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In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early
identification of failures has become crucial to maintain productivity and
prolong components' life. Of all defects, cell-level anomalies can lead to
serious failures and may affect surrounding PV modules in the long run. These
fine defects are usually captured with high spatial resolution
electroluminescence (EL) imaging. The difficulty of acquiring such images has
limited the availability of data. For this work, multiple data resources and
augmentation techniques have been used to surpass this limitation. Current
state-of-the-art detection methods extract barely low-level information from
individual PV cell images, and their performance is conditioned by the
available training data. In this article, we propose an end-to-end deep
learning pipeline that detects, locates and segments cell-level anomalies from
entire photovoltaic modules via EL images. The proposed modular pipeline
combines three deep learning techniques: 1. object detection (modified
Faster-RNN), 2. image classification (EfficientNet) and 3. weakly supervised
segmentation (autoencoder). The modular nature of the pipeline allows to
upgrade the deep learning models to the further improvements in the
state-of-the-art and also extend the pipeline towards new functionalities. | [
"cs.CV",
"cs.LG"
]
|
Vision-Language Navigation (VLN) is a task where agents learn to navigate
following natural language instructions. The key to this task is to perceive
both the visual scene and natural language sequentially. Conventional
approaches exploit the vision and language features in cross-modal grounding.
However, the VLN task remains challenging, since previous works have neglected
the rich semantic information contained in the environment (such as implicit
navigation graphs or sub-trajectory semantics). In this paper, we introduce
Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised
auxiliary reasoning tasks to take advantage of the additional training signals
derived from the semantic information. The auxiliary tasks have four reasoning
objectives: explaining the previous actions, estimating the navigation
progress, predicting the next orientation, and evaluating the trajectory
consistency. As a result, these additional training signals help the agent to
acquire knowledge of semantic representations in order to reason about its
activity and build a thorough perception of the environment. Our experiments
indicate that auxiliary reasoning tasks improve both the performance of the
main task and the model generalizability by a large margin. Empirically, we
demonstrate that an agent trained with self-supervised auxiliary reasoning
tasks substantially outperforms the previous state-of-the-art method, being the
best existing approach on the standard benchmark. | [
"cs.CV"
]
|
Encouraged by the success of contrastive learning on image classification
tasks, we propose a new self-supervised method for the structured regression
task of 3D hand pose estimation. Contrastive learning makes use of unlabeled
data for the purpose of representation learning via a loss formulation that
encourages the learned feature representations to be invariant under any image
transformation. For 3D hand pose estimation, it too is desirable to have
invariance to appearance transformation such as color jitter. However, the task
requires equivariance under affine transformations, such as rotation and
translation. To address this issue, we propose an equivariant contrastive
objective and demonstrate its effectiveness in the context of 3D hand pose
estimation. We experimentally investigate the impact of invariant and
equivariant contrastive objectives and show that learning equivariant features
leads to better representations for the task of 3D hand pose estimation.
Furthermore, we show that standard ResNets with sufficient depth, trained on
additional unlabeled data, attain improvements of up to 14.5% in PA-EPE on
FreiHAND and thus achieves state-of-the-art performance without any task
specific, specialized architectures. Code and models are available at
https://ait.ethz.ch/projects/2021/PeCLR/ | [
"cs.CV"
]
|
Identifying an appropriate underlying graph kernel that reflects pairwise
similarities is critical in many recent graph spectral signal restoration
schemes, including image denoising, dequantization, and contrast enhancement.
Existing graph learning algorithms compute the most likely entries of a
properly defined graph Laplacian matrix $\mathbf{L}$, but require a large
number of signal observations $\mathbf{z}$'s for a stable estimate. In this
work, we assume instead the availability of a relevant feature vector
$\mathbf{f}_i$ per node $i$, from which we compute an optimal feature graph via
optimization of a feature metric. Specifically, we alternately optimize the
diagonal and off-diagonal entries of a Mahalanobis distance matrix $\mathbf{M}$
by minimizing the graph Laplacian regularizer (GLR) $\mathbf{z}^{\top}
\mathbf{L} \mathbf{z}$, where edge weight is $w_{i,j} = \exp\{-(\mathbf{f}_i -
\mathbf{f}_j)^{\top} \mathbf{M} (\mathbf{f}_i - \mathbf{f}_j) \}$, given a
single observation $\mathbf{z}$. We optimize diagonal entries via proximal
gradient (PG), where we constrain $\mathbf{M}$ to be positive definite (PD) via
linear inequalities derived from the Gershgorin circle theorem. To optimize
off-diagonal entries, we design a block descent algorithm that iteratively
optimizes one row and column of $\mathbf{M}$. To keep $\mathbf{M}$ PD, we
constrain the Schur complement of sub-matrix $\mathbf{M}_{2,2}$ of $\mathbf{M}$
to be PD when optimizing via PG. Our algorithm mitigates full
eigen-decomposition of $\mathbf{M}$, thus ensuring fast computation speed even
when feature vector $\mathbf{f}_i$ has high dimension. To validate its
usefulness, we apply our feature graph learning algorithm to the problem of 3D
point cloud denoising, resulting in state-of-the-art performance compared to
competing schemes in extensive experiments. | [
"cs.CV",
"cs.LG",
"eess.IV",
"eess.SP"
]
|
Online system identification is the estimation of parameters of a dynamical
system, such as mass or friction coefficients, for each measurement of the
input and output signals. Here, the nonlinear stochastic differential equation
of a Duffing oscillator is cast to a generative model and dynamical parameters
are inferred using variational message passing on a factor graph of the model.
The approach is validated with an experiment on data from an electronic
implementation of a Duffing oscillator. The proposed inference procedure
performs as well as offline prediction error minimisation in a state-of-the-art
nonlinear model. | [
"cs.LG",
"cs.NE",
"cs.SY",
"eess.SY",
"stat.ML"
]
|
The wide availability of Commercial Off-The-Shelf (COTS) electronics that can
withstand Low Earth Orbit conditions has opened avenue for wide deployment of
CubeSats and small-satellites. CubeSats thanks to their low developmental and
launch costs offer new opportunities for rapidly demonstrating on-orbit
surveillance capabilities. In our earlier work, we proposed development of
SWIMSat (Space based Wide-angle Imaging of Meteors) a 3U CubeSat demonstrator
that is designed to observe illuminated objects entering the Earth's
atmosphere. The spacecraft would operate autonomously using a smart camera with
vision algorithms to detect, track and report of objects. Several CubeSats can
track an object in a coordinated fashion to pinpoint an object's trajectory. An
extension of this smart camera capability is to track unilluminated objects
utilizing capabilities we have been developing to track and navigate to Near
Earth Objects (NEOs). This extension enables detecting and tracking objects
that can't readily be detected by humans. The system maintains a dense star map
of the night sky and performs round the clock observations. Standard optical
flow algorithms are used to obtain trajectories of all moving objects in the
camera field of view. Through a process of elimination, certain stars maybe
occluded by a transiting unilluminated object which is then used to first
detect and obtain a trajectory of the object. Using multiple cameras observing
the event from different points of view, it may be possible then to triangulate
the position of the object in space and obtain its orbital trajectory. In this
work, the performance of our space object detection algorithm coupled with a
spacecraft guidance, navigation, and control system is demonstrated. | [
"cs.CV",
"astro-ph.IM"
]
|
Adversarial training, especially projected gradient descent (PGD), has been a
successful approach for improving robustness against adversarial attacks. After
adversarial training, gradients of models with respect to their inputs have a
preferential direction. However, the direction of alignment is not
mathematically well established, making it difficult to evaluate
quantitatively. We propose a novel definition of this direction as the
direction of the vector pointing toward the closest point of the support of the
closest inaccurate class in decision space. To evaluate the alignment with this
direction after adversarial training, we apply a metric that uses generative
adversarial networks to produce the smallest residual needed to change the
class present in the image. We show that PGD-trained models have a higher
alignment than the baseline according to our definition, that our metric
presents higher alignment values than a competing metric formulation, and that
enforcing this alignment increases the robustness of models. | [
"stat.ML",
"cs.CV",
"cs.LG"
]
|
We introduce a simple but effective unsupervised method for generating
realistic and diverse images. We train a class-conditional GAN model without
using manually annotated class labels. Instead, our model is conditional on
labels automatically derived from clustering in the discriminator's feature
space. Our clustering step automatically discovers diverse modes, and
explicitly requires the generator to cover them. Experiments on standard mode
collapse benchmarks show that our method outperforms several competing methods
when addressing mode collapse. Our method also performs well on large-scale
datasets such as ImageNet and Places365, improving both image diversity and
standard quality metrics, compared to previous methods. | [
"cs.CV",
"cs.LG"
]
|
Deep model-based Reinforcement Learning (RL) has the potential to
substantially improve the sample-efficiency of deep RL. While various
challenges have long held it back, a number of papers have recently come out
reporting success with deep model-based methods. This is a great development,
but the lack of a consistent metric to evaluate such methods makes it difficult
to compare various approaches. For example, the common single-task
sample-efficiency metric conflates improvements due to model-based learning
with various other aspects, such as representation learning, making it
difficult to assess true progress on model-based RL. To address this, we
introduce an experimental setup to evaluate model-based behavior of RL methods,
inspired by work from neuroscience on detecting model-based behavior in humans
and animals. Our metric based on this setup, the Local Change Adaptation (LoCA)
regret, measures how quickly an RL method adapts to a local change in the
environment. Our metric can identify model-based behavior, even if the method
uses a poor representation and provides insight in how close a method's
behavior is from optimal model-based behavior. We use our setup to evaluate the
model-based behavior of MuZero on a variation of the classic Mountain Car task. | [
"cs.LG",
"cs.AI",
"stat.ML"
]
|
Following great success in the image processing field, the idea of
adversarial training has been applied to tasks in the natural language
processing (NLP) field. One promising approach directly applies adversarial
training developed in the image processing field to the input word embedding
space instead of the discrete input space of texts. However, this approach
abandons such interpretability as generating adversarial texts to significantly
improve the performance of NLP tasks. This paper restores interpretability to
such methods by restricting the directions of perturbations toward the existing
words in the input embedding space. As a result, we can straightforwardly
reconstruct each input with perturbations to an actual text by considering the
perturbations to be the replacement of words in the sentence while maintaining
or even improving the task performance. | [
"cs.LG",
"cs.CL",
"stat.ML"
]
|
We present a novel segmentation algorithm based on a hierarchical
representation of images. The main contribution of this work is to explore the
capabilities of the A Contrario reasoning when applied to the segmentation
problem, and to overcome the limitations of current algorithms within that
framework. This exploratory approach has three main goals.
Our first goal is to extend the search space of greedy merging algorithms to
the set of all partitions spanned by a certain hierarchy, and to cast the
segmentation as a selection problem within this space. In this way we increase
the number of tested partitions and thus we potentially improve the
segmentation results. In addition, this space is considerably smaller than the
space of all possible partitions, thus we still keep the complexity controlled.
Our second goal aims to improve the locality of region merging algorithms,
which usually merge pairs of neighboring regions. In this work, we overcome
this limitation by introducing a validation procedure for complete partitions,
rather than for pairs of regions.
The third goal is to perform an exhaustive experimental evaluation
methodology in order to provide reproducible results.
Finally, we embed the selection process on a statistical A Contrario
framework which allows us to have only one free parameter related to the
desired scale. | [
"cs.CV"
]
|
Object detection is an important task in computer vision and learning
systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an
algorithm of fast and accurate object detection. By sampling particle windows
from a proposal distribution (PD), MPW avoids exhaustively scanning the image.
Despite its success, it is unknown how to determine the number of stages and
the number of particle windows in each stage. Moreover, it has to generate too
many particle windows in the initialization step and it redraws unnecessary too
many particle windows around object-like regions. In this paper, we attempt to
solve the problems of MPW. An important fact we used is that there is large
probability for a randomly generated particle window not to contain the object
because the object is a sparse event relevant to the huge number of candidate
windows. Therefore, we design the proposal distribution so as to efficiently
reject the huge number of non-object windows. Specifically, we propose the
concepts of rejection, acceptance, and ambiguity windows and regions. This
contrasts to MPW which utilizes only on region of support. The PD of MPW is
acceptance-oriented whereas the PD of our method (called iPW) is
rejection-oriented. Experimental results on human and face detection
demonstrate the efficiency and effectiveness of the iPW algorithm. The source
code is publicly accessible. | [
"cs.CV",
"cs.LG"
]
|
Dynamic dispatching aims to smartly allocate the right resources to the right
place at the right time. Dynamic dispatching is one of the core problems for
operations optimization in the mining industry. Theoretically, deep
reinforcement learning (RL) should be a natural fit to solve this problem.
