either not annotated at all or only with
a single relation when multiple ones could be valid. To address this particular
issue, we propose a novel SGG training scheme that capitalizes on self-learned
knowledge. It involves two relation classifiers, one offering a less biased
setting for the other to base on. The proposed scheme can be applied to most of
the existing SGG models and is straightforward to implement. We observe
significant relative improvements in mR (between +6.6% and +20.4%) and
competitive or better R (between -2.4% and 0.3%) across all standard SGG tasks. | [
"cs.CV",
"cs.LG"
]
|
Recently, the advancement of 3D point clouds in deep learning has attracted
intensive research in different application domains such as computer vision and
robotic tasks. However, creating feature representation of robust,
discriminative from unordered and irregular point clouds is challenging. In
this paper, our ultimate goal is to provide a comprehensive overview of the
point clouds feature representation which uses attention models. More than 75+
key contributions in the recent three years are summarized in this survey,
including the 3D objective detection, 3D semantic segmentation, 3D pose
estimation, point clouds completion etc. We provide a detailed characterization
(1) the role of attention mechanisms, (2) the usability of attention models
into different tasks, (3) the development trend of key technology. | [
"cs.CV",
"cs.AI"
]
|
Self-supervised learning is currently gaining a lot of attention, as it
allows neural networks to learn robust representations from large quantities of
unlabeled data. Additionally, multi-task learning can further improve
representation learning by training networks simultaneously on related tasks,
leading to significant performance improvements. In this paper, we propose
three novel self-supervised auxiliary tasks to train graph-based neural network
models in a multi-task fashion. Since Graph Convolutional Networks are among
the most promising approaches for capturing relationships among structured data
points, we use them as a building block to achieve competitive results on
standard semi-supervised graph classification tasks. | [
"cs.LG"
]
|
Exploring contextual information in convolution neural networks (CNNs) has
gained substantial attention in recent years for semantic segmentation. This
paper introduces a Bi-directional Contextual Aggregating Network, called
BiCANet, for semantic segmentation. Unlike previous approaches that encode
context in feature space, BiCANet aggregates contextual cues from a categorical
perspective, which is mainly consist of three parts: contextual condensed
projection block (CCPB), bi-directional context interaction block (BCIB), and
muti-scale contextual fusion block (MCFB). More specifically, CCPB learns a
category-based mapping through a split-transform-merge architecture, which
condenses contextual cues with different receptive fields from intermediate
layer. BCIB, on the other hand, employs dense skipped-connections to enhance
the class-level context exchanging. Finally, MCFB integrates multi-scale
contextual cues by investigating short- and long-ranged spatial dependencies.
To evaluate BiCANet, we have conducted extensive experiments on three semantic
segmentation datasets: PASCAL VOC 2012, Cityscapes, and ADE20K. The
experimental results demonstrate that BiCANet outperforms recent
state-of-the-art networks without any postprocess techniques. Particularly,
BiCANet achieves the mIoU score of 86.7%, 82.4% and 38.66% on PASCAL VOC 2012,
Cityscapes and ADE20K testset, respectively. | [
"cs.CV",
"eess.IV"
]
|
Unsupervised person re-identification (Re-ID) aims to match pedestrian images
from different camera views in unsupervised setting. Existing methods for
unsupervised person Re-ID are usually built upon the pseudo labels from
clustering. However, the quality of clustering depends heavily on the quality
of the learned features, which are overwhelmingly dominated by the colors in
images especially in the unsupervised setting. In this paper, we propose a
Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised
person Re-ID, in which cluster structure is leveraged to guide the feature
learning in a properly designed asymmetric contrastive learning framework. To
be specific, we propose a novel cluster-level contrastive loss to help the
siamese network effectively mine the invariance in feature learning with
respect to the cluster structure within and between different data augmentation
views, respectively. Extensive experiments conducted on three benchmark
datasets demonstrate superior performance of our proposal. | [
"cs.CV"
]
|
Convolution Neural Networks (CNN) have been extremely successful in solving
intensive computer vision tasks. The convolutional filters used in CNNs have
played a major role in this success, by extracting useful features from the
inputs. Recently researchers have tried to boost the performance of CNNs by
re-calibrating the feature maps produced by these filters, e.g.,
Squeeze-and-Excitation Networks (SENets). These approaches have achieved better
performance by Exciting up the important channels or feature maps while
diminishing the rest. However, in the process, architectural complexity has
increased. We propose an architectural block that introduces much lower
complexity than the existing methods of CNN performance boosting while
performing significantly better than them. We carry out experiments on the
CIFAR, ImageNet and MS-COCO datasets, and show that the proposed block can
challenge the state-of-the-art results. Our method boosts the ResNet-50
architecture to perform comparably to the ResNet-152 architecture, which is a
three times deeper network, on classification. We also show experimentally that
our method is not limited to classification but also generalizes well to other
tasks such as object detection. | [
"cs.CV",
"cs.AI",
"cs.LG"
]
|
Some reinforcement learning methods suffer from high sample complexity
causing them to not be practical in real-world situations. $Q$-function reuse,
a transfer learning method, is one way to reduce the sample complexity of
learning, potentially improving usefulness of existing algorithms. Prior work
has shown the empirical effectiveness of $Q$-function reuse for various
environments when applied to model-free algorithms. To the best of our
knowledge, there has been no theoretical work showing the regret of
$Q$-function reuse when applied to the tabular, model-free setting. We aim to
bridge the gap between theoretical and empirical work in $Q$-function reuse by
providing some theoretical insights on the effectiveness of $Q$-function reuse
when applied to the $Q$-learning with UCB-Hoeffding algorithm. Our main
contribution is showing that in a specific case if $Q$-function reuse is
applied to the $Q$-learning with UCB-Hoeffding algorithm it has a regret that
is independent of the state or action space. We also provide empirical results
supporting our theoretical findings. | [
"cs.LG"
]
|
In this paper, we introduce a new public dataset for 6D object pose
estimation and instance segmentation for industrial bin-picking. The dataset
comprises both synthetic and real-world scenes. For both, point clouds, depth
images, and annotations comprising the 6D pose (position and orientation), a
visibility score, and a segmentation mask for each object are provided. Along
with the raw data, a method for precisely annotating real-world scenes is
proposed. To the best of our knowledge, this is the first public dataset for 6D
object pose estimation and instance segmentation for bin-picking containing
sufficiently annotated data for learning-based approaches. Furthermore, it is
one of the largest public datasets for object pose estimation in general. The
dataset is publicly available at http://www.bin-picking.ai/en/dataset.html. | [
"cs.CV",
"cs.AI",
"cs.RO"
]
|
Limited availability of annotated medical imaging data poses a challenge for
deep learning algorithms. Although transfer learning minimizes this hurdle in
general, knowledge transfer across disparate domains is shown to be less
effective. On the other hand, smaller architectures were found to be more
compelling in learning better features. Consequently, we propose a lightweight
architecture that uses mixed asymmetric kernels (MAKNet) to reduce the number
of parameters significantly. Additionally, we train the proposed architecture
using semi-supervised learning to provide pseudo-labels for a large medical
dataset to assist with transfer learning. The proposed MAKNet provides better
classification performance with $60 - 70\%$ less parameters than popular
architectures. Experimental results also highlight the importance of
domain-specific knowledge for effective transfer learning. | [
"cs.CV"
]
|
The utilization of prior knowledge about anomalies is an essential issue for
anomaly detections. Recently, the visual attention mechanism has become a
promising way to improve the performance of CNNs for some computer vision
tasks. In this paper, we propose a novel model called Layer-wise External
Attention Network (LEA-Net) for efficient image anomaly detection. The core
idea relies on the integration of unsupervised and supervised anomaly detectors
via the visual attention mechanism. Our strategy is as follows: (i) Prior
knowledge about anomalies is represented as the anomaly map generated by
unsupervised learning of normal instances, (ii) The anomaly map is translated
to an attention map by the external network, (iii) The attention map is then
incorporated into intermediate layers of the anomaly detection network.