However, the industry relies on heuristics or even human intuitions, which are
often short-sighted and sub-optimal solutions. In this paper, we review the
main challenges in using deep RL to address the dynamic dispatching problem in
the mining industry. | [
"cs.LG",
"cs.AI"
]
|
Understanding the behavior of road users is of vital importance for the
development of trajectory prediction systems. In this context, the latest
advances have focused on recurrent structures, establishing the social
interaction between the agents involved in the scene. More recently, simpler
structures have also been introduced for predicting pedestrian trajectories,
based on Transformer Networks, and using positional information. They allow the
individual modelling of each agent's trajectory separately without any complex
interaction terms. Our model exploits these simple structures by adding
augmented data (position and heading), and adapting their use to the problem of
vehicle trajectory prediction in urban scenarios in prediction horizons up to 5
seconds. In addition, a cross-performance analysis is performed between
different types of scenarios, including highways, intersections and
roundabouts, using recent datasets (inD, rounD, highD and INTERACTION). Our
model achieves state-of-the-art results and proves to be flexible and adaptable
to different types of urban contexts. | [
"cs.CV"
]
|
Noise is an inherent issue of low-light image capture, one which is
exacerbated on mobile devices due to their narrow apertures and small sensors.
One strategy for mitigating noise in a low-light situation is to increase the
shutter time of the camera, thus allowing each photosite to integrate more
light and decrease noise variance. However, there are two downsides of long
exposures: (a) bright regions can exceed the sensor range, and (b) camera and
scene motion will result in blurred images. Another way of gathering more light
is to capture multiple short (thus noisy) frames in a "burst" and intelligently
integrate the content, thus avoiding the above downsides. In this paper, we use
the burst-capture strategy and implement the intelligent integration via a
recurrent fully convolutional deep neural net (CNN). We build our novel,
multiframe architecture to be a simple addition to any single frame denoising
model, and design to handle an arbitrary number of noisy input frames. We show
that it achieves state of the art denoising results on our burst dataset,
improving on the best published multi-frame techniques, such as VBM4D and
FlexISP. Finally, we explore other applications of image enhancement by
integrating content from multiple frames and demonstrate that our DNN
architecture generalizes well to image super-resolution. | [
"cs.CV",
"cs.LG",
"stat.ML"
]
|
Automatic histopathology image segmentation is crucial to disease analysis.
Limited available labeled data hinders the generalizability of trained models
under the fully supervised setting. Semi-supervised learning (SSL) based on
generative methods has been proven to be effective in utilizing diverse image
characteristics. However, it has not been well explored what kinds of generated
images would be more useful for model training and how to use such images. In
this paper, we propose a new data guided generative method for histopathology
image segmentation by leveraging the unlabeled data distributions. First, we
design an image generation module. Image content and style are disentangled and
embedded in a clustering-friendly space to utilize their distributions. New
images are synthesized by sampling and cross-combining contents and styles.
Second, we devise an effective data selection policy for judiciously sampling
the generated images: (1) to make the generated training set better cover the
dataset, the clusters that are underrepresented in the original training set
are covered more; (2) to make the training process more effective, we identify
and oversample the images of "hard cases" in the data for which annotated
training data may be scarce. Our method is evaluated on glands and nuclei
datasets. We show that under both the inductive and transductive settings, our
SSL method consistently boosts the performance of common segmentation models
and attains state-of-the-art results. | [
"cs.CV"
]
|
State-of-the-art methods for self-supervised learning (SSL) build
representations by maximizing the similarity between different transformed
"views" of a sample. Without sufficient diversity in the transformations used
to create views, however, it can be difficult to overcome nuisance variables in
the data and build rich representations. This motivates the use of the dataset
itself to find similar, yet distinct, samples to serve as views for one
another. In this paper, we introduce Mine Your Own vieW (MYOW), a new approach
for self-supervised learning that looks within the dataset to define diverse
targets for prediction. The idea behind our approach is to actively mine views,
finding samples that are neighbors in the representation space of the network,
and then predict, from one sample's latent representation, the representation
of a nearby sample. After showing the promise of MYOW on benchmarks used in
computer vision, we highlight the power of this idea in a novel application in
neuroscience where SSL has yet to be applied. When tested on multi-unit neural
recordings, we find that MYOW outperforms other self-supervised approaches in
all examples (in some cases by more than 10%), and often surpasses the
supervised baseline. With MYOW, we show that it is possible to harness the
diversity of the data to build rich views and leverage self-supervision in new
domains where augmentations are limited or unknown. | [
"cs.LG"
]
|
Point clouds and images could provide complementary information when
representing 3D objects. Fusing the two kinds of data usually helps to improve
the detection results. However, it is challenging to fuse the two data
modalities, due to their different characteristics and the interference from
the non-interest areas. To solve this problem, we propose a Multi-Branch Deep
Fusion Network (MBDF-Net) for 3D object detection. The proposed detector has
two stages. In the first stage, our multi-branch feature extraction network
utilizes Adaptive Attention Fusion (AAF) modules to produce cross-modal fusion
features from single-modal semantic features. In the second stage, we use a
region of interest (RoI) -pooled fusion module to generate enhanced local
features for refinement. A novel attention-based hybrid sampling strategy is
also proposed for selecting key points in the downsampling process. We evaluate
our approach on two widely used benchmark datasets including KITTI and
SUN-RGBD. The experimental results demonstrate the advantages of our method
over state-of-the-art approaches. | [
"cs.CV"
]
|
In a series of papers by Dai and colleagues [1,2], a feature map (or kernel)
was introduced for semi- and unsupervised learning. This feature map is build
from the output of an ensemble of classifiers trained without using the
ground-truth class labels. In this critique, we analyze the latest version of
this series of papers, which is called Ensemble Projections [2]. We show that
the results reported in [2] were not well conducted, and that Ensemble
Projections performs poorly for semi-supervised learning. | [
"cs.CV"
]
|
In this work, we study the problem of word-level confidence calibration for
scene-text recognition (STR). Although the topic of confidence calibration has
been an active research area for the last several decades, the case of
structured and sequence prediction calibration has been scarcely explored. We
analyze several recent STR methods and show that they are consistently
overconfident. We then focus on the calibration of STR models on the word
rather than the character level. In particular, we demonstrate that for
attention based decoders, calibration of individual character predictions
increases word-level calibration error compared to an uncalibrated model. In
addition, we apply existing calibration methodologies as well as new
sequence-based extensions to numerous STR models, demonstrating reduced
calibration error by up to a factor of nearly 7. Finally, we show consistently
improved accuracy results by applying our proposed sequence calibration method
as a preprocessing step to beam-search. | [
"cs.CV",
"cs.LG"
]
|
Recently, the Vision Transformer (ViT) has shown impressive performance on
high-level and low-level vision tasks. In this paper, we propose a new ViT
architecture, named Hybrid Local-Global Vision Transformer (HyLoG-ViT), for
single image dehazing. The HyLoG-ViT block consists of two paths, the local ViT
path and the global ViT path, which are used to capture local and global
dependencies. The hybrid features are fused via convolution layers. As a
result, the HyLoG-ViT reduces the computational complexity and introduces
locality in the networks. Then, the HyLoG-ViT blocks are incorporated within
our dehazing networks, which jointly learn the intrinsic image decomposition
and image dehazing. Specifically, the network consists of one shared encoder
and three decoders for reflectance prediction, shading prediction, and
haze-free image generation. The tasks of reflectance and shading prediction can
produce meaningful intermediate features that can serve as complementary
features for haze-free image generation. To effectively aggregate the
complementary features, we propose a complementary features selection module
(CFSM) to select the useful ones for image dehazing. Extensive experiments on
homogeneous, non-homogeneous, and nighttime dehazing tasks reveal that our
proposed Transformer-based dehazing network can achieve comparable or even
better performance than CNNs-based dehazing models. | [
"cs.CV",
"68U10 (Primary) 94A08, 54H30 (Secondary)",
"I.4.3; I.4.4"
]
|
Videos captured using Transmission Electron Microscopy (TEM) can encode
details regarding the morphological and temporal evolution of a material by
taking snapshots of the microstructure sequentially. However, manual analysis
of such video is tedious, error-prone, unreliable, and prohibitively
time-consuming if one wishes to analyze a significant fraction of frames for
even videos of modest length. In this work, we developed an automated TEM video
analysis system for microstructural features based on the advanced object
detection model called YOLO and tested the system on an in-situ ion irradiation
TEM video of dislocation loops formed in a FeCrAl alloy. The system provides
analysis of features observed in TEM including both static and dynamic
properties using the YOLO-based defect detection module coupled to a geometry
analysis module and a dynamic tracking module. Results show that the system can
achieve human comparable performance with an F1 score of 0.89 for fast,
consistent, and scalable frame-level defect analysis. This result is obtained
on a real but exceptionally clean and stable data set and more challenging data
sets may not achieve this performance. The dynamic tracking also enabled
evaluation of individual defect evolution like per defect growth rate at a
fidelity never before achieved using common human analysis methods. Our work
shows that automatically detecting and tracking interesting microstructures and
properties contained in TEM videos is viable and opens new doors for evaluating
materials dynamics. | [
"cs.CV",
"cond-mat.mtrl-sci"
]
|
Learning accurate drug representation is essential for tasks such as
computational drug repositioning and prediction of drug side-effects. A drug
hierarchy is a valuable source that encodes human knowledge of drug relations
in a tree-like structure where drugs that act on the same organs, treat the
same disease, or bind to the same biological target are grouped together.
However, its utility in learning drug representations has not yet been
explored, and currently described drug representations cannot place novel
molecules in a drug hierarchy. Here, we develop a semi-supervised drug
embedding that incorporates two sources of information: (1) underlying chemical
grammar that is inferred from molecular structures of drugs and drug-like
molecules (unsupervised), and (2) hierarchical relations that are encoded in an
expert-crafted hierarchy of approved drugs (supervised). We use the Variational
Auto-Encoder (VAE) framework to encode the chemical structures of molecules and
use the knowledge-based drug-drug similarity to induce the clustering of drugs
in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical
concepts. Both quantitative and qualitative results support that the learned
drug embedding can accurately reproduce the chemical structure and induce the
hierarchical relations among drugs. Furthermore, our approach can infer the
pharmacological properties of novel molecules by retrieving similar drugs from
the embedding space. We demonstrate that the learned drug embedding can be used
to find new uses for existing drugs and to discover side-effects. We show that
it significantly outperforms baselines in both tasks. | [
"cs.LG",
"q-bio.MN",
"q-bio.QM",
"stat.ML"
]
|
This paper investigates the problem of reconstructing hyperspectral (HS)
images from single RGB images captured by commercial cameras, \textbf{without}
using paired HS and RGB images during training. To tackle this challenge, we
propose a new lightweight and end-to-end learning-based framework.
Specifically, on the basis of the intrinsic imaging degradation model of RGB
images from HS images, we progressively spread the differences between input
RGB images and re-projected RGB images from recovered HS images via effective
unsupervised camera spectral response function estimation. To enable the
learning without paired ground-truth HS images as supervision, we adopt the
adversarial learning manner and boost it with a simple yet effective
$\mathcal{L}_1$ gradient clipping scheme. Besides, we embed the semantic
information of input RGB images to locally regularize the unsupervised
learning, which is expected to promote pixels with identical semantics to have
consistent spectral signatures. In addition to conducting quantitative
experiments over two widely-used datasets for HS image reconstruction from
synthetic RGB images, we also evaluate our method by applying recovered HS
images from real RGB images to HS-based visual tracking. Extensive results show
that our method significantly outperforms state-of-the-art unsupervised methods
and even exceeds the latest supervised method under some settings. The source
code is public available at
https://github.com/zbzhzhy/Unsupervised-Spectral-Reconstruction. | [
"cs.CV"
]
|
Optical character recognition (OCR) is widely applied in real applications
serving as a key preprocessing tool. The adoption of deep neural network (DNN)
in OCR results in the vulnerability against adversarial examples which are
crafted to mislead the output of the threat model. Different from vanilla
colorful images, images of printed text have clear backgrounds usually.
However, adversarial examples generated by most of the existing adversarial
attacks are unnatural and pollute the background severely. To address this
issue, we propose a watermark attack method to produce natural distortion that
is in the disguise of watermarks and evade human eyes' detection. Experimental
results show that watermark attacks can yield a set of natural adversarial
examples attached with watermarks and attain similar attack performance to the
state-of-the-art methods in different attack scenarios. | [
"cs.CV",
"cs.CR",
"cs.LG"
]
|
Predicting human perceptual similarity is a challenging subject of ongoing
research. The visual process underlying this aspect of human vision is thought
to employ multiple different levels of visual analysis (shapes, objects,
texture, layout, color, etc). In this paper, we postulate that the perception
of image similarity is not an explicitly learned capability, but rather one
that is a byproduct of learning others. This claim is supported by leveraging
representations learned from a diverse set of visual tasks and using them
jointly to predict perceptual similarity. This is done via simple feature
concatenation, without any further learning. Nevertheless, experiments
performed on the challenging Totally-Looks-Like (TLL) benchmark significantly
surpass recent baselines, closing much of the reported gap towards prediction
of human perceptual similarity. We provide an analysis of these results and
discuss them in a broader context of emergent visual capabilities and their
implications on the course of machine-vision research. | [
"cs.CV",
"cs.AI",
"cs.LG"
]
|
Unsupervised domain adaptation (UDA) has achieved unprecedented success in
improving the cross-domain robustness of object detection models. However,
existing UDA methods largely ignore the instantaneous data distribution during
model learning, which could deteriorate the feature representation given large
domain shift. In this work, we propose a Self-Guided Adaptation (SGA) model,
target at aligning feature representation and transferring object detection
models across domains while considering the instantaneous alignment difficulty.
The core of SGA is to calculate "hardness" factors for sample pairs indicating
domain distance in a kernel space. With the hardness factor, the proposed SGA
adaptively indicates the importance of samples and assigns them different
constrains. Indicated by hardness factors, Self-Guided Progressive Sampling
(SPS) is implemented in an "easy-to-hard" way during model adaptation. Using
multi-stage convolutional features, SGA is further aggregated to fully align
hierarchical representations of detection models. Extensive experiments on
commonly used benchmarks show that SGA improves the state-of-the-art methods
with significant margins, while demonstrating the effectiveness on large domain
shift. | [
"cs.CV"
]
|
Many recent methods of zero-shot learning (ZSL) attempt to utilize generative
model to generate the unseen visual samples from semantic descriptions and
random noise. Therefore, the ZSL problem becomes a traditional supervised
classification problem. However, most of the existing methods based on the
generative model only focus on the quality of synthesized samples at the
training stage, and ignore the importance of the zero-shot recognition stage.
In this paper, we consider both the above two points and propose a novel
approach. Specially, we select the Generative Adversarial Network (GAN) as our
generative model. In order to improve the quality of synthesized samples,
considering the internal relation of the semantic description in the semantic
space as well as the fact that the seen and unseen visual information belong to
different domains, we propose a bi-semantic reconstructing (BSR) component
which contain two different semantic reconstructing regressors to lead the
training of GAN. Since the semantic descriptions are available during the
training stage, to further improve the ability of classifier, we combine the
visual samples and semantic descriptions to train a classifier. At the
recognition stage, we naturally utilize the BSR component to transfer the
visual features and semantic descriptions, and concatenate them for
classification. Experimental results show that our method outperforms the state
of the art on several ZSL benchmark datasets with significant improvements. | [
"cs.CV",
"cs.LG"
]
|
Long Short Term Memory Fully Convolutional Neural Networks (LSTM-FCN) and
Attention LSTM-FCN (ALSTM-FCN) have shown to achieve state-of-the-art
performance on the task of classifying time series signals on the old
University of California-Riverside (UCR) time series repository. However, there
has been no study on why LSTM-FCN and ALSTM-FCN perform well. In this paper, we
perform a series of ablation tests (3627 experiments) on LSTM-FCN and ALSTM-FCN
to provide a better understanding of the model and each of its sub-module.
Results from the ablation tests on ALSTM-FCN and LSTM-FCN show that the LSTM
and the FCN blocks perform better when applied in a conjoined manner. Two
z-normalizing techniques, z-normalizing each sample independently and
z-normalizing the whole dataset, are compared using a Wilcoxson signed-rank
test to show a statistical difference in performance. In addition, we provide
an understanding of the impact dimension shuffle has on LSTM-FCN by comparing
its performance with LSTM-FCN when no dimension shuffle is applied. Finally, we
demonstrate the performance of the LSTM-FCN when the LSTM block is replaced by
a GRU, basic RNN, and Dense Block. | [
"cs.LG",
"stat.ML"
]
|
Although much significant progress has been made in the research field of
object detection with deep learning, there still exists a challenging task for
the objects with small size, which is notably pronounced in UAV-captured
images. Addressing these issues, it is a critical need to explore the feature
extraction methods that can extract more sufficient feature information of
small objects. In this paper, we propose a novel method called Dense Multiscale
Feature Fusion Pyramid Networks(DMFFPN), which is aimed at obtaining rich
features as much as possible, improving the information propagation and reuse.
Specifically, the dense connection is designed to fully utilize the
representation from the different convolutional layers. Furthermore, cascade
architecture is applied in the second stage to enhance the localization
capability. Experiments on the drone-based datasets named VisDrone-DET suggest
a competitive performance of our method. | [
"cs.CV",
"cs.LG",
"eess.IV"
]
|
Recently, graph neural networks (GNNs) have achieved remarkable performances
for quantum mechanical problems. However, a graph convolution can only cover a
localized region, and cannot capture long-range interactions of atoms. This
behavior is contrary to theoretical interatomic potentials, which is a
fundamental limitation of the spatial based GNNs. In this work, we propose a
novel attention-based framework for molecular property prediction tasks. We
represent a molecular conformation as a discrete atomic sequence combined by
atom-atom distance attributes, named Geometry-aware Transformer (GeoT). In
particular, we adopt a Transformer architecture, which has been widely used for
sequential data. Our proposed model trains sequential representations of
molecular graphs based on globally constructed attentions, maintaining all
spatial arrangements of atom pairs. Our method does not suffer from cost
intensive computations, such as angle calculations. The experimental results on
several public benchmarks and visualization maps verified that keeping the
long-range interatomic attributes can significantly improve the model
predictability. | [
"cs.LG",
"physics.chem-ph"
]
|
Multi-task learning (MTL) is a machine learning technique aiming to improve
model performance by leveraging information across many tasks. It has been used
extensively on various data modalities, including electronic health record
(EHR) data. However, despite significant use on EHR data, there has been little
systematic investigation of the utility of MTL across the diverse set of
possible tasks and training schemes of interest in healthcare. In this work, we
examine MTL across a battery of tasks on EHR time-series data. We find that
while MTL does suffer from common negative transfer, we can realize significant
gains via MTL pre-training combined with single-task fine-tuning. We
demonstrate that these gains can be achieved in a task-independent manner and
offer not only minor improvements under traditional learning, but also notable
gains in a few-shot learning context, thereby suggesting this could be a
scalable vehicle to offer improved performance in important healthcare
contexts. | [
"cs.LG",
"stat.ML"
]
|
Motivated by the center-surround mechanism in the human visual attention
system, we propose to use average contrast maps for the challenge of pedestrian
detection in street scenes due to the observation that pedestrians indeed
exhibit discriminative contrast texture. Our main contributions are first to
design a local, statistical multi-channel descriptorin order to incorporate
both color and gradient information. Second, we introduce a multi-direction and
multi-scale contrast scheme based on grid-cells in order to integrate
expressive local variations. Contributing to the issue of selecting most
discriminative features for assessing and classification, we perform extensive
comparisons w.r.t. statistical descriptors, contrast measurements, and scale
structures. This way, we obtain reasonable results under various
configurations. Empirical findings from applying our optimized detector on the
INRIA and Caltech pedestrian datasets show that our features yield
state-of-the-art performance in pedestrian detection. | [
"cs.CV"
]
|
Molecular graph generation is a fundamental problem for drug discovery and
has been attracting growing attention. The problem is challenging since it
requires not only generating chemically valid molecular structures but also
optimizing their chemical properties in the meantime. Inspired by the recent
progress in deep generative models, in this paper we propose a flow-based
autoregressive model for graph generation called GraphAF. GraphAF combines the
advantages of both autoregressive and flow-based approaches and enjoys: (1)
high model flexibility for data density estimation; (2) efficient parallel
computation for training; (3) an iterative sampling process, which allows
leveraging chemical domain knowledge for valency checking. Experimental results
show that GraphAF is able to generate 68% chemically valid molecules even
without chemical knowledge rules and 100% valid molecules with chemical rules.
The training process of GraphAF is two times faster than the existing
state-of-the-art approach GCPN. After fine-tuning the model for goal-directed
property optimization with reinforcement learning, GraphAF achieves
state-of-the-art performance on both chemical property optimization and
constrained property optimization. | [
"cs.LG",
"stat.ML"
]
|
We suggest a deep learning based sensor signal processing method to remove
chemical, kinetic and electrical artifacts from ion selective electrodes'
measured values. An ISE is used to investigate the concentration of a specific
ion from aqueous solution, by measuring the Nernst potential along the glass
membrane. However, application of ISE on a mixture of multiple ion has some
problem. First problem is a chemical artifact which is called ion interference
effect. Electrically charged particles interact with each other and flows
through the glass membrane of different ISEs. Second problem is the kinetic
artifact caused by the movement of the liquid. Water molecules collide with the
glass membrane causing abnormal peak values of voltage. The last artifact is
the interference of ISEs. When multiple ISEs are dipped into same solution, one
electrode's signal emission interference voltage measurement of other
electrodes. Therefore, an ISE is recommended to be applied on single-ion
solution, without any other sensors applied at the same time. Deep learning
approach can remove both 3 artifacts at the same time. The proposed method used
5 layers of artificial neural networks to regress correct signal to remove
complex artifacts with one-shot calculation. Its MAPE was less than 1.8% and R2
of regression was 0.997. A randomly chosen value of AI-processed data has MAPE
less than 5% (p-value 0.016). | [
"cs.LG",
"eess.SP"
]
|
Few researches have been proposed specifically for real-time semantic
segmentation in rainy environments. However, the demand in this area is huge
and it is challenging for lightweight networks. Therefore, this paper proposes
a lightweight network which is specially designed for the foreground
segmentation in rainy environments, named De-raining Semantic Segmentation
Network (DRSNet). By analyzing the characteristics of raindrops, the
MultiScaleSE Block is targetedly designed to encode the input image, it uses
multi-scale dilated convolutions to increase the receptive field, and SE
attention mechanism to learn the weights of each channels. In order to combine
semantic information between different encoder and decoder layers, it is
proposed to use Asymmetric Skip, that is, the higher semantic layer of encoder
employs bilinear interpolation and the output passes through pointwise
convolution, then added element-wise to the lower semantic layer of decoder.
According to the control experiments, the performances of MultiScaleSE Block
and Asymmetric Skip compared with SEResNet18 and Symmetric Skip respectively
are improved to a certain degree on the Foreground Accuracy index. The
parameters and the floating point of operations (FLOPs) of DRSNet is only 0.54M
and 0.20GFLOPs separately. The state-of-the-art results and real-time
performances are achieved on both the UESTC all-day Scenery add rain
(UAS-add-rain) and the Baidu People Segmentation add rain (BPS-add-rain)
benchmarks with the input sizes of 192*128, 384*256 and 768*512. The speed of
DRSNet exceeds all the networks within 1GFLOPs, and Foreground Accuracy index
is also the best among the similar magnitude networks on both benchmarks. | [
"cs.CV"
]
|
Video summarization plays an important role in selecting keyframe for
understanding a video. Traditionally, it aims to find the most representative
and diverse contents (or frames) in a video for short summaries. Recently,
query-conditioned video summarization has been introduced, which considers user
queries to learn more user-oriented summaries and its preference. However,
there are obstacles in text queries for user subjectivity and finding
similarity between the user query and input frames. In this work, (i) Image is
introduced as a query for user preference (ii) a mathematical model is proposed
to minimize redundancy based on the loss function & summary variance and (iii)
the similarity score between the query image and input video to obtain the
summarized video. Furthermore, the Object-based Query Image (OQI) dataset has
been introduced, which contains the query images. The proposed method has been
validated using UT Egocentric (UTE) dataset. The proposed model successfully
resolved the issues of (i) user preference, (ii) recognize important frames and
selecting that keyframe in daily life videos, with different illumination
conditions. The proposed method achieved 57.06% average F1-Score for UTE
dataset and outperforms the existing state-of-theart by 11.01%. The process
time is 7.81 times faster than actual time of video Experiments on a recently
proposed UTE dataset show the efficiency of the proposed method | [
"cs.CV"
]
|
With the high requirements of automation in the era of Industry 4.0, anomaly
detection plays an increasingly important role in higher safety and reliability
in the production and manufacturing industry. Recently, autoencoders have been
widely used as a backend algorithm for anomaly detection. Different techniques
have been developed to improve the anomaly detection performance of
autoencoders. Nonetheless, little attention has been paid to the latent
representations learned by autoencoders. In this paper, we propose a novel
selection-and-weighting-based anomaly detection framework called SWAD. In
particular, the learned latent representations are individually selected and
weighted. Experiments on both benchmark and real-world datasets have shown the
effectiveness and superiority of SWAD. On the benchmark datasets, the SWAD
framework has reached comparable or even better performance than the
state-of-the-art approaches. | [
"cs.LG"
]
|
FPN is a common component used in object detectors, it supplements
multi-scale information by adjacent level features interpolation and summation.