Notably, this layer-wise external attention can be applied to any CNN model in
an end-to-end training manner. For a pilot study, we validate LEA-Net on color
anomaly detection tasks. Through extensive experiments on PlantVillage, MVTec
AD, and Cloud datasets, we demonstrate that the proposed layer-wise visual
attention mechanism consistently boosts anomaly detection performances of an
existing CNN model, even on imbalanced datasets. Moreover, we show that our
attention mechanism successfully boosts the performance of several CNN models. | [
"cs.CV",
"cs.LG"
]
|
Image segmentation is one of the most essential biomedical image processing
problems for different imaging modalities, including microscopy and X-ray in
the Internet-of-Medical-Things (IoMT) domain. However, annotating biomedical
images is knowledge-driven, time-consuming, and labor-intensive, making it
difficult to obtain abundant labels with limited costs. Active learning
strategies come into ease the burden of human annotation, which queries only a
subset of training data for annotation. Despite receiving attention, most of
active learning methods generally still require huge computational costs and
utilize unlabeled data inefficiently. They also tend to ignore the intermediate
knowledge within networks. In this work, we propose a deep active
semi-supervised learning framework, DSAL, combining active learning and
semi-supervised learning strategies. In DSAL, a new criterion based on deep
supervision mechanism is proposed to select informative samples with high
uncertainties and low uncertainties for strong labelers and weak labelers
respectively. The internal criterion leverages the disagreement of intermediate
features within the deep learning network for active sample selection, which
subsequently reduces the computational costs. We use the proposed criteria to
select samples for strong and weak labelers to produce oracle labels and pseudo
labels simultaneously at each active learning iteration in an ensemble learning
manner, which can be examined with IoMT Platform. Extensive experiments on
multiple medical image datasets demonstrate the superiority of the proposed
method over state-of-the-art active learning methods. | [
"cs.CV",
"cs.AI",
"eess.IV"
]
|
Deep image embedding provides a way to measure the semantic similarity of two
images. It plays a central role in many applications such as image search, face
verification, and zero-shot learning. It is desirable to have a universal deep
embedding model applicable to various domains of images. However, existing
methods mainly rely on training specialist embedding models each of which is
applicable to images from a single domain. In this paper, we study an important
but unexplored task: how to train a single universal image embedding model to
match the performance of several specialists on each specialist's domain.
Simply fusing the training data from multiple domains cannot solve this problem
because some domains become overfitted sooner when trained together using
existing methods. Therefore, we propose to distill the knowledge in multiple
specialists into a universal embedding to solve this problem. In contrast to
existing embedding distillation methods that distill the absolute distances
between images, we transform the absolute distances between images into a
probabilistic distribution and minimize the KL-divergence between the
distributions of the specialists and the universal embedding. Using several
public datasets, we validate that our proposed method accomplishes the goal of
universal image embedding. | [
"cs.CV"
]
|
Convolutional neural networks (CNNs) have so far been the de-facto model for
visual data. Recent work has shown that (Vision) Transformer models (ViT) can
achieve comparable or even superior performance on image classification tasks.
This raises a central question: how are Vision Transformers solving these
tasks? Are they acting like convolutional networks, or learning entirely
different visual representations? Analyzing the internal representation
structure of ViTs and CNNs on image classification benchmarks, we find striking
differences between the two architectures, such as ViT having more uniform
representations across all layers. We explore how these differences arise,
finding crucial roles played by self-attention, which enables early aggregation
of global information, and ViT residual connections, which strongly propagate
features from lower to higher layers. We study the ramifications for spatial
localization, demonstrating ViTs successfully preserve input spatial
information, with noticeable effects from different classification methods.
Finally, we study the effect of (pretraining) dataset scale on intermediate
features and transfer learning, and conclude with a discussion on connections
to new architectures such as the MLP-Mixer. | [
"cs.CV",
"cs.AI",
"cs.LG",
"stat.ML"
]
|
This paper explores four different visualization techniques for long
short-term memory (LSTM) networks applied to continuous-valued time series. On
the datasets analysed, we find that the best visualization technique is to
learn an input deletion mask that optimally reduces the true class score. With
a specific focus on single-lead electrocardiograms from the MIT-BIH arrhythmia
dataset, we show that salient input features for the LSTM classifier align well
with medical theory. | [
"stat.ML",
"cs.LG"
]
|
After the COVID-19 outbreak, it has become important to automatically detect
whether people are wearing masks in order to reduce risk of front-line workers.
In addition, processing user data locally is a great way to address both
privacy and network bandwidth issues. In this paper, we present a
light-weighted model for detecting whether people in a particular area wear
masks, which can also be deployed on Coral Dev Board, a commercially available
development board containing Google Edge TPU. Our approach combines the object
detecting network based on MobileNetV2 plus SSD and the quantization scheme for
integer-only hardware. As a result, the lighter model in the Edge TPU has a
significantly lower latency which is more appropriate for real-time execution
while maintaining accuracy comparable to a floating point device. | [
"cs.CV",
"cs.LG"
]
|
In real-world decision-making problems, for instance in the fields of
finance, robotics or autonomous driving, keeping uncertainty under control is
as important as maximizing expected returns. Risk aversion has been addressed
in the reinforcement learning literature through risk measures related to the
variance of returns. However, in many cases, the risk is measured not only on a
long-term perspective, but also on the step-wise rewards (e.g., in trading, to
ensure the stability of the investment bank, it is essential to monitor the
risk of portfolio positions on a daily basis). In this paper, we define a novel
measure of risk, which we call reward volatility, consisting of the variance of
the rewards under the state-occupancy measure. We show that the reward
volatility bounds the return variance so that reducing the former also
constrains the latter. We derive a policy gradient theorem with a new objective
function that exploits the mean-volatility relationship, and develop an
actor-only algorithm. Furthermore, thanks to the linearity of the Bellman
equations defined under the new objective function, it is possible to adapt the
well-known policy gradient algorithms with monotonic improvement guarantees
such as TRPO in a risk-averse manner. Finally, we test the proposed approach in
two simulated financial environments. | [
"cs.LG",
"math.OC",
"stat.ML"
]
|
We propose a light-weight variational framework for online tracking of object
segmentations in videos based on optical flow and image boundaries. While
high-end computer vision methods on this task rely on sequence specific
training of dedicated CNN architectures, we show the potential of a variational
model, based on generic video information from motion and color. Such cues are
usually required for tasks such as robot navigation or grasp estimation. We
leverage them directly for video object segmentation and thus provide accurate
segmentations at potentially very low extra cost. Our simple method can provide
competitive results compared to the costly CNN-based methods with parameter
tuning. Furthermore, we show that our approach can be combined with
state-of-the-art CNN-based segmentations in order to improve over their
respective results. We evaluate our method on the datasets DAVIS 16,17 and
SegTrack v2. | [
"cs.CV"
]
|
Revealing hidden features in unlabeled data is called unsupervised feature
learning, which plays an important role in pretraining a deep neural network.
Here we provide a statistical mechanics analysis of the unsupervised learning
in a restricted Boltzmann machine with binary synapses. A message passing
equation to infer the hidden feature is derived, and furthermore, variants of
this equation are analyzed. A statistical analysis by replica theory describes
the thermodynamic properties of the model. Our analysis confirms an entropy
crisis preceding the non-convergence of the message passing equation,
suggesting a discontinuous phase transition as a key characteristic of the
restricted Boltzmann machine. Continuous phase transition is also confirmed
depending on the embedded feature strength in the data. The mean-field result
under the replica symmetric assumption agrees with that obtained by running
message passing algorithms on single instances of finite sizes. Interestingly,
in an approximate Hopfield model, the entropy crisis is absent, and a
continuous phase transition is observed instead. We also develop an iterative
equation to infer the hyper-parameter (temperature) hidden in the data, which
in physics corresponds to iteratively imposing Nishimori condition. Our study
provides insights towards understanding the thermodynamic properties of the
restricted Boltzmann machine learning, and moreover important theoretical basis
to build simplified deep networks. | [
"cs.LG",
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"cs.NE",
"q-bio.NC"
]
|
We propose a dynamic neighborhood aggregation (DNA) procedure guided by
(multi-head) attention for representation learning on graphs. In contrast to
current graph neural networks which follow a simple neighborhood aggregation
scheme, our DNA procedure allows for a selective and node-adaptive aggregation
of neighboring embeddings of potentially differing locality. In order to avoid
overfitting, we propose to control the channel-wise connections between input
and output by making use of grouped linear projections. In a number of
transductive node-classification experiments, we demonstrate the effectiveness
of our approach. | [
"cs.LG",
"stat.ML"
]
|
In this paper, we introduce a collaborative training algorithm of balanced
random forests with convolutional neural networks for domain adaptation tasks.
In real scenarios, most domain adaptation algorithms face the challenges from
noisy, insufficient training data and open set categorization. In such cases,
conventional methods suffer from overfitting and fail to successfully transfer
the knowledge of the source to the target domain. To address these issues, the
following two techniques are proposed. First, we introduce the optimized
decision tree construction method with convolutional neural networks, in which
the data at each node are split into equal sizes while maximizing the
information gain. It generates balanced decision trees on deep features because
of the even-split constraint, which contributes to enhanced discrimination
power and reduced overfitting problem. Second, to tackle the domain
misalignment problem, we propose the domain alignment loss which penalizes
uneven splits of the source and target domain data. By collaboratively
optimizing the information gain of the labeled source data as well as the
entropy of unlabeled target data distributions, the proposed CoBRF algorithm
achieves significantly better performance than the state-of-the-art methods. | [
"cs.CV"
]
|
In this paper, we investigate deep image synthesis guided by sketch, color,
and texture. Previous image synthesis methods can be controlled by sketch and
color strokes but we are the first to examine texture control. We allow a user
to place a texture patch on a sketch at arbitrary locations and scales to
control the desired output texture. Our generative network learns to synthesize
objects consistent with these texture suggestions. To achieve this, we develop
a local texture loss in addition to adversarial and content loss to train the
generative network. We conduct experiments using sketches generated from real
images and textures sampled from a separate texture database and results show
that our proposed algorithm is able to generate plausible images that are
faithful to user controls. Ablation studies show that our proposed pipeline can
generate more realistic images than adapting existing methods directly. | [
"cs.CV",
"cs.GR"
]
|
LiDAR-based SLAM algorithms are extensively studied to providing robust and
accurate positioning for autonomous driving vehicles (ADV) in the past decades.
Satisfactory performance can be obtained using high-grade 3D LiDAR with 64
channels, which can provide dense point clouds. Unfortunately, the high price
significantly prevents its extensive commercialization in ADV. The
cost-effective 3D LiDAR with 16 channels is a promising replacement. However,
only limited and sparse point clouds can be provided by the 16 channels LiDAR,
which cannot guarantee sufficient positioning accuracy for ADV in challenging
dynamic environments. The high-resolution image from the low-cost camera can
provide ample information about the surroundings. However, the explicit depth
information is not available from the image. Inspired by the complementariness
of 3D LiDAR and camera, this paper proposes to make use of the high-resolution
images from a camera to enrich the raw 3D point clouds from the low-cost 16
channels LiDAR based on a state-of-the-art deep learning algorithm. An ERFNet
is firstly employed to segment the image with the aid of the raw sparse 3D
point clouds. Meanwhile, the sparse convolutional neural network is employed to
predict the dense point clouds based on raw sparse 3D point clouds. Then, the
predicted dense point clouds are fused with the segmentation outputs from
ERFnet using a novel multi-layer convolutional neural network to refine the
predicted 3D point clouds. Finally, the enriched point clouds are employed to
perform LiDAR SLAM based on the state-of-the-art normal distribution transform
(NDT). We tested our approach on the re-edited KITTI datasets: (1)the sparse 3D
point clouds are significantly enriched with a mean square error of 1.1m MSE.
(2)the map generated from the LiDAR SLAM is denser which includes more details
without significant accuracy loss. | [
"cs.CV",
"cs.LG",
"cs.RO"
]
|
Much of the recent efforts on salient object detection (SOD) have been
devoted to producing accurate saliency maps without being aware of their
instance labels. To this end, we propose a new pipeline for end-to-end salient
instance segmentation (SIS) that predicts a class-agnostic mask for each
detected salient instance. To better use the rich feature hierarchies in deep
networks and enhance the side predictions, we propose the regularized dense
connections, which attentively promote informative features and suppress
non-informative ones from all feature pyramids. A novel multi-level RoIAlign
based decoder is introduced to adaptively aggregate multi-level features for
better mask predictions. Such strategies can be well-encapsulated into the Mask
R-CNN pipeline. Extensive experiments on popular benchmarks demonstrate that
our design significantly outperforms existing \sArt competitors by 6.3\%
(58.6\% vs. 52.3\%) in terms of the AP metric.The code is available at
https://github.com/yuhuan-wu/RDPNet. | [
"cs.CV",
"cs.LG",
"eess.IV"
]
|
3D object detection from a single image is an important task in Autonomous
Driving (AD), where various approaches have been proposed. However, the task is
intrinsically ambiguous and challenging as single image depth estimation is
already an ill-posed problem. In this paper, we propose an instance-aware
approach to aggregate useful information for improving the accuracy of 3D
object detection with the following contributions. First, an instance-aware
feature aggregation (IAFA) module is proposed to collect local and global
features for 3D bounding boxes regression. Second, we empirically find that the
spatial attention module can be well learned by taking coarse-level instance
annotations as a supervision signal. The proposed module has significantly
boosted the performance of the baseline method on both 3D detection and 2D
bird-eye's view of vehicle detection among all three categories. Third, our
proposed method outperforms all single image-based approaches (even these
methods trained with depth as auxiliary inputs) and achieves state-of-the-art
3D detection performance on the KITTI benchmark. | [
"cs.CV"
]
|
Imagining a colored realistic image from an arbitrarily drawn sketch is one
of the human capabilities that we eager machines to mimic. Unlike previous
methods that either requires the sketch-image pairs or utilize low-quantity
detected edges as sketches, we study the exemplar-based sketch-to-image (s2i)
synthesis task in a self-supervised learning manner, eliminating the necessity
of the paired sketch data. To this end, we first propose an unsupervised method
to efficiently synthesize line-sketches for general RGB-only datasets. With the
synthetic paired-data, we then present a self-supervised Auto-Encoder (AE) to
decouple the content/style features from sketches and RGB-images, and
synthesize images that are both content-faithful to the sketches and
style-consistent to the RGB-images. While prior works employ either the
cycle-consistence loss or dedicated attentional modules to enforce the
content/style fidelity, we show AE's superior performance with pure
self-supervisions. To further improve the synthesis quality in high resolution,
we also leverage an adversarial network to refine the details of synthetic
images. Extensive experiments on 1024*1024 resolution demonstrate a new
state-of-art-art performance of the proposed model on CelebA-HQ and Wiki-Art
datasets. Moreover, with the proposed sketch generator, the model shows a
promising performance on style mixing and style transfer, which require
synthesized images to be both style-consistent and semantically meaningful. Our
code is available on
https://github.com/odegeasslbc/Self-Supervised-Sketch-to-Image-Synthesis-PyTorch,
and please visit https://create.playform.io/my-projects?mode=sketch for an
online demo of our model. | [
"cs.CV",
"cs.GR",
"cs.MM"
]
|
In this paper, we focus on graph representation learning of heterogeneous
information network (HIN), in which various types of vertices are connected by
various types of relations. Most of the existing methods conducted on HIN
revise homogeneous graph embedding models via meta-paths to learn
low-dimensional vector space of HIN. In this paper, we propose a novel
Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly
encode structural information of HIN without meta-path and achieve more
informative representations. With this method, domain experts will not be
needed to design meta-path schemes and the heterogeneous information can be
processed automatically by our proposed model. Specifically, we implicitly
represent heterogeneous information using the following two methods: 1) we
model the transformation between heterogeneous vertices through a projection in
low-dimensional entity spaces; 2) afterwards, we apply the graph neural network
to aggregate multi-relational information of projected neighborhood by means of
attention mechanism. We also present three extensions of HetSANN, i.e.,
voices-sharing product attention for the pairwise relationships in HIN,
cycle-consistency loss to retain the transformation between heterogeneous
entity spaces, and multi-task learning with full use of information. The
experiments conducted on three public datasets demonstrate that our proposed
models achieve significant and consistent improvements compared to
state-of-the-art solutions. | [
"cs.LG",
"cs.SI",
"stat.ML"
]
|
The difficulty of obtaining paired data remains a major bottleneck for
learning image restoration and enhancement models for real-world applications.