However, due to the existence of nonlinear operations and the convolutional
layers with different output dimensions, the relationship between different
levels is much more complex, the pixel-wise summation is not an efficient
approach. In this paper, we first analyze the design defects from pixel level
and feature map level. Then, we design a novel parameter-free feature pyramid
networks named Dual Refinement Feature Pyramid Networks (DRFPN) for the
problems. Specifically, DRFPN consists of two modules: Spatial Refinement Block
(SRB) and Channel Refinement Block (CRB). SRB learns the location and content
of sampling points based on contextual information between adjacent levels. CRB
learns an adaptive channel merging method based on attention mechanism. Our
proposed DRFPN can be easily plugged into existing FPN-based models. Without
bells and whistles, for two-stage detectors, our model outperforms different
FPN-based counterparts by 1.6 to 2.2 AP on the COCO detection benchmark, and
1.5 to 1.9 AP on the COCO segmentation benchmark. For one-stage detectors,
DRFPN improves anchor-based RetinaNet by 1.9 AP and anchor-free FCOS by 1.3 AP
when using ResNet50 as backbone. Extensive experiments verifies the robustness
and generalization ability of DRFPN. The code will be made publicly available. | [
"cs.CV"
]
|
The emergence of Graph Convolutional Network (GCN) has greatly boosted the
progress of graph learning. However, two disturbing factors, noise and
redundancy in graph data, and lack of interpretation for prediction results,
impede further development of GCN. One solution is to recognize a predictive
yet compressed subgraph to get rid of the noise and redundancy and obtain the
interpretable part of the graph. This setting of subgraph is similar to the
information bottleneck (IB) principle, which is less studied on
graph-structured data and GCN. Inspired by the IB principle, we propose a novel
subgraph information bottleneck (SIB) framework to recognize such subgraphs,
named IB-subgraph. However, the intractability of mutual information and the
discrete nature of graph data makes the objective of SIB notoriously hard to
optimize. To this end, we introduce a bilevel optimization scheme coupled with
a mutual information estimator for irregular graphs. Moreover, we propose a
continuous relaxation for subgraph selection with a connectivity loss for
stabilization. We further theoretically prove the error bound of our estimation
scheme for mutual information and the noise-invariant nature of IB-subgraph.
Extensive experiments on graph learning and large-scale point cloud tasks
demonstrate the superior property of IB-subgraph. | [
"cs.LG",
"cs.AI"
]
|
In the last few years, we have seen the transformative impact of deep
learning in many applications, particularly in speech recognition and computer
vision. Inspired by Google's Inception-ResNet deep convolutional neural network
(CNN) for image classification, we have developed "Chemception", a deep CNN for
the prediction of chemical properties, using just the images of 2D drawings of
molecules. We develop Chemception without providing any additional explicit
chemistry knowledge, such as basic concepts like periodicity, or advanced
features like molecular descriptors and fingerprints. We then show how
Chemception can serve as a general-purpose neural network architecture for
predicting toxicity, activity, and solvation properties when trained on a
modest database of 600 to 40,000 compounds. When compared to multi-layer
perceptron (MLP) deep neural networks trained with ECFP fingerprints,
Chemception slightly outperforms in activity and solvation prediction and
slightly underperforms in toxicity prediction. Having matched the performance
of expert-developed QSAR/QSPR deep learning models, our work demonstrates the
plausibility of using deep neural networks to assist in computational chemistry
research, where the feature engineering process is performed primarily by a
deep learning algorithm. | [
"stat.ML",
"cs.AI",
"cs.CE",
"cs.CV",
"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"
]
|
Pedestrian detection benefits from deep learning technology and gains rapid
development in recent years. Most of detectors follow general object detection
frame, i.e. default boxes and two-stage process. Recently, anchor-free and
one-stage detectors have been introduced into this area. However, their
accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of
anchor-free detectors and the accuracy of two-stage ones simultaneously, we
propose some adaptations based on a detector, Center and Scale Prediction(CSP).
The main contributions of our paper are: (1) We improve the robustness of CSP
and make it easier to train. (2) We propose a novel method to predict width,
namely compressing width. (3) We achieve the second best performance on
CityPersons benchmark, i.e. 9.3% log-average miss rate(MR) on reasonable set,
8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and
one-stage detector can still have high accuracy. (4) We explore some
capabilities of Switchable Normalization which are not mentioned in its
original paper. | [
"cs.CV"
]
|
One major branch of saliency object detection methods is diffusion-based
which construct a graph model on a given image and diffuse seed saliency values
to the whole graph by a diffusion matrix. While their performance is sensitive
to specific feature spaces and scales used for the diffusion matrix definition,
little work has been published to systematically promote the robustness and
accuracy of salient object detection under the generic mechanism of diffusion.
In this work, we firstly present a novel view of the working mechanism of the
diffusion process based on mathematical analysis, which reveals that the
diffusion process is actually computing the similarity of nodes with respect to
the seeds based on diffusion maps. Following this analysis, we propose super
diffusion, a novel inclusive learning-based framework for salient object
detection, which makes the optimum and robust performance by integrating a
large pool of feature spaces, scales and even features originally computed for
non-diffusion-based salient object detection. A closed-form solution of the
optimal parameters for the integration is determined through supervised
learning. At the local level, we propose to promote each individual diffusion
before the integration. Our mathematical analysis reveals the close
relationship between saliency diffusion and spectral clustering. Based on this,
we propose to re-synthesize each individual diffusion matrix from the most
discriminative eigenvectors and the constant eigenvector (for saliency
normalization). The proposed framework is implemented and experimented on
prevalently used benchmark datasets, consistently leading to state-of-the-art
performance. | [
"cs.CV"
]
|
Temporal action detection is a fundamental yet challenging task in video
understanding. Video context is a critical cue to effectively detect actions,
but current works mainly focus on temporal context, while neglecting semantic
context as well as other important context properties. In this work, we propose
a graph convolutional network (GCN) model to adaptively incorporate multi-level
semantic context into video features and cast temporal action detection as a
sub-graph localization problem. Specifically, we formulate video snippets as
graph nodes, snippet-snippet correlations as edges, and actions associated with
context as target sub-graphs. With graph convolution as the basic operation, we
design a GCN block called GCNeXt, which learns the features of each node by
aggregating its context and dynamically updates the edges in the graph. To
localize each sub-graph, we also design an SGAlign layer to embed each
sub-graph into the Euclidean space. Extensive experiments show that G-TAD is
capable of finding effective video context without extra supervision and
achieves state-of-the-art performance on two detection benchmarks. On
ActivityNet-1.3, it obtains an average mAP of 34.09%; on THUMOS14, it reaches
51.6% at [email protected] when combined with a proposal processing method. G-TAD code is
publicly available at https://github.com/frostinassiky/gtad. | [
"cs.CV"
]
|
A serious problem in image classification is that a trained model might
perform well for input data that originates from the same distribution as the
data available for model training, but performs much worse for
out-of-distribution (OOD) samples. In real-world safety-critical applications,
in particular, it is important to be aware if a new data point is OOD. To date,
OOD detection is typically addressed using either confidence scores,
auto-encoder based reconstruction, or by contrastive learning. However, the
global image context has not yet been explored to discriminate the non-local
objectness between in-distribution and OOD samples. This paper proposes a
first-of-its-kind OOD detection architecture named OODformer that leverages the
contextualization capabilities of the transformer. Incorporating the
trans\-former as the principal feature extractor allows us to exploit the
object concepts and their discriminate attributes along with their
co-occurrence via visual attention. Using the contextualised embedding, we
demonstrate OOD detection using both class-conditioned latent space similarity
and a network confidence score. Our approach shows improved generalizability
across various datasets. We have achieved a new state-of-the-art result on
CIFAR-10/-100 and ImageNet30. | [
"cs.CV"
]
|
Automated Vehicle License Plate (VLP) detection and recognition have ended up
being a significant research issue as of late. VLP localization and recognition
are some of the most essential techniques for managing traffic using digital
techniques. In this paper, four smart systems are developed to recognize
Egyptian vehicles license plates. Two systems are based on character
recognition, which are (System1, Characters Recognition with Classical Machine
Learning) and (System2, Characters Recognition with Deep Machine Learning). The
other two systems are based on the whole plate recognition which are (System3,
Whole License Plate Recognition with Classical Machine Learning) and (System4,
Whole License Plate Recognition with Deep Machine Learning). We use object
detection algorithms, and machine learning based object recognition algorithms.
The performance of the developed systems has been tested on real images, and
the experimental results demonstrate that the best detection accuracy rate for
VLP is provided by using the deep learning method. Where the VLP detection
accuracy rate is better than the classical system by 32%. However, the best
detection accuracy rate for Vehicle License Plate Arabic Character (VLPAC) is
provided by using the classical method. Where VLPAC detection accuracy rate is
better than the deep learning-based system by 6%. Also, the results show that
deep learning is better than the classical technique used in VLP recognition
processes. Where the recognition accuracy rate is better than the classical
system by 8%. Finally, the paper output recommends a robust VLP recognition
system based on both statistical and deep machine learning. | [
"cs.CV",
"cs.LG",
"eess.IV"
]
|
Multicuts enable to conveniently represent discrete graphical models for
unsupervised and supervised image segmentation, in the case of local energy
functions that exhibit symmetries. The basic Potts model and natural extensions
thereof to higher-order models provide a prominent class of such objectives,
that cover a broad range of segmentation problems relevant to image analysis
and computer vision. We exhibit a way to systematically take into account such
higher-order terms for computational inference. Furthermore, we present results
of a comprehensive and competitive numerical evaluation of a variety of
dedicated cutting-plane algorithms. Our approach enables the globally optimal
evaluation of a significant subset of these models, without compromising
runtime. Polynomially solvable relaxations are studied as well, along with
advanced rounding schemes for post-processing. | [
"cs.CV"
]
|
Feature visualizations such as synthetic maximally activating images are a
widely used explanation method to better understand the information processing
of convolutional neural networks (CNNs). At the same time, there are concerns
that these visualizations might not accurately represent CNNs' inner workings.
Here, we measure how much extremely activating images help humans to predict
CNN activations. Using a well-controlled psychophysical paradigm, we compare
the informativeness of synthetic images by Olah et al. (2017) with a simple
baseline visualization, namely exemplary natural images that also strongly
activate a specific feature map. Given either synthetic or natural reference
images, human participants choose which of two query images leads to strong
positive activation. The experiments are designed to maximize participants'
performance, and are the first to probe intermediate instead of final layer
representations. We find that synthetic images indeed provide helpful
information about feature map activations ($82\pm4\%$ accuracy; chance would be
$50\%$). However, natural images - originally intended as a baseline -
outperform synthetic images by a wide margin ($92\pm2\%$). Additionally,
participants are faster and more confident for natural images, whereas
subjective impressions about the interpretability of the feature visualizations
are mixed. The higher informativeness of natural images holds across most
layers, for both expert and lay participants as well as for hand- and
randomly-picked feature visualizations. Even if only a single reference image
is given, synthetic images provide less information than natural images
($65\pm5\%$ vs. $73\pm4\%$). In summary, synthetic images from a popular
feature visualization method are significantly less informative for assessing
CNN activations than natural images. We argue that visualization methods should
improve over this baseline. | [
"cs.CV",
"cs.AI",
"cs.HC",
"cs.LG"
]
|
Semi-supervised learning has recently been attracting attention as an
alternative to fully supervised models that require large pools of labeled
data. Moreover, optimizing a model for multiple tasks can provide better
generalizability than single-task learning. Leveraging self-supervision and
adversarial training, we propose a novel general purpose semi-supervised,
multiple-task model---namely, self-supervised, semi-supervised, multitask
learning (S$^4$MTL)---for accomplishing two important tasks in medical imaging,
segmentation and diagnostic classification. Experimental results on chest and
spine X-ray datasets suggest that our S$^4$MTL model significantly outperforms
semi-supervised single task, semi/fully-supervised multitask, and
fully-supervised single task models, even with a 50\% reduction of class and
segmentation labels. We hypothesize that our proposed model can be effective in
tackling limited annotation problems for joint training, not only in medical
imaging domains, but also for general-purpose vision tasks. | [
"cs.CV"
]
|
Continual (sequential) training and multitask (simultaneous) training are
often attempting to solve the same overall objective: to find a solution that
performs well on all considered tasks. The main difference is in the training
regimes, where continual learning can only have access to one task at a time,
which for neural networks typically leads to catastrophic forgetting. That is,
the solution found for a subsequent task does not perform well on the previous
ones anymore. However, the relationship between the different minima that the
two training regimes arrive at is not well understood. What sets them apart? Is
there a local structure that could explain the difference in performance
achieved by the two different schemes? Motivated by recent work showing that
different minima of the same task are typically connected by very simple curves
of low error, we investigate whether multitask and continual solutions are
similarly connected. We empirically find that indeed such connectivity can be
reliably achieved and, more interestingly, it can be done by a linear path,
conditioned on having the same initialization for both. We thoroughly analyze
this observation and discuss its significance for the continual learning
process. Furthermore, we exploit this finding to propose an effective algorithm
that constrains the sequentially learned minima to behave as the multitask
solution. We show that our method outperforms several state of the art
continual learning algorithms on various vision benchmarks. | [
"cs.LG",
"cs.AI",
"cs.CV"
]
|
Compositional generalization is the ability to generalize systematically to a
new data distribution by combining known components. Although humans seem to
have a great ability to generalize compositionally, state-of-the-art neural
models struggle to do so. In this work, we study compositional generalization
in classification tasks and present two main contributions. First, we study
ways to convert a natural language sequence-to-sequence dataset to a
classification dataset that also requires compositional generalization. Second,
we show that providing structural hints (specifically, providing parse trees
and entity links as attention masks for a Transformer model) helps
compositional generalization. | [
"cs.LG",
"cs.CL"
]
|
In this work, we present a memory-augmented approach for image-goal
navigation. Earlier attempts, including RL-based and SLAM-based approaches have
either shown poor generalization performance, or are heavily-reliant on
pose/depth sensors. Our method uses an attention-based end-to-end model that
leverages an episodic memory to learn to navigate. First, we train a
state-embedding network in a self-supervised fashion, and then use it to embed
previously-visited states into the agent's memory. Our navigation policy takes
advantage of this information through an attention mechanism. We validate our
approach with extensive evaluations, and show that our model establishes a new
state of the art on the challenging Gibson dataset. Furthermore, we achieve
this impressive performance from RGB input alone, without access to additional
information such as position or depth, in stark contrast to related work. | [
"cs.CV"
]
|
Real estate contributes significantly to all major economies around the
world. In particular, house prices have a direct impact on stakeholders,
ranging from house buyers to financing companies. Thus, a plethora of
techniques have been developed for real estate price prediction. Most of the
existing techniques rely on different house features to build a variety of
prediction models to predict house prices. Perceiving the effect of spatial
dependence on house prices, some later works focused on introducing spatial
regression models for improving prediction performance. However, they fail to
take into account the geo-spatial context of the neighborhood amenities such as
how close a house is to a train station, or a highly-ranked school, or a
shopping center. Such contextual information may play a vital role in users'
interests in a house and thereby has a direct influence on its price. In this
paper, we propose to leverage the concept of graph neural networks to capture
the geo-spatial context of the neighborhood of a house. In particular, we
present a novel method, the Geo-Spatial Network Embedding (GSNE), that learns
the embeddings of houses and various types of Points of Interest (POIs) in the
form of multipartite networks, where the houses and the POIs are represented as
attributed nodes and the relationships between them as edges. Extensive
experiments with a large number of regression techniques show that the
embeddings produced by our proposed GSNE technique consistently and
significantly improve the performance of the house price prediction task
regardless of the downstream regression model. | [
"cs.LG",
"stat.ML"
]
|
Attention mechanisms, which enable a neural network to accurately focus on
all the relevant elements of the input, have become an essential component to
improve the performance of deep neural networks. There are mainly two attention
mechanisms widely used in computer vision studies, \textit{spatial attention}
and \textit{channel attention}, which aim to capture the pixel-level pairwise
relationship and channel dependency, respectively. Although fusing them
together may achieve better performance than their individual implementations,
it will inevitably increase the computational overhead. In this paper, we
propose an efficient Shuffle Attention (SA) module to address this issue, which
adopts Shuffle Units to combine two types of attention mechanisms effectively.
Specifically, SA first groups channel dimensions into multiple sub-features
before processing them in parallel. Then, for each sub-feature, SA utilizes a
Shuffle Unit to depict feature dependencies in both spatial and channel
dimensions. After that, all sub-features are aggregated and a "channel shuffle"
operator is adopted to enable information communication between different
sub-features. The proposed SA module is efficient yet effective, e.g., the
parameters and computations of SA against the backbone ResNet50 are 300 vs.
25.56M and 2.76e-3 GFLOPs vs. 4.12 GFLOPs, respectively, and the performance
boost is more than 1.34% in terms of Top-1 accuracy. Extensive experimental
results on common-used benchmarks, including ImageNet-1k for classification, MS
COCO for object detection, and instance segmentation, demonstrate that the
proposed SA outperforms the current SOTA methods significantly by achieving
higher accuracy while having lower model complexity. The code and models are
available at https://github.com/wofmanaf/SA-Net. | [
"cs.CV",
"cs.AI"
]
|
Recognition of Arabic characters is essential for natural language processing
and computer vision fields. The need to recognize and classify the handwritten
Arabic letters and characters are essentially required. In this paper, we
present an algorithm for recognizing Arabic letters and characters based on
using deep convolution neural networks (DCNN) and support vector machine (SVM).
This paper addresses the problem of recognizing the Arabic handwritten
characters by determining the similarity between the input templates and the
pre-stored templates using both fully connected DCNN and dropout SVM.
Furthermore, this paper determines the correct classification rate (CRR)
depends on the accuracy of the corrected classified templates, of the
recognized handwritten Arabic characters. Moreover, we determine the error
classification rate (ECR). The experimental results of this work indicate the
ability of the proposed algorithm to recognize, identify, and verify the input
handwritten Arabic characters. Furthermore, the proposed system determines
similar Arabic characters using a clustering algorithm based on the K-means
clustering approach to handle the problem of multi-stroke in Arabic characters.
The comparative evaluation is stated and the system accuracy reached 95.07% CRR
with 4.93% ECR compared with the state of the art. | [
"cs.CV"
]
|
We tackle a common scenario in imitation learning (IL), where agents try to
recover the optimal policy from expert demonstrations without further access to
the expert or environment reward signals. Except the simple Behavior Cloning
(BC) that adopts supervised learning followed by the problem of compounding
error, previous solutions like inverse reinforcement learning (IRL) and recent
generative adversarial methods involve a bi-level or alternating optimization
for updating the reward function and the policy, suffering from high
computational cost and training instability. Inspired by recent progress in
energy-based model (EBM), in this paper, we propose a simplified IL framework
named Energy-Based Imitation Learning (EBIL). Instead of updating the reward
and policy iteratively, EBIL breaks out of the traditional IRL paradigm by a
simple and flexible two-stage solution: first estimating the expert energy as
the surrogate reward function through score matching, then utilizing such a
reward for learning the policy by reinforcement learning algorithms. EBIL
combines the idea of both EBM and occupancy measure matching, and via theoretic
analysis we reveal that EBIL and Max-Entropy IRL (MaxEnt IRL) approaches are
two sides of the same coin, and thus EBIL could be an alternative of
adversarial IRL methods. Extensive experiments on qualitative and quantitative
evaluations indicate that EBIL is able to recover meaningful and interpretative
reward signals while achieving effective and comparable performance against
existing algorithms on IL benchmarks. | [
"cs.LG",
"stat.ML"
]
|
Inspired by the well-known permutation entropy (PE), an effective image
encoding scheme for chaotic time series, Triad State Space Construction (TSSC),
is proposed. The TSSC image can recognize higher-order temporal patterns and
identify new forbidden regions in time series motifs beyond the Bandt-Pompe
probabilities. The Convolutional Neural Network (ConvNet) is widely used in
image classification. The ConvNet classifier based on TSSC images
(TSSC-ConvNet) are highly accurate and very robust in the chaotic signal
classification. | [
"cs.LG",
"nlin.CD",
"physics.data-an",
"stat.ML"
]
|
We propose a new algorithm for training generative adversarial networks that
jointly learns latent codes for both identities (e.g. individual humans) and
observations (e.g. specific photographs). By fixing the identity portion of the
latent codes, we can generate diverse images of the same subject, and by fixing
the observation portion, we can traverse the manifold of subjects while
maintaining contingent aspects such as lighting and pose. Our algorithm
features a pairwise training scheme in which each sample from the generator
consists of two images with a common identity code. Corresponding samples from
the real dataset consist of two distinct photographs of the same subject. In
order to fool the discriminator, the generator must produce pairs that are
photorealistic, distinct, and appear to depict the same individual. We augment
both the DCGAN and BEGAN approaches with Siamese discriminators to facilitate
pairwise training. Experiments with human judges and an off-the-shelf face
verification system demonstrate our algorithm's ability to generate convincing,
identity-matched photographs. | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.NE",
"stat.ML"
]
|
Multiple Kernel Learning is a conventional way to learn the kernel function
in kernel-based methods. MKL algorithms enhance the performance of kernel
methods. However, these methods have a lower complexity compared to deep
learning models and are inferior to these models in terms of recognition
accuracy. Deep learning models can learn complex functions by applying
nonlinear transformations to data through several layers. In this paper, we
show that a typical MKL algorithm can be interpreted as a one-layer neural
network with linear activation functions. By this interpretation, we propose a
Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the
conventional multiple kernel learning framework to a multi-layer neural network
with nonlinear activation functions. Our experiments on several benchmarks show
that the proposed method improves the complexity of MKL algorithms and leads to
higher recognition accuracy. | [
"cs.LG"
]
|
Quantizing deep convolutional neural networks for image super-resolution
substantially reduces their computational costs. However, existing works either
suffer from a severe performance drop in ultra-low precision of 4 or lower
bit-widths, or require a heavy fine-tuning process to recover the performance.
To our knowledge, this vulnerability to low precisions relies on two
statistical observations of feature map values. First, distribution of feature
map values varies significantly per channel and per input image. Second,
feature maps have outliers that can dominate the quantization error. Based on
these observations, we propose a novel distribution-aware quantization scheme
(DAQ) which facilitates accurate training-free quantization in ultra-low
precision. A simple function of DAQ determines dynamic range of feature maps
and weights with low computational burden. Furthermore, our method enables
mixed-precision quantization by calculating the relative sensitivity of each
channel, without any training process involved. Nonetheless, quantization-aware
training is also applicable for auxiliary performance gain. Our new method
outperforms recent training-free and even training-based quantization methods
to the state-of-the-art image super-resolution networks in ultra-low precision. | [
"cs.CV",
"eess.IV"
]
|
Embedding large and high dimensional data into low dimensional vector spaces
is a necessary task to computationally cope with contemporary data sets.
Superseding latent semantic analysis recent approaches like word2vec or
node2vec are well established tools in this realm. In the present paper we add
to this line of research by introducing fca2vec, a family of embedding
techniques for formal concept analysis (FCA). Our investigation contributes to
two distinct lines of research. First, we enable the application of FCA notions
to large data sets. In particular, we demonstrate how the cover relation of a
concept lattice can be retrieved from a computational feasible embedding.