Current strategies aim to synthesize realistic training data by modeling noise
and degradations that appear in real-world settings. We propose DeFlow, a
method for learning stochastic image degradations from unpaired data. Our
approach is based on a novel unpaired learning formulation for conditional
normalizing flows. We model the degradation process in the latent space of a
shared flow encoder-decoder network. This allows us to learn the conditional
distribution of a noisy image given the clean input by solely minimizing the
negative log-likelihood of the marginal distributions. We validate our DeFlow
formulation on the task of joint image restoration and super-resolution. The
models trained with the synthetic data generated by DeFlow outperform previous
learnable approaches on three recent datasets. Code and trained models are
available at: https://github.com/volflow/DeFlow | [
"cs.CV",
"cs.LG",
"eess.IV"
]
|
Machine learning on graph structured data has attracted much research
interest due to its ubiquity in real world data. However, how to efficiently
represent graph data in a general way is still an open problem. Traditional
methods use handcraft graph features in a tabular form but suffer from the
defects of domain expertise requirement and information loss. Graph
representation learning overcomes these defects by automatically learning the
continuous representations from graph structures, but they require abundant
training labels, which are often hard to fulfill for graph-level prediction
problems. In this work, we demonstrate that, if available, the domain expertise
used for designing handcraft graph features can improve the graph-level
representation learning when training labels are scarce. Specifically, we
proposed a multi-task knowledge distillation method. By incorporating
network-theory-based graph metrics as auxiliary tasks, we show on both
synthetic and real datasets that the proposed multi-task learning method can
improve the prediction performance of the original learning task, especially
when the training data size is small. | [
"cs.LG",
"cs.AI",
"stat.ML"
]
|
Popular Maximum Entropy Inverse Reinforcement Learning approaches require the
computation of expected state visitation frequencies for the optimal policy
under an estimate of the reward function. This usually requires intermediate
value estimation in the inner loop of the algorithm, slowing down convergence
considerably. In this work, we introduce a novel class of algorithms that only
needs to solve the MDP underlying the demonstrated behavior once to recover the
expert policy. This is possible through a formulation that exploits a
probabilistic behavior assumption for the demonstrations within the structure
of Q-learning. We propose Inverse Action-value Iteration which is able to fully
recover an underlying reward of an external agent in closed-form analytically.
We further provide an accompanying class of sampling-based variants which do
not depend on a model of the environment. We show how to extend this class of
algorithms to continuous state-spaces via function approximation and how to
estimate a corresponding action-value function, leading to a policy as close as
possible to the policy of the external agent, while optionally satisfying a
list of predefined hard constraints. We evaluate the resulting algorithms
called Inverse Action-value Iteration, Inverse Q-learning and Deep Inverse
Q-learning on the Objectworld benchmark, showing a speedup of up to several
orders of magnitude compared to (Deep) Max-Entropy algorithms. We further apply
Deep Constrained Inverse Q-learning on the task of learning autonomous
lane-changes in the open-source simulator SUMO achieving competent driving
after training on data corresponding to 30 minutes of demonstrations. | [
"cs.LG",
"cs.RO",
"stat.ML"
]
|
Sliding-window object detectors that generate bounding-box object predictions
over a dense, regular grid have advanced rapidly and proven popular. In
contrast, modern instance segmentation approaches are dominated by methods that
first detect object bounding boxes, and then crop and segment these regions, as
popularized by Mask R-CNN. In this work, we investigate the paradigm of dense
sliding-window instance segmentation, which is surprisingly under-explored. Our
core observation is that this task is fundamentally different than other dense
prediction tasks such as semantic segmentation or bounding-box object
detection, as the output at every spatial location is itself a geometric
structure with its own spatial dimensions. To formalize this, we treat dense
instance segmentation as a prediction task over 4D tensors and present a
general framework called TensorMask that explicitly captures this geometry and
enables novel operators on 4D tensors. We demonstrate that the tensor view
leads to large gains over baselines that ignore this structure, and leads to
results comparable to Mask R-CNN. These promising results suggest that
TensorMask can serve as a foundation for novel advances in dense mask
prediction and a more complete understanding of the task. Code will be made
available. | [
"cs.CV"
]
|
There is active research targeting local image manipulations that can fool
deep neural networks (DNNs) into producing incorrect results. This paper
examines a type of global image manipulation that can produce similar adverse
effects. Specifically, we explore how strong color casts caused by incorrectly
applied computational color constancy - referred to as white balance (WB) in
photography - negatively impact the performance of DNNs targeting image
segmentation and classification. In addition, we discuss how existing image
augmentation methods used to improve the robustness of DNNs are not well suited
for modeling WB errors. To address this problem, a novel augmentation method is
proposed that can emulate accurate color constancy degradation. We also explore
pre-processing training and testing images with a recent WB correction
algorithm to reduce the effects of incorrectly white-balanced images. We
examine both augmentation and pre-processing strategies on different datasets
and demonstrate notable improvements on the CIFAR-10, CIFAR-100, and ADE20K
datasets. | [
"cs.CV"
]
|
Despite recent progress on semantic segmentation, there still exist huge
challenges in medical ultra-resolution image segmentation. The methods based on
multi-branch structure can make a good balance between computational burdens
and segmentation accuracy. However, the fusion structure in these methods
require to be designed elaborately to achieve desirable result, which leads to
model redundancy. In this paper, we propose Meta Segmentation Network (MSN) to
solve this challenging problem. With the help of meta-learning, the fusion
module of MSN is quite simple but effective. MSN can fast generate the weights
of fusion layers through a simple meta-learner, requiring only a few training
samples and epochs to converge. In addition, to avoid learning all branches
from scratch, we further introduce a particular weight sharing mechanism to
realize a fast knowledge adaptation and share the weights among multiple
branches, resulting in the performance improvement and significant parameters
reduction. The experimental results on two challenging ultra-resolution medical
datasets BACH and ISIC show that MSN achieves the best performance compared
with the state-of-the-art methods. | [
"cs.CV"
]
|
In this study, we introduce \textbf{AttendSeg}, a low-precision, highly
compact deep neural network tailored for on-device semantic segmentation.
AttendSeg possesses a self-attention network architecture comprising of
light-weight attention condensers for improved spatial-channel selective
attention at a very low complexity. The unique macro-architecture and
micro-architecture design properties of AttendSeg strike a strong balance
between representational power and efficiency, achieved via a machine-driven
design exploration strategy tailored specifically for the task at hand.
Experimental results demonstrated that the proposed AttendSeg can achieve
segmentation accuracy comparable to much larger deep neural networks with
greater complexity while possessing a significantly lower architecture and
computational complexity (requiring as much as >27x fewer MACs, >72x fewer
parameters, and >288x lower weight memory requirements), making it well-suited
for TinyML applications on the edge. | [
"cs.CV",
"cs.LG"
]
|
Can machine learning help us make better decisions about a changing planet?
In this paper, we illustrate and discuss the potential of a promising corner of
machine learning known as _reinforcement learning_ (RL) to help tackle the most
challenging conservation decision problems. RL is uniquely well suited to
conservation and global change challenges for three reasons: (1) RL explicitly
focuses on designing an agent who _interacts_ with an environment which is
dynamic and uncertain, (2) RL approaches do not require massive amounts of
data, (3) RL approaches would utilize rather than replace existing models,
simulations, and the knowledge they contain. We provide a conceptual and
technical introduction to RL and its relevance to ecological and conservation
challenges, including examples of a problem in setting fisheries quotas and in
managing ecological tipping points. Four appendices with annotated code provide
a tangible introduction to researchers looking to adopt, evaluate, or extend
these approaches. | [
"cs.LG",
"q-bio.QM"
]
|
Deep learning techniques are increasingly being adopted for classification
tasks over the past decade, yet explaining how deep learning architectures can
achieve state-of-the-art performance is still an elusive goal. While all the
training information is embedded deeply in a trained model, we still do not
understand much about its performance by only analyzing the model. This paper
examines the neuron activation patterns of deep learning-based classification
models and explores whether the models' performances can be explained through
neurons' activation behavior. We propose two approaches: one that models
neurons' activation behavior as a graph and examines whether the neurons form
meaningful communities, and the other examines the predictability of neurons'
behavior using entropy. Our comprehensive experimental study reveals that both
the community quality (modularity) and entropy are closely related to the deep
learning models' performances, thus paves a novel way of explaining deep
learning models directly from the neurons' activation pattern. | [
"cs.LG",
"cs.IT",
"cs.NE",
"math.IT"
]
|
A central aspect of online decision tree solutions is evaluating the incoming
data and enabling model growth. For such, trees much deal with different kinds
of input features and partition them to learn from the data. Numerical features
are no exception, and they pose additional challenges compared to other kinds
of features, as there is no trivial strategy to choose the best point to make a
split decision. The problem is even more challenging in regression tasks
because both the features and the target are continuous. Typical online
solutions evaluate and store all the points monitored between split attempts,
which goes against the constraints posed in real-time applications. In this
paper, we introduce the Quantization Observer (QO), a simple yet effective
hashing-based algorithm to monitor and evaluate split point candidates in
numerical features for online tree regressors. QO can be easily integrated into
incremental decision trees, such as Hoeffding Trees, and it has a monitoring
cost of $O(1)$ per instance and sub-linear cost to evaluate split candidates.