Secondly, we show an enhancement for the classical node2vec approach in low
dimension. For both directions the overall constraint of FCA of explainable
results is preserved. We evaluate our novel procedures by computing fca2vec on
different data sets like, wiki44 (a dense part of the Wikidata knowledge
graph), the Mushroom data set and a publication network derived from the FCA
community. | [
"cs.LG",
"cs.AI",
"stat.ML",
"68T30, 03G10, 06A06"
]
|
Edge detection is a classic problem in the field of image processing, which
lays foundations for other tasks such as image segmentation. Conventionally,
this operation is performed using gradient operators such as the Roberts or
Sobel operator, which can discover local changes in intensity levels. These
operators, however, perform poorly on low contrast images. In this paper, we
propose an edge detector architecture for color images based on fuzzy theory
and the Sobel operator. First, the R, G and B channels are extracted from an
image and enhanced using fuzzy methods, in order to suppress noise and improve
the contrast between the background and the objects. The Sobel operator is then
applied to each of the channels, which are finally combined into an edge map of
the origin image. Experimental results obtained through an FPGA-based
implementation have proved the proposed method effective. | [
"cs.CV"
]
|
Background: The deployment of various networks (e.g., Internet of Things
(IoT) and mobile networks) and databases (e.g., nutrition tables and food
compositional databases) in the food system generates massive information silos
due to the well-known data harmonization problem. The food knowledge graph
provides a unified and standardized conceptual terminology and their
relationships in a structured form and thus can transform these information
silos across the whole food system to a more reusable globally digitally
connected Internet of Food, enabling every stage of the food system from
farm-to-fork.
Scope and approach: We review the evolution of food knowledge organization,
from food classification, food ontology to food knowledge graphs. We then
discuss the progress in food knowledge graphs from several representative
applications. We finally discuss the main challenges and future directions.
Key findings and conclusions: Our comprehensive summary of current research
on food knowledge graphs shows that food knowledge graphs play an important
role in food-oriented applications, including food search and Question
Answering (QA), personalized dietary recommendation, food analysis and
visualization, food traceability, and food machinery intelligent manufacturing.
Future directions for food knowledge graphs cover several fields such as
multimodal food knowledge graphs and food intelligence. | [
"cs.CV"
]
|
Graph topology inference of network processes with co-evolving and
interacting time-series is crucial for network studies. Vector autoregressive
models (VAR) are popular approaches for topology inference of directed graphs;
however, in large networks with short time-series, topology estimation becomes
ill-posed. The present paper proposes a novel nonlinearity-preserving topology
inference method for directed networks with co-evolving nodal processes that
solves the ill-posedness problem. The proposed method, large-scale kernelized
Granger causality (lsKGC), uses kernel functions to transform data into a
low-dimensional feature space and solves the autoregressive problem in the
feature space, then finds the pre-images in the input space to infer the
topology. Extensive simulations on synthetic datasets with nonlinear and linear
dependencies and known ground-truth demonstrate significant improvement in the
Area Under the receiver operating characteristic Curve ( AUC ) of the receiver
operating characteristic for network recovery compared to existing methods.
Furthermore, tests on real datasets from a functional magnetic resonance
imaging (fMRI) study demonstrate 96.3 percent accuracy in diagnosis tasks of
schizophrenia patients, which is the highest in the literature with only brain
time-series information. | [
"cs.LG",
"cs.CV"
]
|
In recent studies on model-based reinforcement learning (MBRL), incorporating
uncertainty in forward dynamics is a state-of-the-art strategy to enhance
learning performance, making MBRLs competitive to cutting-edge model free
methods, especially in simulated robotics tasks. Probabilistic ensembles with
trajectory sampling (PETS) is a leading type of MBRL, which employs Bayesian
inference to dynamics modeling and model predictive control (MPC) with
stochastic optimization via the cross entropy method (CEM). In this paper, we
propose a novel extension to the uncertainty-aware MBRL. Our main contributions
are twofold: Firstly, we introduce a variational inference MPC, which
reformulates various stochastic methods, including CEM, in a Bayesian fashion.
Secondly, we propose a novel instance of the framework, called probabilistic
action ensembles with trajectory sampling (PaETS). As a result, our Bayesian
MBRL can involve multimodal uncertainties both in dynamics and optimal
trajectories. In comparison to PETS, our method consistently improves
asymptotic performance on several challenging locomotion tasks. | [
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
]
|
Centralised training with decentralised execution is an important setting for
cooperative deep multi-agent reinforcement learning due to communication
constraints during execution and computational tractability in training. In
this paper, we analyse value-based methods that are known to have superior
performance in complex environments [43]. We specifically focus on QMIX [40],
the current state-of-the-art in this domain. We show that the representational
constraints on the joint action-values introduced by QMIX and similar methods
lead to provably poor exploration and suboptimality. Furthermore, we propose a
novel approach called MAVEN that hybridises value and policy-based methods by
introducing a latent space for hierarchical control. The value-based agents
condition their behaviour on the shared latent variable controlled by a
hierarchical policy. This allows MAVEN to achieve committed, temporally
extended exploration, which is key to solving complex multi-agent tasks. Our
experimental results show that MAVEN achieves significant performance
improvements on the challenging SMAC domain [43]. | [
"cs.LG",
"stat.ML"
]
|
Clustering is widely used in unsupervised learning method that deals with
unlabeled data. Deep clustering has become a popular study area that relates
clustering with Deep Neural Network (DNN) architecture. Deep clustering method
downsamples high dimensional data, which may also relate clustering loss. Deep
clustering is also introduced in semi-supervised learning (SSL). Most SSL
methods depend on pairwise constraint information, which is a matrix containing
knowledge if data pairs can be in the same cluster or not. This paper
introduces a novel embedding system named AutoEmbedder, that downsamples higher
dimensional data to clusterable embedding points. To the best of our knowledge,
this is the first research endeavor that relates to traditional classifier DNN
architecture with a pairwise loss reduction technique. The training process is
semi-supervised and uses Siamese network architecture to compute pairwise
constraint loss in the feature learning phase. The AutoEmbedder outperforms
most of the existing DNN based semi-supervised methods tested on famous
datasets. | [
"cs.LG",
"cs.CV",
"stat.ML"
]
|
Arbitrary-oriented objects widely appear in natural scenes, aerial
photographs, remote sensing images, etc., thus arbitrary-oriented object
detection has received considerable attention. Many current rotation detectors
use plenty of anchors with different orientations to achieve spatial alignment
with ground truth boxes, then Intersection-over-Union (IoU) is applied to
sample the positive and negative candidates for training. However, we observe
that the selected positive anchors cannot always ensure accurate detections
after regression, while some negative samples can achieve accurate
localization. It indicates that the quality assessment of anchors through IoU
is not appropriate, and this further lead to inconsistency between
classification confidence and localization accuracy. In this paper, we propose
a dynamic anchor learning (DAL) method, which utilizes the newly defined
matching degree to comprehensively evaluate the localization potential of the
anchors and carry out a more efficient label assignment process. In this way,
the detector can dynamically select high-quality anchors to achieve accurate
object detection, and the divergence between classification and regression will
be alleviated. With the newly introduced DAL, we achieve superior detection
performance for arbitrary-oriented objects with only a few horizontal preset
anchors. Experimental results on three remote sensing datasets HRSC2016, DOTA,
UCAS-AOD as well as a scene text dataset ICDAR 2015 show that our method
achieves substantial improvement compared with the baseline model. Besides, our
approach is also universal for object detection using horizontal bound box. The
code and models are available at https://github.com/ming71/DAL. | [
"cs.CV"
]
|
We introduce Imagination-Augmented Agents (I2As), a novel architecture for
deep reinforcement learning combining model-free and model-based aspects. In
contrast to most existing model-based reinforcement learning and planning
methods, which prescribe how a model should be used to arrive at a policy, I2As
learn to interpret predictions from a learned environment model to construct
implicit plans in arbitrary ways, by using the predictions as additional
context in deep policy networks. I2As show improved data efficiency,
performance, and robustness to model misspecification compared to several
baselines. | [
"cs.LG",
"cs.AI",
"stat.ML"
]
|
Weakly-Supervised Temporal Action Localization (WSTAL) aims to localize
actions in untrimmed videos with only video-level labels. Currently, most
state-of-the-art WSTAL methods follow a Multi-Instance Learning (MIL) pipeline:
producing snippet-level predictions first and then aggregating to the
video-level prediction. However, we argue that existing methods have overlooked
two important drawbacks: 1) inadequate use of motion information and 2) the
incompatibility of prevailing cross-entropy training loss. In this paper, we
analyze that the motion cues behind the optical flow features are complementary
informative. Inspired by this, we propose to build a context-dependent motion
prior, termed as motionness. Specifically, a motion graph is introduced to
model motionness based on the local motion carrier (e.g., optical flow). In
addition, to highlight more informative video snippets, a motion-guided loss is
proposed to modulate the network training conditioned on motionness scores.
Extensive ablation studies confirm that motionness efficaciously models
action-of-interest, and the motion-guided loss leads to more accurate results.
Besides, our motion-guided loss is a plug-and-play loss function and is
applicable with existing WSTAL methods. Without loss of generality, based on
the standard MIL pipeline, our method achieves new state-of-the-art performance
on three challenging benchmarks, including THUMOS'14, ActivityNet v1.2 and
v1.3. | [
"cs.CV"
]
|
Reinforcement Learning (RL) has recently been applied to sequential
estimation and prediction problems identifying and developing hypothetical
treatment strategies for septic patients, with a particular focus on offline
learning with observational data. In practice, successful RL relies on
informative latent states derived from sequential observations to develop
optimal treatment strategies. To date, how best to construct such states in a
healthcare setting is an open question. In this paper, we perform an empirical
study of several information encoding architectures using data from septic
patients in the MIMIC-III dataset to form representations of a patient state.
We evaluate the impact of representation dimension, correlations with
established acuity scores, and the treatment policies derived from them. We
find that sequentially formed state representations facilitate effective policy
learning in batch settings, validating a more thoughtful approach to
representation learning that remains faithful to the sequential and partial
nature of healthcare data. | [
"cs.LG"
]
|
We present Spectral Inference Networks, a framework for learning
eigenfunctions of linear operators by stochastic optimization. Spectral
Inference Networks generalize Slow Feature Analysis to generic symmetric
operators, and are closely related to Variational Monte Carlo methods from
computational physics. As such, they can be a powerful tool for unsupervised
representation learning from video or graph-structured data. We cast training
Spectral Inference Networks as a bilevel optimization problem, which allows for
online learning of multiple eigenfunctions. We show results of training
Spectral Inference Networks on problems in quantum mechanics and feature
learning for videos on synthetic datasets. Our results demonstrate that
Spectral Inference Networks accurately recover eigenfunctions of linear
operators and can discover interpretable representations from video in a fully
unsupervised manner. | [
"cs.LG",
"cs.AI",
"stat.ML"
]
|
The transition from today's mostly human-driven traffic to a purely automated
one will be a gradual evolution, with the effect that we will likely experience
mixed traffic in the near future. Connected and automated vehicles can benefit
human-driven ones and the whole traffic system in different ways, for example
by improving collision avoidance and reducing traffic waves. Many studies have
been carried out to improve intersection management, a significant bottleneck
in traffic, with intelligent traffic signals or exclusively automated vehicles.
However, the problem of how to improve mixed traffic at unsignalized
intersections has received less attention. In this paper, we propose a novel
approach to optimizing traffic flow at intersections in mixed traffic
situations using deep reinforcement learning. Our reinforcement learning agent
learns a policy for a centralized controller to let connected autonomous
vehicles at unsignalized intersections give up their right of way and yield to
other vehicles to optimize traffic flow. We implemented our approach and tested
it in the traffic simulator SUMO based on simulated and real traffic data. The
experimental evaluation demonstrates that our method significantly improves
traffic flow through unsignalized intersections in mixed traffic settings and
also provides better performance on a wide range of traffic situations compared
to the state-of-the-art traffic signal controller for the corresponding
signalized intersection. | [
"cs.LG",
"cs.RO"
]
|
Decision trees provide a rich family of highly non-linear but efficient
models, due to which they continue to be the go-to family of predictive models
by practitioners across domains. But learning trees is a challenging problem
due to their highly discrete and non-differentiable decision boundaries. The
state-of-the-art techniques use greedy methods that exploit the discrete tree
structure but are tailored to specific problem settings (say, categorical vs
real-valued predictions). In this work, we propose a reformulation of the tree
learning problem that provides better conditioned gradients, and leverages
successful deep network learning techniques like overparameterization and
straight-through estimators. Our reformulation admits an efficient and {\em
accurate} gradient-based algorithm that allows us to deploy our solution in
disparate tree learning settings like supervised batch learning and online
bandit feedback based learning.