Previous solutions had a $O(\log n)$ cost per insertion (in the best case) and
a linear cost to evaluate split points. Our extensive experimental setup
highlights QO's effectiveness in providing accurate split point suggestions
while spending much less memory and processing time than its competitors. | [
"cs.LG",
"I.2.6; I.5.4"
]
|
Regular inspection of rail valves and engines is an important task to ensure
the safety and efficiency of railway networks around the globe. Over the past
decade, computer vision and pattern recognition based techniques have gained
traction for such inspection and defect detection tasks. An automated
end-to-end trained system can potentially provide a low-cost, high throughput,
and cheap alternative to manual visual inspection of these components. However,
such systems require a huge amount of defective images for networks to
understand complex defects. In this paper, a multi-phase deep learning based
technique is proposed to perform accurate fault detection of rail-valves. Our
approach uses a two-step method to perform high precision image segmentation of
rail-valves resulting in pixel-wise accurate segmentation. Thereafter, a
computer vision technique is used to identify faulty valves. We demonstrate
that the proposed approach results in improved detection performance when
compared to current state-of-theart techniques used in fault detection. | [
"cs.CV"
]
|
Temporal grounding aims to temporally localize a video moment in the video
whose semantics are related to a given natural language query. Existing methods
typically apply a detection or regression pipeline on the fused representation
with a focus on designing complicated heads and fusion strategies. Instead,
from a perspective on temporal grounding as a metric-learning problem, we
present a Dual Matching Network (DMN), to directly model the relations between
language queries and video moments in a joint embedding space. This new
metric-learning framework enables fully exploiting negative samples from two
new aspects: constructing negative cross-modal pairs from a dual matching
scheme and mining negative pairs across different videos. These new negative
samples could enhance the joint representation learning of two modalities via
cross-modal pair discrimination to maximize their mutual information.
Experiments show that DMN achieves highly competitive performance compared with
state-of-the-art methods on four video grounding benchmarks. Based on DMN, we
present a winner solution for STVG challenge of the 3rd PIC workshop. This
suggests that metric-learning is still a promising method for temporal
grounding via capturing the essential cross-modal correlation in a joint
embedding space. | [
"cs.CV",
"cs.MM"
]
|
In supervised learning, we fit a single statistical model to a given data
set, assuming that the data is associated with a singular task, which yields
well-tuned models for specific use, but does not adapt well to new contexts. By
contrast, in meta-learning, the data is associated with numerous tasks, and we
seek a model that may perform well on all tasks simultaneously, in pursuit of
greater generalization. One challenge in meta-learning is how to exploit
relationships between tasks and classes, which is overlooked by commonly used
random or cyclic passes through data. In this work, we propose actively
selecting samples on which to train by discerning covariates inside and between
meta-training sets. Specifically, we cast the problem of selecting a sample
from a number of meta-training sets as either a multi-armed bandit or a Markov
Decision Process (MDP), depending on how one encapsulates correlation across
tasks. We develop scheduling schemes based on Upper Confidence Bound (UCB),
Gittins Index and tabular Markov Decision Problems (MDPs) solved with linear
programming, where the reward is the scaled statistical accuracy to ensure it
is a time-invariant function of state and action. Across a variety of
experimental contexts, we observe significant reductions in sample complexity
of active selection scheme relative to cyclic or i.i.d. sampling, demonstrating
the merit of exploiting covariates in practice. | [
"cs.LG",
"math.OC",
"stat.ML"
]
|
Most approaches for instance-aware semantic labeling traditionally focus on
accuracy. Other aspects like runtime and memory footprint are arguably as
important for real-time applications such as autonomous driving. Motivated by
this observation and inspired by recent works that tackle multiple tasks with a
single integrated architecture, in this paper we present a real-time efficient
implementation based on ENet that solves three autonomous driving related tasks
at once: semantic scene segmentation, instance segmentation and monocular depth
estimation. Our approach builds upon a branched ENet architecture with a shared
encoder but different decoder branches for each of the three tasks. The
presented method can run at 21 fps at a resolution of 1024x512 on the
Cityscapes dataset without sacrificing accuracy compared to running each task
separately. | [
"cs.CV",
"cs.RO"
]
|
Existing optical flow methods are erroneous in challenging scenes, such as
fog, rain, and night because the basic optical flow assumptions such as
brightness and gradient constancy are broken. To address this problem, we
present an unsupervised learning approach that fuses gyroscope into optical
flow learning. Specifically, we first convert gyroscope readings into motion
fields named gyro field. Second, we design a self-guided fusion module to fuse
the background motion extracted from the gyro field with the optical flow and
guide the network to focus on motion details. To the best of our knowledge,
this is the first deep learning-based framework that fuses gyroscope data and
image content for optical flow learning. To validate our method, we propose a
new dataset that covers regular and challenging scenes. Experiments show that
our method outperforms the state-of-art methods in both regular and challenging
scenes. Code and dataset are available at
https://github.com/megvii-research/GyroFlow. | [
"cs.CV"
]
|
Model-based reinforcement learning algorithms make decisions by building and
utilizing a model of the environment. However, none of the existing algorithms
attempts to infer the dynamics of any state-action pair from known state-action
pairs before meeting it for sufficient times. We propose a new model-based
method called Greedy Inference Model (GIM) that infers the unknown dynamics
from known dynamics based on the internal spectral properties of the
environment. In other words, GIM can "learn by analogy". We further introduce a
new exploration strategy which ensures that the agent rapidly and evenly visits
unknown state-action pairs. GIM is much more computationally efficient than
state-of-the-art model-based algorithms, as the number of dynamic programming
operations is independent of the environment size. Lower sample complexity
could also be achieved under mild conditions compared against methods without
inferring. Experimental results demonstrate the effectiveness and efficiency of
GIM in a variety of real-world tasks. | [
"cs.LG",
"stat.ML"
]
|
Our work aims to obtain 3D reconstruction of hands and manipulated objects
from monocular videos. Reconstructing hand-object manipulations holds a great
potential for robotics and learning from human demonstrations. The supervised
learning approach to this problem, however, requires 3D supervision and remains
limited to constrained laboratory settings and simulators for which 3D ground
truth is available. In this paper we first propose a learning-free fitting
approach for hand-object reconstruction which can seamlessly handle two-hand
object interactions. Our method relies on cues obtained with common methods for
object detection, hand pose estimation and instance segmentation. We
quantitatively evaluate our approach and show that it can be applied to
datasets with varying levels of difficulty for which training data is
unavailable. | [
"cs.CV"
]
|
In a variety of problems originating in supervised, unsupervised, and
reinforcement learning, the loss function is defined by an expectation over a
collection of random variables, which might be part of a probabilistic model or
the external world. Estimating the gradient of this loss function, using
samples, lies at the core of gradient-based learning algorithms for these
problems. We introduce the formalism of stochastic computation
graphs---directed acyclic graphs that include both deterministic functions and
conditional probability distributions---and describe how to easily and
automatically derive an unbiased estimator of the loss function's gradient. The
resulting algorithm for computing the gradient estimator is a simple
modification of the standard backpropagation algorithm. The generic scheme we
propose unifies estimators derived in variety of prior work, along with
variance-reduction techniques therein. It could assist researchers in
developing intricate models involving a combination of stochastic and
deterministic operations, enabling, for example, attention, memory, and control
actions. | [
"cs.LG"
]
|
Generative adversarial networks (GANs) studies have grown exponentially in
the past few years. Their impact has been seen mainly in the computer vision
field with realistic image and video manipulation, especially generation,
making significant advancements. While these computer vision advances have
garnered much attention, GAN applications have diversified across disciplines
such as time series and sequence generation. As a relatively new niche for
GANs, fieldwork is ongoing to develop high quality, diverse and private time
series data. In this paper, we review GAN variants designed for time series
related applications. We propose a taxonomy of discrete-variant GANs and
continuous-variant GANs, in which GANs deal with discrete time series and
continuous time series data. Here we showcase the latest and most popular
literature in this field; their architectures, results, and applications. We
also provide a list of the most popular evaluation metrics and their
suitability across applications. Also presented is a discussion of privacy
measures for these GANs and further protections and directions for dealing with
sensitive data. We aim to frame clearly and concisely the latest and
state-of-the-art research in this area and their applications to real-world
technologies. | [
"cs.LG",
"cs.AI"
]
|
We present BiLingUNet, a state-of-the-art model for image segmentation using
referring expressions. BiLingUNet uses language to customize visual filters and
outperforms approaches that concatenate a linguistic representation to the
visual input. We find that using language to modulate both bottom-up and
top-down visual processing works better than just making the top-down
processing language-conditional. We argue that common 1x1 language-conditional
filters cannot represent relational concepts and experimentally demonstrate
that wider filters work better. Our model achieves state-of-the-art performance
on four referring expression datasets. | [
"cs.CV",
"cs.CL",
"cs.LG"
]
|
Robust vision restoration for an underwater image remains a challenging
problem. For the lack of aligned underwater-terrestrial image pairs, the
unsupervised method is more suited to this task. However, the pure data-driven
unsupervised method usually has difficulty in achieving realistic color
correction for lack of optical constraint. In this paper, we propose a data-
and physics-driven unsupervised architecture that learns underwater vision
restoration from unpaired underwater-terrestrial images. For sufficient domain
transformation and detail preservation, the underwater degeneration needs to be
explicitly constructed based on the optically unambiguous physics law. Thus, we
employ the Jaffe-McGlamery degradation theory to design the generation models,
and use neural networks to describe the process of underwater degradation.