Using extensive validation on standard benchmarks, we observe that in the
supervised learning setting, our general method is competitive to, and in some
cases more accurate than, existing methods that are designed {\em specifically}
for the supervised settings. In contrast, for bandit settings, where most of
the existing techniques are not applicable, our models are still accurate and
significantly outperform the applicable state-of-the-art methods. | [
"cs.LG"
]
|
The prediction of future climate scenarios under anthropogenic forcing is
critical to understand climate change and to assess the impact of potentially
counter-acting technologies. Machine learning and hybrid techniques for this
prediction rely on informative metrics that are sensitive to pertinent but
often subtle influences. For atmospheric dynamics, a critical part of the
climate system, the "eyeball metric", i.e. a visual inspection by an expert, is
currently still the gold standard. However, it cannot be used as metric in
machine learning systems where an algorithmic description is required.
Motivated by the success of intermediate neural network activations as basis
for learned metrics, e.g. in computer vision, we present a novel,
self-supervised representation learning approach specifically designed for
atmospheric dynamics. Our approach, called AtmoDist, trains a neural network on
a simple, auxiliary task: predicting the temporal distance between elements of
a shuffled sequence of atmospheric fields (e.g. the components of the wind
field from a reanalysis or simulation). The task forces the network to learn
important intrinsic aspects of the data as activations in its layers and from
these hence a discriminative metric can be obtained. We demonstrate this by
using AtmoDist to define a metric for GAN-based super resolution of vorticity
and divergence. Our upscaled data matches closely the true statistics of a high
resolution reference and it significantly outperform the state-of-the-art based
on mean squared error. Since AtmoDist is unsupervised, only requires a temporal
sequence of fields, and uses a simple auxiliary task, it can be used in a wide
range of applications that aim to understand and mitigate climate change. | [
"cs.LG",
"physics.ao-ph",
"I.2.6; J.2"
]
|
Transfer of pre-trained representations improves sample efficiency and
simplifies hyperparameter tuning when training deep neural networks for vision.
We revisit the paradigm of pre-training on large supervised datasets and
fine-tuning the model on a target task. We scale up pre-training, and propose a
simple recipe that we call Big Transfer (BiT). By combining a few carefully
selected components, and transferring using a simple heuristic, we achieve
strong performance on over 20 datasets. BiT performs well across a surprisingly
wide range of data regimes -- from 1 example per class to 1M total examples.
BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3%
on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT
attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10
with 10 examples per class. We conduct detailed analysis of the main components
that lead to high transfer performance. | [
"cs.CV",
"cs.LG"
]
|
The potential number of drug like small molecules is estimated to be between
10^23 and 10^60 while current databases of known compounds are orders of
magnitude smaller with approximately 10^8 compounds. This discrepancy has led
to an interest in generating virtual libraries using hand crafted chemical
rules and fragment based methods to cover a larger area of chemical space and
generate chemical libraries for use in in silico drug discovery endeavors. Here
it is explored to what extent a recurrent neural network with long short term
memory cells can figure out sensible chemical rules and generate synthesizable
molecules by being trained on existing compounds encoded as SMILES. The
networks can to a high extent generate novel, but chemically sensible
molecules. The properties of the molecules are tuned by training on two
different datasets consisting of fragment like molecules and drug like
molecules. The produced molecules and the training databases have very similar
distributions of molar weight, predicted logP, number of hydrogen bond
acceptors and donors, number of rotatable bonds and topological polar surface
area when compared to their respective training sets. The compounds are for the
most cases synthesizable as assessed with SA score and Wiley ChemPlanner. | [
"cs.LG",
"q-bio.BM"
]
|
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI
through machine learning have been a subject of intense research in recent
years. Recent success of deep learning in computer vision have progressed such
research further. However, common limitations with such algorithms are reliance
on a large number of training images, and requirement of careful optimization
of the architecture of deep networks. In this paper, we attempt solving these
issues with transfer learning, where state-of-the-art architectures such as VGG
and Inception are initialized with pre-trained weights from large benchmark
datasets consisting of natural images, and the fully-connected layer is
re-trained with only a small number of MRI images. We employ image entropy to
select the most informative slices for training. Through experimentation on the
OASIS MRI dataset, we show that with training size almost 10 times smaller than
the state-of-the-art, we reach comparable or even better performance than
current deep-learning based methods. | [
"cs.CV"
]
|
A safe and robust on-road navigation system is a crucial component of
achieving fully automated vehicles. NVIDIA recently proposed an End-to-End
algorithm that can directly learn steering commands from raw pixels of a front
camera by using one convolutional neural network. In this paper, we leverage
auxiliary information aside from raw images and design a novel network
structure, called Auxiliary Task Network (ATN), to help boost the driving
performance while maintaining the advantage of minimal training data and an
End-to-End training method. In this network, we introduce human prior knowledge
into vehicle navigation by transferring features from image recognition tasks.
Image semantic segmentation is applied as an auxiliary task for navigation. We
consider temporal information by introducing an LSTM module and optical flow to
the network. Finally, we combine vehicle kinematics with a sensor fusion step.
We discuss the benefits of our method over state-of-the-art visual navigation
methods both in the Udacity simulation environment and on the real-world
Comma.ai dataset. | [
"cs.CV"
]
|
Descriptive region features extracted by object detection networks have
played an important role in the recent advancements of image captioning.
However, they are still criticized for the lack of contextual information and
fine-grained details, which in contrast are the merits of traditional grid
features. In this paper, we introduce a novel Dual-Level Collaborative
Transformer (DLCT) network to realize the complementary advantages of the two
features. Concretely, in DLCT, these two features are first processed by a
novelDual-way Self Attenion (DWSA) to mine their intrinsic properties, where a
Comprehensive Relation Attention component is also introduced to embed the
geometric information. In addition, we propose a Locality-Constrained Cross
Attention module to address the semantic noises caused by the direct fusion of
these two features, where a geometric alignment graph is constructed to
accurately align and reinforce region and grid features. To validate our model,
we conduct extensive experiments on the highly competitive MS-COCO dataset, and
achieve new state-of-the-art performance on both local and online test sets,
i.e., 133.8% CIDEr-D on Karpathy split and 135.4% CIDEr on the official split.
Code is available at https://github.com/luo3300612/image-captioning-DLCT. | [
"cs.CV"
]
|
This paper proposes a novel approach to create an automated visual
surveillance system which is very efficient in detecting and tracking moving
objects in a video captured by moving camera without any apriori information
about the captured scene. Separating foreground from the background is
challenging job in videos captured by moving camera as both foreground and
background information change in every consecutive frames of the image
sequence; thus a pseudo-motion is perceptive in background. In the proposed
algorithm, the pseudo-motion in background is estimated and compensated using
phase correlation of consecutive frames based on the principle of Fourier shift
theorem. Then a method is proposed to model an acting background from recent
history of commonality of the current frame and the foreground is detected by
the differences between the background model and the current frame. Further
exploiting the recent history of dissimilarities of the current frame, actual
moving objects are detected in the foreground. Next, a two-stepped
morphological operation is proposed to refine the object region for an optimum
object size. Each object is attributed by its centroid, dimension and three
highest peaks of its gray value histogram. Finally, each object is tracked
using Kalman filter based on its attributes. The major advantage of this
algorithm over most of the existing object detection and tracking algorithms is
that, it does not require initialization of object position in the first frame
or training on sample data to perform. Performance of the algorithm is tested
on benchmark videos containing variable background and very satisfiable results
is achieved. The performance of the algorithm is also comparable with some of
the state-of-the-art algorithms for object detection and tracking. | [
"cs.CV"
]
|
Data augmentation methods are indispensable heuristics to boost the
performance of deep neural networks, especially in image recognition tasks.
Recently, several studies have shown that augmentation strategies found by
search algorithms outperform hand-made strategies. Such methods employ
black-box search algorithms over image transformations with continuous or
discrete parameters and require a long time to obtain better strategies. In
this paper, we propose a differentiable policy search pipeline for data
augmentation, which is much faster than previous methods. We introduce
approximate gradients for several transformation operations with discrete
parameters as well as the differentiable mechanism for selecting operations. As
the objective of training, we minimize the distance between the distributions
of augmented data and the original data, which can be differentiated. We show
that our method, Faster AutoAugment, achieves significantly faster searching
than prior work without a performance drop. | [
"cs.CV"
]
|
The problem of distributed representation learning is one in which multiple
sources of information $X_1,\ldots,X_K$ are processed separately so as to learn
as much information as possible about some ground truth $Y$. We investigate
this problem from information-theoretic grounds, through a generalization of
Tishby's centralized Information Bottleneck (IB) method to the distributed
setting. Specifically, $K$ encoders, $K \geq 2$, compress their observations
$X_1,\ldots,X_K$ separately in a manner such that, collectively, the produced
representations preserve as much information as possible about $Y$. We study
both discrete memoryless (DM) and memoryless vector Gaussian data models. For
the discrete model, we establish a single-letter characterization of the
optimal tradeoff between complexity (or rate) and relevance (or information)
for a class of memoryless sources (the observations $X_1,\ldots,X_K$ being
conditionally independent given $Y$). For the vector Gaussian model, we provide
an explicit characterization of the optimal complexity-relevance tradeoff.
Furthermore, we develop a variational bound on the complexity-relevance
tradeoff which generalizes the evidence lower bound (ELBO) to the distributed
setting. We also provide two algorithms that allow to compute this bound: i) a
Blahut-Arimoto type iterative algorithm which enables to compute optimal
complexity-relevance encoding mappings by iterating over a set of
self-consistent equations, and ii) a variational inference type algorithm in
which the encoding mappings are parametrized by neural networks and the bound
approximated by Markov sampling and optimized with stochastic gradient descent.
Numerical results on synthetic and real datasets are provided to support the
efficiency of the approaches and algorithms developed in this paper. | [
"stat.ML",
"cs.LG"
]
|
Adversarial regularization has been shown to improve the generalization
performance of deep learning models in various natural language processing
tasks. Existing works usually formulate the method as a zero-sum game, which is
solved by alternating gradient descent/ascent algorithms. Such a formulation
treats the adversarial and the defending players equally, which is undesirable
because only the defending player contributes to the generalization
performance. To address this issue, we propose Stackelberg Adversarial
Regularization (SALT), which formulates adversarial regularization as a
Stackelberg game. This formulation induces a competition between a leader and a
follower, where the follower generates perturbations, and the leader trains the
model subject to the perturbations. Different from conventional approaches, in
SALT, the leader is in an advantageous position. When the leader moves, it
recognizes the strategy of the follower and takes the anticipated follower's
outcomes into consideration. Such a leader's advantage enables us to improve
the model fitting to the unperturbed data. The leader's strategic information
is captured by the Stackelberg gradient, which is obtained using an unrolling
algorithm. Our experimental results on a set of machine translation and natural
language understanding tasks show that SALT outperforms existing adversarial
regularization baselines across all tasks. Our code is publicly available. | [
"cs.LG",
"cs.CL"
]
|
Unsupervised multi-object representation learning depends on inductive biases
to guide the discovery of object-centric representations that generalize.
However, we observe that methods for learning these representations are either
impractical due to long training times and large memory consumption or forego
key inductive biases. In this work, we introduce EfficientMORL, an efficient
framework for the unsupervised learning of object-centric representations. We
show that optimization challenges caused by requiring both symmetry and
disentanglement can in fact be addressed by high-cost iterative amortized
inference by designing the framework to minimize its dependence on it. We take
a two-stage approach to inference: first, a hierarchical variational
autoencoder extracts symmetric and disentangled representations through
bottom-up inference, and second, a lightweight network refines the
representations with top-down feedback. The number of refinement steps taken
during training is reduced following a curriculum, so that at test time with
zero steps the model achieves 99.1% of the refined decomposition performance.
We demonstrate strong object decomposition and disentanglement on the standard
multi-object benchmark while achieving nearly an order of magnitude faster
training and test time inference over the previous state-of-the-art model. | [
"cs.CV",
"cs.AI",
"cs.LG"
]
|
Over the past few years, several new methods for scene text recognition have
been proposed. Most of these methods propose novel building blocks for neural
networks. These novel building blocks are specially tailored for the task of
scene text recognition and can thus hardly be used in any other tasks. In this
paper, we introduce a new model for scene text recognition that only consists
of off-the-shelf building blocks for neural networks. Our model (KISS) consists
of two ResNet based feature extractors, a spatial transformer, and a
transformer. We train our model only on publicly available, synthetic training
data and evaluate it on a range of scene text recognition benchmarks, where we
reach state-of-the-art or competitive performance, although our model does not
use methods like 2D-attention, or image rectification. | [
"cs.CV"
]
|
We introduce TIDE, a framework and associated toolbox for analyzing the
sources of error in object detection and instance segmentation algorithms.