Furthermore, to overcome the problem of invalid gradient when optimizing the
hybrid physical-neural model, we fully investigate the intrinsic correlation
between the scene depth and the degradation factors for the backscattering
estimation, to improve the restoration performance through physical
constraints. Our experimental results show that the proposed method is able to
perform high-quality restoration for unconstrained underwater images without
any supervision. On multiple benchmarks, we outperform several state-of-the-art
supervised and unsupervised approaches. We also demonstrate that our methods
yield encouraging results on real-world applications. | [
"cs.CV",
"eess.IV"
]
|
A vision-based keystroke inference attack is a side-channel attack in which
an attacker uses an optical device to record users on their mobile devices and
infer their keystrokes. The threat space for these attacks has been studied in
the past, but we argue that the defining characteristics for this threat space,
namely the strength of the attacker, are outdated. Previous works do not study
adversaries with vision systems that have been trained with deep neural
networks because these models require large amounts of training data and
curating such a dataset is expensive. To address this, we create a large-scale
synthetic dataset to simulate the attack scenario for a keystroke inference
attack. We show that first pre-training on synthetic data, followed by adopting
transfer learning techniques on real-life data, increases the performance of
our deep learning models. This indicates that these models are able to learn
rich, meaningful representations from our synthetic data and that training on
the synthetic data can help overcome the issue of having small, real-life
datasets for vision-based key stroke inference attacks. For this work, we focus
on single keypress classification where the input is a frame of a keypress and
the output is a predicted key. We are able to get an accuracy of 95.6% after
pre-training a CNN on our synthetic data and training on a small set of
real-life data in an adversarial domain adaptation framework. Source Code for
Simulator:
https://github.com/jlim13/keystroke-inference-attack-synthetic-dataset-generator- | [
"cs.CV",
"cs.CR"
]
|
Color image segmentation is a very emerging research topic in the area of
color image analysis and pattern recognition. Many state-of-the-art algorithms
have been developed for this purpose. But, often the segmentation results of
these algorithms seem to be suffering from miss-classifications and
over-segmentation. The reasons behind these are the degradation of image
quality during the acquisition, transmission and color space conversion. So,
here arises the need of an efficient image enhancement technique which can
remove the redundant pixels or noises from the color image before proceeding
for final segmentation. In this paper, an effort has been made to study and
analyze different image enhancement techniques and thereby finding out the
better one for color image segmentation. Also, this comparative study is done
on two well-known color spaces HSV and LAB separately to find out which color
space supports segmentation task more efficiently with respect to those
enhancement techniques. | [
"cs.CV"
]
|
With the proliferation of various gaming technology, services, game styles,
and platforms, multi-dimensional aesthetic assessment of the gaming contents is
becoming more and more important for the gaming industry. Depending on the
diverse needs of diversified game players, game designers, graphical
developers, etc. in particular conditions, multi-modal aesthetic assessment is
required to consider different aesthetic dimensions/perspectives. Since there
are different underlying relationships between different aesthetic dimensions,
e.g., between the `Colorfulness' and `Color Harmony', it could be advantageous
to leverage effective information attached in multiple relevant dimensions. To
this end, we solve this problem via multi-task learning. Our inclination is to
seek and learn the correlations between different aesthetic relevant dimensions
to further boost the generalization performance in predicting all the aesthetic
dimensions. Therefore, the `bottleneck' of obtaining good predictions with
limited labeled data for one individual dimension could be unplugged by
harnessing complementary sources of other dimensions, i.e., augment the
training data indirectly by sharing training information across dimensions.
According to experimental results, the proposed model outperforms
state-of-the-art aesthetic metrics significantly in predicting four gaming
aesthetic dimensions. | [
"cs.CV",
"cs.AI",
"68U10",
"J.0"
]
|
We propose CRaWl (CNNs for Random Walks), a novel neural network architecture
for graph learning. It is based on processing sequences of small subgraphs
induced by random walks with standard 1D CNNs. Thus, CRaWl is fundamentally
different from typical message passing graph neural network architectures. It
is inspired by techniques counting small subgraphs, such as the graphlet kernel
and motif counting, and combines them with random walk based techniques in a
highly efficient and scalable neural architecture. We demonstrate empirically
that CRaWl matches or outperforms state-of-the-art GNN architectures across a
multitude of benchmark datasets for classification and regression on graphs. | [
"cs.LG",
"cs.SI"
]
|
Existing image fusion methods pay few research attention to image fusion
efficiency and network architecture. However, the efficiency and accuracy of
image fusion has an important impact in practical applications. To solve this
problem, we propose an \textit{efficient autonomous evolution image fusion
method, dubed by AE-Netv2}. Different from other image fusion methods based on
deep learning, AE-Netv2 is inspired by human brain cognitive mechanism.
Firstly, we discuss the influence of different network architecture on image
fusion quality and fusion efficiency, which provides a reference for the design
of image fusion architecture. Secondly, we explore the influence of pooling
layer on image fusion task and propose an image fusion method with pooling
layer. Finally, we explore the commonness and characteristics of different
image fusion tasks, which provides a research basis for further research on the
continuous learning characteristics of human brain in the field of image
fusion. Comprehensive experiments demonstrate the superiority of AE-Netv2
compared with state-of-the-art methods in different fusion tasks at a real time
speed of 100+ FPS on GTX 2070. Among all tested methods based on deep learning,
AE-Netv2 has the faster speed, the smaller model size and the better
robustness. | [
"cs.CV"
]
|
Scene graph generation (SGG) aims to detect objects in an image along with
their pairwise relationships. There are three key properties of scene graph
that have been underexplored in recent works: namely, the edge direction
information, the difference in priority between nodes, and the long-tailed
distribution of relationships. Accordingly, in this paper, we propose a Graph
Property Sensing Network (GPS-Net) that fully explores these three properties
for SGG. First, we propose a novel message passing module that augments the
node feature with node-specific contextual information and encodes the edge
direction information via a tri-linear model. Second, we introduce a node
priority sensitive loss to reflect the difference in priority between nodes
during training. This is achieved by designing a mapping function that adjusts
the focusing parameter in the focal loss. Third, since the frequency of
relationships is affected by the long-tailed distribution problem, we mitigate
this issue by first softening the distribution and then enabling it to be
adjusted for each subject-object pair according to their visual appearance.
Systematic experiments demonstrate the effectiveness of the proposed
techniques. Moreover, GPS-Net achieves state-of-the-art performance on three
popular databases: VG, OI, and VRD by significant gains under various settings
and metrics. The code and models are available at
\url{https://github.com/taksau/GPS-Net}. | [
"cs.CV"
]
|
Deep Learning using the eponymous deep neural networks (DNNs) has become an
attractive approach towards various data-based problems of theoretical physics
in the past decade. There has been a clear trend to deeper architectures
containing increasingly more powerful and involved layers. Contrarily, Taylor
coefficients of DNNs still appear mainly in the light of interpretability
studies, where they are computed at most to first order. However, especially in
theoretical physics numerous problems benefit from accessing higher orders, as
well. This gap motivates a general formulation of neural network (NN) Taylor
expansions. Restricting our analysis to multilayer perceptrons (MLPs) and
introducing quantities we refer to as propagators and vertices, both depending
on the MLP's weights and biases, we establish a graph-theoretical approach.
Similarly to Feynman rules in quantum field theories, we can systematically
assign diagrams containing propagators and vertices to the corresponding
partial derivative. Examining this approach for S-wave scattering lengths of
shallow potentials, we observe NNs to adapt their derivatives mainly to the
leading order of the target function's Taylor expansion. To circumvent this
problem, we propose an iterative NN perturbation theory. During each iteration
we eliminate the leading order, such that the next-to-leading order can be
faithfully learned during the subsequent iteration. After performing two
iterations, we find that the first- and second-order Born terms are correctly
adapted during the respective iterations. Finally, we combine both results to
find a proxy that acts as a machine-learned second-order Born approximation. | [
"cs.LG",
"nucl-th",
"physics.comp-ph",
"stat.ML"
]
|
Self-supervised depth estimation has made a great success in learning depth
from unlabeled image sequences. While the mappings between image and pixel-wise
depth are well-studied in current methods, the correlation between image, depth
and scene semantics, however, is less considered. This hinders the network to
better understand the real geometry of the scene, since the contextual clues,
contribute not only the latent representations of scene depth, but also the
straight constraints for depth map. In this paper, we leverage the two benefits
by proposing the implicit and explicit semantic guidance for accurate
self-supervised depth estimation. We propose a Semantic-aware Spatial Feature
Alignment (SSFA) scheme to effectively align implicit semantic features with
depth features for scene-aware depth estimation. We also propose a
semantic-guided ranking loss to explicitly constrain the estimated depth maps
to be consistent with real scene contextual properties. Both semantic label
noise and prediction uncertainty is considered to yield reliable depth
supervisions. Extensive experimental results show that our method produces high
quality depth maps which are consistently superior either on complex scenes or
diverse semantic categories, and outperforms the state-of-the-art methods by a
significant margin. | [
"cs.CV"
]
|
Nonnegative matrix factorization (NMF) has been actively investigated and
used in a wide range of problems in the past decade. A significant amount of
attention has been given to develop NMF algorithms that are suitable to model
time series with strong temporal dependencies. In this paper, we propose a
novel state-space approach to perform dynamic NMF (D-NMF). In the proposed
probabilistic framework, the NMF coefficients act as the state variables and
their dynamics are modeled using a multi-lag nonnegative vector autoregressive
(N-VAR) model within the process equation. We use expectation maximization and
propose a maximum-likelihood estimation framework to estimate the basis matrix
and the N-VAR model parameters. Interestingly, the N-VAR model parameters are
obtained by simply applying NMF. Moreover, we derive a maximum a posteriori
estimate of the state variables (i.e., the NMF coefficients) that is based on a
prediction step and an update step, similarly to the Kalman filter. We
illustrate the benefits of the proposed approach using different numerical
simulations where D-NMF significantly outperforms its static counterpart.
Experimental results for three different applications show that the proposed
approach outperforms two state-of-the-art NMF approaches that exploit temporal
dependencies, namely a nonnegative hidden Markov model and a frame stacking
approach, while it requires less memory and computational power. | [
"cs.LG",
"stat.ML"
]
|
Transfer learning has emerged as a powerful methodology for adapting
pre-trained deep neural networks on image recognition tasks to new domains.
This process consists of taking a neural network pre-trained on a large
feature-rich source dataset, freezing the early layers that encode essential
generic image properties, and then fine-tuning the last few layers in order to
capture specific information related to the target situation. This approach is
particularly useful when only limited or weakly labeled data are available for
the new task. In this work, we demonstrate that adversarially-trained models
transfer better than non-adversarially-trained models, especially if only
limited data are available for the new domain task. Further, we observe that
adversarial training biases the learnt representations to retaining shapes, as
opposed to textures, which impacts the transferability of the source models.
Finally, through the lens of influence functions, we discover that transferred
adversarially-trained models contain more human-identifiable semantic
information, which explains -- at least partly -- why adversarially-trained
models transfer better. | [
"cs.LG",
"stat.ML"
]
|
Medical image registration is an active research topic and forms a basis for
many medical image analysis tasks. Although image registration is a rather
general concept specialized methods are usually required to target a specific
registration problem. The development and implementation of such methods has
been tough so far as the gradient of the objective has to be computed. Also,
its evaluation has to be performed preferably on a GPU for larger images and
for more complex transformation models and regularization terms. This hinders
researchers from rapid prototyping and poses hurdles to reproduce research
results. There is a clear need for an environment which hides this complexity
to put the modeling and the experimental exploration of registration methods
into the foreground. With the "Autograd Image Registration Laboratory"
(AIRLab), we introduce an open laboratory for image registration tasks, where
the analytic gradients of the objective function are computed automatically and
the device where the computations are performed, on a CPU or a GPU, is
transparent. It is meant as a laboratory for researchers and developers
enabling them to rapidly try out new ideas for registering images and to
reproduce registration results which have already been published. AIRLab is
implemented in Python using PyTorch as tensor and optimization library and
SimpleITK for basic image IO. Therefore, it profits from recent advances made
by the machine learning community concerning optimization and deep neural
network models. The presented draft of this paper outlines AIRLab with first
code snippets and performance analyses. A more exhaustive introduction will
follow as a final version soon. | [
"cs.CV"
]
|
A crucial factor to trust Machine Learning (ML) algorithm decisions is a good
representation of its application field by the training dataset. This is
particularly true when parts of the training data have been artificially
generated to overcome common training problems such as lack of data or
imbalanced dataset. Over the last few years, Generative Adversarial Networks
(GANs) have shown remarkable results in generating realistic data. However,
this ML approach lacks an objective function to evaluate the quality of the
generated data. Numerous GAN applications focus on generating image data mostly
because they can be easily evaluated by a human eye. Less efforts have been
made to generate time series data. Assessing their quality is more complicated,
particularly for technical data. In this paper, we propose a human-centered
approach supporting a ML or domain expert to accomplish this task using Visual
Analytics (VA) techniques. The presented approach consists of two views, namely
a GAN Iteration View showing similarity metrics between real and generated data
over the iterations of the generation process and a Detailed Comparative View
equipped with different time series visualizations such as TimeHistograms, to
compare the generated data at different iteration steps. Starting from the GAN
Iteration View, the user can choose suitable iteration steps for detailed
inspection. We evaluate our approach with a usage scenario that enabled an
efficient comparison of two different GAN models. | [
"cs.LG",
"cs.HC",
"eess.IV"
]
|
Existing approaches to solving combinatorial optimization problems on graphs
suffer from the need to engineer each problem algorithmically, with practical
problems recurring in many instances. The practical side of theoretical
computer science, such as computational complexity, then needs to be addressed.