Importantly, our framework is applicable across datasets and can be applied
directly to output prediction files without required knowledge of the
underlying prediction system. Thus, our framework can be used as a drop-in
replacement for the standard mAP computation while providing a comprehensive
analysis of each model's strengths and weaknesses. We segment errors into six
types and, crucially, are the first to introduce a technique for measuring the
contribution of each error in a way that isolates its effect on overall
performance. We show that such a representation is critical for drawing
accurate, comprehensive conclusions through in-depth analysis across 4 datasets
and 7 recognition models. Available at https://dbolya.github.io/tide/ | [
"cs.CV"
]
|
Color-coded aperture (CCA) methods can physically measure the depth of a
scene given by physical cues from a single-shot image of a monocular camera.
However, they are vulnerable to actual lens aberrations in real scenes because
they assume an ideal lens for simplifying algorithms. In this paper, we propose
physical cue-based deep learning for CCA photography. To address actual lens
aberrations, we developed a deep deaberration network (DDN) that is
additionally equipped with a self-attention mechanism of position and color
channels to efficiently learn the lens aberration. Furthermore, a new Bayes L1
loss function based on Bayesian deep learning enables to handle the uncertainty
of depth estimation more accurately. Quantitative and qualitative comparisons
demonstrate that our method is superior to conventional methods including real
outdoor scenes. Furthermore, compared to a long-baseline stereo camera, the
proposed method provides an error-free depth map at close range, as there is no
blind spot between the left and right cameras. | [
"cs.CV"
]
|
Methods from convex optimization are widely used as building blocks for deep
learning algorithms. However, the reasons for their empirical success are
unclear, since modern convolutional networks (convnets), incorporating
rectifier units and max-pooling, are neither smooth nor convex. Standard
guarantees therefore do not apply. This paper provides the first convergence
rates for gradient descent on rectifier convnets. The proof utilizes the
particular structure of rectifier networks which consists in binary
active/inactive gates applied on top of an underlying linear network. The
approach generalizes to max-pooling, dropout and maxout. In other words, to
precisely the neural networks that perform best empirically. The key step is to
introduce gated games, an extension of convex games with similar convergence
properties that capture the gating function of rectifiers. The main result is
that rectifier convnets converge to a critical point at a rate controlled by
the gated-regret of the units in the network. Corollaries of the main result
include: (i) a game-theoretic description of the representations learned by a
neural network; (ii) a logarithmic-regret algorithm for training neural nets;
and (iii) a formal setting for analyzing conditional computation in neural nets
that can be applied to recently developed models of attention. | [
"cs.LG",
"cs.GT",
"cs.NE",
"stat.ML"
]
|
Humans spend vast hours in bed -- about one-third of the lifetime on average.
Besides, a human at rest is vital in many healthcare applications. Typically,
humans are covered by a blanket when resting, for which we propose a multimodal
approach to uncover the subjects so their bodies at rest can be viewed without
the occlusion of the blankets above. We propose a pyramid scheme to effectively
fuse the different modalities in a way that best leverages the knowledge
captured by the multimodal sensors. Specifically, the two most informative
modalities (i.e., depth and infrared images) are first fused to generate good
initial pose and shape estimation. Then pressure map and RGB images are further
fused one by one to refine the result by providing occlusion-invariant
information for the covered part, and accurate shape information for the
uncovered part, respectively. However, even with multimodal data, the task of
detecting human bodies at rest is still very challenging due to the extreme
occlusion of bodies. To further reduce the negative effects of the occlusion
from blankets, we employ an attention-based reconstruction module to generate
uncovered modalities, which are further fused to update current estimation via
a cyclic fashion. Extensive experiments validate the superiority of the
proposed model over others. | [
"cs.CV",
"cs.MM"
]
|
Cloud based medical image analysis has become popular recently due to the
high computation complexities of various deep neural network (DNN) based
frameworks and the increasingly large volume of medical images that need to be
processed. It has been demonstrated that for medical images the transmission
from local to clouds is much more expensive than the computation in the clouds
itself. Towards this, 3D image compression techniques have been widely applied
to reduce the data traffic. However, most of the existing image compression
techniques are developed around human vision, i.e., they are designed to
minimize distortions that can be perceived by human eyes. In this paper we will
use deep learning based medical image segmentation as a vehicle and demonstrate
that interestingly, machine and human view the compression quality differently.
Medical images compressed with good quality w.r.t. human vision may result in
inferior segmentation accuracy. We then design a machine vision oriented 3D
image compression framework tailored for segmentation using DNNs. Our method
automatically extracts and retains image features that are most important to
the segmentation. Comprehensive experiments on widely adopted segmentation
frameworks with HVSMR 2016 challenge dataset show that our method can achieve
significantly higher segmentation accuracy at the same compression rate, or
much better compression rate under the same segmentation accuracy, when
compared with the existing JPEG 2000 method. To the best of the authors'
knowledge, this is the first machine vision guided medical image compression
framework for segmentation in the clouds. | [
"cs.CV",
"cs.LG",
"stat.ML"
]
|
Graph classification is a highly impactful task that plays a crucial role in
a myriad of real-world applications such as molecular property prediction and
protein function prediction.Aiming to handle the new classes with limited
labeled graphs, few-shot graph classification has become a bridge of existing
graph classification solutions and practical usage.This work explores the
potential of metric-based meta-learning for solving few-shot graph
classification.We highlight the importance of considering structural
characteristics in the solution and propose a novel framework which explicitly
considers global structure and local structure of the input graph. An
implementation upon GIN, named SMF-GIN, is tested on two datasets, Chembl and
TRIANGLES, where extensive experiments validate the effectiveness of the
proposed method. The Chembl is constructed to fill in the gap of lacking
large-scale benchmark for few-shot graph classification evaluation, which is
released together with the implementation of SMF-GIN at:
https://github.com/jiangshunyu/SMF-GIN. | [
"cs.LG"
]
|
We introduce a new technique that automatically generates diverse, visually
compelling stylizations for a photograph in an unsupervised manner. We achieve
this by learning style ranking for a given input using a large photo collection
and selecting a diverse subset of matching styles for final style transfer. We
also propose a novel technique that transfers the global color and tone of the
chosen exemplars to the input photograph while avoiding the common visual
artifacts produced by the existing style transfer methods. Together, our style
selection and transfer techniques produce compelling, artifact-free results on
a wide range of input photographs, and a user study shows that our results are
preferred over other techniques. | [
"cs.CV"
]
|
We propose RPSRNet - a novel end-to-end trainable deep neural network for
rigid point set registration. For this task, we use a novel $2^D$-tree
representation for the input point sets and a hierarchical deep feature
embedding in the neural network. An iterative transformation refinement module
in our network boosts the feature matching accuracy in the intermediate stages.
We achieve an inference speed of 12-15ms to register a pair of input point
clouds as large as 250K. Extensive evaluation on (i) KITTI LiDAR odometry and
(ii) ModelNet-40 datasets shows that our method outperforms prior
state-of-the-art methods - e.g., on the KITTI data set, DCP-v2 by1.3 and 1.5
times, and PointNetLK by 1.8 and 1.9 times better rotational and translational
accuracy respectively. Evaluation on ModelNet40 shows that RPSRNet is more
robust than other benchmark methods when the samples contain a significant
amount of noise and other disturbances. RPSRNet accurately registers point
clouds with non-uniform sampling densities, e.g., LiDAR data, which cannot be
processed by many existing deep-learning-based registration methods. | [
"cs.CV"
]
|
Scene text recognition (STR) is very challenging due to the diversity of text
instances and the complexity of scenes. The community has paid increasing
attention to boost the performance by improving the pre-processing image
module, like rectification and deblurring, or the sequence translator. However,
another critical module, i.e., the feature sequence extractor, has not been
extensively explored. In this work, inspired by the success of neural
architecture search (NAS), which can identify better architectures than
human-designed ones, we propose automated STR (AutoSTR) to search
data-dependent backbones to boost text recognition performance. First, we
design a domain-specific search space for STR, which contains both choices on
operations and constraints on the downsampling path. Then, we propose a
two-step search algorithm, which decouples operations and downsampling path,
for an efficient search in the given space. Experiments demonstrate that, by
searching data-dependent backbones, AutoSTR can outperform the state-of-the-art
approaches on standard benchmarks with much fewer FLOPS and model parameters. | [
"cs.CV"
]
|
Normalizing flows have emerged as an important family of deep neural networks
for modelling complex probability distributions. In this note, we revisit their
coupling and autoregressive transformation layers as probabilistic graphical
models and show that they reduce to Bayesian networks with a pre-defined
topology and a learnable density at each node. From this new perspective, we
provide three results. First, we show that stacking multiple transformations in
a normalizing flow relaxes independence assumptions and entangles the model
distribution. Second, we show that a fundamental leap of capacity emerges when
the depth of affine flows exceeds 3 transformation layers. Third, we prove the
non-universality of the affine normalizing flow, regardless of its depth. | [
"cs.LG",
"stat.ML"
]
|
Corporate credit rating reflects the level of corporate credit and plays a
crucial role in modern financial risk control. But real-world credit rating
data usually shows long-tail distributions, which means heavy class imbalanced
problem challenging the corporate credit rating system greatly. To tackle that,
inspried by the recent advances of pre-train techniques in self-supervised
representation learning, we propose a novel framework named Contrastive
Pre-training for Corporate Credit Rating (CP4CCR), which utilizes the
self-surpervision for getting over class imbalance. Specifically, we propose
to, in the first phase, exert constrastive self-superivised pre-training
without label information, which want to learn a better class-agnostic
initialization. During this phase, two self-supervised task are developed
within CP4CCR: (i) Feature Masking (FM) and (ii) Feature Swapping(FS). In the
second phase, we can train any standard corporate redit rating model
initialized by the pre-trained network. Extensive experiments conducted on the
Chinese public-listed corporate rating dataset, prove that CP4CCR can improve
the performance of standard corporate credit rating models, especially for
class with few samples. | [
"cs.LG"
]
|
For graph classification tasks, many methods use a common strategy to
aggregate information of vertex neighbors. Although this strategy provides an
efficient means of extracting graph topological features, it brings excessive
amounts of information that might greatly reduce its accuracy when dealing with
large-scale neighborhoods. Learning graphs using paths or walks will not suffer
from this difficulty, but many have low utilization of each path or walk, which
might engender information loss and high computational costs. To solve this, we
propose a graph kernel using a longest common subsequence (LCS kernel) to
compute more comprehensive similarity between paths and walks, which resolves
substructure isomorphism difficulties. We also combine it with optimal
transport theory to extract more in-depth features of graphs. Furthermore, we
propose an LCS metric space and apply an adjacent point merge operation to
reduce its computational costs. Finally, we demonstrate that our proposed
method outperforms many state-of-the-art graph kernel methods. | [
"cs.LG",
"cs.AI",
"cs.DS",
"stat.ML"
]
|
This paper is a presentation of a new method for denoising images using
Haralick features and further segmenting the characters using artificial neural
networks. The image is divided into kernels, each of which is converted to a
GLCM (Gray Level Co-Occurrence Matrix) on which a Haralick Feature generation
function is called, the result of which is an array with fourteen elements
corresponding to fourteen features The Haralick values and the corresponding
noise/text classification form a dictionary, which is then used to de-noise the
image through kernel comparison. Segmentation is the process of extracting
characters from a document and can be used when letters are separated by white
space, which is an explicit boundary marker. Segmentation is the first step in
many Natural Language Processing problems. This paper explores the process of
segmentation using Neural Networks. While there have been numerous methods to
segment characters of a document, this paper is only concerned with the
accuracy of doing so using neural networks. It is imperative that the
characters be segmented correctly, for failing to do so will lead to incorrect
recognition by Natural language processing tools. Artificial Neural Networks
was used to attain accuracy of upto 89%. This method is suitable for languages
where the characters are delimited by white space. However, this method will
fail to provide acceptable results when the language heavily uses connected
letters. An example would be the Devanagari script, which is predominantly used
in northern India. | [
"cs.CV",
"cs.LG",
"eess.IV"
]
|
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