Relevant developments in machine learning research on graphs are surveyed for
this purpose. We organize and compare the structures involved with learning to
solve combinatorial optimization problems, with a special eye on the
telecommunications domain and its continuous development of live and research
networks. | [
"cs.LG",
"cs.AI",
"stat.ML",
"68-01, 90-01",
"A.1"
]
|
Today's success of state of the art methods for semantic segmentation is
driven by large datasets. Data is considered an important asset that needs to
be protected, as the collection and annotation of such datasets comes at
significant efforts and associated costs. In addition, visual data might
contain private or sensitive information, that makes it equally unsuited for
public release. Unfortunately, recent work on membership inference in the
broader area of adversarial machine learning and inference attacks on machine
learning models has shown that even black box classifiers leak information on
the dataset that they were trained on. We show that such membership inference
attacks can be successfully carried out on complex, state of the art models for
semantic segmentation. In order to mitigate the associated risks, we also study
a series of defenses against such membership inference attacks and find
effective counter measures against the existing risks with little effect on the
utility of the segmentation method. Finally, we extensively evaluate our
attacks and defenses on a range of relevant real-world datasets: Cityscapes,
BDD100K, and Mapillary Vistas. | [
"cs.CV"
]
|
In this paper, we study reinforcement learning (RL) algorithms to solve
real-world decision problems with the objective of maximizing the long-term
reward as well as satisfying cumulative constraints. We propose a novel
first-order policy optimization method, Interior-point Policy Optimization
(IPO), which augments the objective with logarithmic barrier functions,
inspired by the interior-point method. Our proposed method is easy to implement
with performance guarantees and can handle general types of cumulative
multiconstraint settings. We conduct extensive evaluations to compare our
approach with state-of-the-art baselines. Our algorithm outperforms the
baseline algorithms, in terms of reward maximization and constraint
satisfaction. | [
"cs.LG",
"math.OC",
"stat.ML"
]
|
This paper introduces a Bayesian image segmentation algorithm based on finite
mixtures. An EM algorithm is developed to estimate parameters of the Gaussian
mixtures. The finite mixture is a flexible and powerful probabilistic modeling
tool. It can be used to provide a model-based clustering in the field of
pattern recognition. However, the application of finite mixtures to image
segmentation presents some difficulties; especially it's sensible to noise. In
this paper we propose a variant of this method which aims to resolve this
problem. Our approach proceeds by the characterization of pixels by two
features: the first one describes the intrinsic properties of the pixel and the
second characterizes the neighborhood of pixel. Then the classification is made
on the base on adaptive distance which privileges the one or the other features
according to the spatial position of the pixel in the image. The obtained
results have shown a significant improvement of our approach compared to the
standard version of EM algorithm. | [
"cs.CV"
]
|
Contact tracing is of paramount importance when it comes to preventing the
spreading of infectious diseases. Contact tracing is usually performed manually
by authorized personnel. Manual contact tracing is an inefficient, error-prone,
time-consuming process of limited utility to the population at large as those
in close contact with infected individuals are informed hours, if not days,
later. This paper introduces an alternative way to manual contact tracing. The
proposed Smart Contact Tracing (SCT) system utilizes the smartphone's Bluetooth
Low Energy (BLE) signals and machine learning classifier to accurately and
quickly determined the contact profile. SCT's contribution is two-fold: a)
classification of the user's contact as high/low-risk using precise proximity
sensing, and b) user anonymity using a privacy-preserving communications
protocol. SCT leverages BLE's non-connectable advertising feature to broadcast
a signature packet when the user is in the public space. Both broadcasted and
observed signatures are stored in the user's smartphone and they are only
uploaded to a secure signature database when a user is confirmed by public
health authorities to be infected. Using received signal strength (RSS) each
smartphone estimates its distance from other user's phones and issues real-time
alerts when social distancing rules are violated. The paper includes extensive
experimentation utilizing real-life smartphone positions and a comparative
evaluation of five machine learning classifiers. Reported results indicate that
a decision tree classifier outperforms other states of the art classification
methods in terms of accuracy. Lastly, to facilitate research in this area, and
to contribute to the timely development of advanced solutions the entire data
set of six experiments with about 123,000 data points is made publicly
available. | [
"cs.LG",
"cs.CR",
"cs.HC",
"cs.NI"
]
|
We propose Styleformer, which is a style-based generator for GAN
architecture, but a convolution-free transformer-based generator. In our paper,
we explain how a transformer can generate high-quality images, overcoming the
disadvantage that convolution operations are difficult to capture global
features in an image. Furthermore, we change the demodulation of StyleGAN2 and
modify the existing transformer structure (e.g., residual connection, layer
normalization) to create a strong style-based generator with a convolution-free
structure. We also make Styleformer lighter by applying Linformer, enabling
Styleformer to generate higher resolution images and result in improvements in
terms of speed and memory. We experiment with the low-resolution image dataset
such as CIFAR-10, as well as the high-resolution image dataset like
LSUN-church. Styleformer records FID 2.82 and IS 9.94 on CIFAR-10, a benchmark
dataset, which is comparable performance to the current state-of-the-art and
outperforms all GAN-based generative models, including StyleGAN2-ADA with fewer
parameters on the unconditional setting. We also both achieve new
state-of-the-art with FID 15.17, IS 11.01, and FID 3.66, respectively on STL-10
and CelebA. We release our code at
https://github.com/Jeeseung-Park/Styleformer. | [
"cs.CV",
"eess.IV"
]
|
In most interactive image generation tasks, given regions of interest (ROI)
by users, the generated results are expected to have adequate diversities in
appearance while maintaining correct and reasonable structures in original
images. Such tasks become more challenging if only limited data is available.
Recently proposed generative models complete training based on only one image.
They pay much attention to the monolithic feature of the sample while ignoring
the actual semantic information of different objects inside the sample. As a
result, for ROI-based generation tasks, they may produce inappropriate samples
with excessive randomicity and without maintaining the related objects' correct
structures. To address this issue, this work introduces a
MOrphologic-structure-aware Generative Adversarial Network named MOGAN that
produces random samples with diverse appearances and reliable structures based
on only one image. For training for ROI, we propose to utilize the data coming
from the original image being augmented and bring in a novel module to
transform such augmented data into knowledge containing both structures and
appearances, thus enhancing the model's comprehension of the sample. To learn
the rest areas other than ROI, we employ binary masks to ensure the generation
isolated from ROI. Finally, we set parallel and hierarchical branches of the
mentioned learning process. Compared with other single image GAN schemes, our
approach focuses on internal features including the maintenance of rational
structures and variation on appearance. Experiments confirm a better capacity
of our model on ROI-based image generation tasks than its competitive peers. | [
"cs.CV"
]
|
Probabilistic models learned as density estimators can be exploited in
representation learning beside being toolboxes used to answer inference queries
only. However, how to extract useful representations highly depends on the
particular model involved. We argue that tractable inference, i.e. inference
that can be computed in polynomial time, can enable general schemes to extract
features from black box models. We plan to investigate how Tractable
Probabilistic Models (TPMs) can be exploited to generate embeddings by random
query evaluations. We devise two experimental designs to assess and compare
different TPMs as feature extractors in an unsupervised representation learning
framework. We show some experimental results on standard image datasets by
applying such a method to Sum-Product Networks and Mixture of Trees as
tractable models generating embeddings. | [
"cs.LG",
"cs.AI",
"stat.ML"
]
|
It is well known that deep learning approaches to face recognition and facial
landmark detection suffer from biases in modern training datasets. In this
work, we propose to use synthetic face images to reduce the negative effects of
dataset biases on these tasks. Using a 3D morphable face model, we generate
large amounts of synthetic face images with full control over facial shape and
color, pose, illumination, and background. With a series of experiments, we
extensively test the effects of priming deep nets by pre-training them with
synthetic faces. We observe the following positive effects for face recognition
and facial landmark detection tasks: 1) Priming with synthetic face images
improves the performance consistently across all benchmarks because it reduces
the negative effects of biases in the training data. 2) Traditional approaches
for reducing the damage of dataset bias, such as data augmentation and transfer
learning, are less effective than training with synthetic faces. 3) Using
synthetic data, we can reduce the size of real-world datasets by 75% for face
recognition and by 50% for facial landmark detection while maintaining
performance. Thus, offering a means to focus the data collection process on
less but higher quality data. | [
"cs.CV"
]
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.