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There is an emerging trend in the reinforcement learning for healthcare
literature. In order to prepare longitudinal, irregularly sampled, clinical
datasets for reinforcement learning algorithms, many researchers will resample
the time series data to short, regular intervals and use
last-observation-carried-forward (LOCF) imputation to fill in these gaps.
Typically, they will not maintain any explicit information about which values
were imputed. In this work, we (1) call attention to this practice and discuss
its potential implications; (2) propose an alternative representation of the
patient state that addresses some of these issues; and (3) demonstrate in a
novel but representative clinical dataset that our alternative representation
yields consistently better results for achieving optimal control, as measured
by off-policy policy evaluation, compared to representations that do not
incorporate missingness information. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Point cloud learning has lately attracted increasing attention due to its
wide applications in many areas, such as computer vision, autonomous driving,
and robotics. As a dominating technique in AI, deep learning has been
successfully used to solve various 2D vision problems. However, deep learning
on point clouds is still in its infancy due to the unique challenges faced by
the processing of point clouds with deep neural networks. Recently, deep
learning on point clouds has become even thriving, with numerous methods being
proposed to address different problems in this area. To stimulate future
research, this paper presents a comprehensive review of recent progress in deep
learning methods for point clouds. It covers three major tasks, including 3D
shape classification, 3D object detection and tracking, and 3D point cloud
segmentation. It also presents comparative results on several publicly
available datasets, together with insightful observations and inspiring future
research directions. | [
"cs.CV",
"cs.LG",
"cs.RO",
"eess.IV"
] |
The task of estimating the gradient of a function in the presence of noise is
central to several forms of reinforcement learning, including policy search
methods. We present two techniques for reducing gradient estimation errors in
the presence of observable input noise applied to the control signal. The first
method extends the idea of a reinforcement baseline by fitting a local linear
model to the function whose gradient is being estimated; we show how to find
the linear model that minimizes the variance of the gradient estimate, and how
to estimate the model from data. The second method improves this further by
discounting components of the gradient vector that have high variance. These
methods are applied to the problem of motor control learning, where actuator
noise has a significant influence on behavior. In particular, we apply the
techniques to learn locally optimal controllers for a dart-throwing task using
a simulated three-link arm; we demonstrate that proposed methods significantly
improve the reward function gradient estimate and, consequently, the learning
curve, over existing methods. | [
"cs.LG",
"cs.SY"
] |
We present Sparse R-CNN, a purely sparse method for object detection in
images. Existing works on object detection heavily rely on dense object
candidates, such as $k$ anchor boxes pre-defined on all grids of image feature
map of size $H\times W$. In our method, however, a fixed sparse set of learned
object proposals, total length of $N$, are provided to object recognition head
to perform classification and location. By eliminating $HWk$ (up to hundreds of
thousands) hand-designed object candidates to $N$ (e.g. 100) learnable
proposals, Sparse R-CNN completely avoids all efforts related to object
candidates design and many-to-one label assignment. More importantly, final
predictions are directly output without non-maximum suppression post-procedure.
Sparse R-CNN demonstrates accuracy, run-time and training convergence
performance on par with the well-established detector baselines on the
challenging COCO dataset, e.g., achieving 45.0 AP in standard $3\times$
training schedule and running at 22 fps using ResNet-50 FPN model. We hope our
work could inspire re-thinking the convention of dense prior in object
detectors. The code is available at: https://github.com/PeizeSun/SparseR-CNN. | [
"cs.CV"
] |
The performance of neural network models is often limited by the availability
of big data sets. To treat this problem, we survey and develop novel synthetic
data generation and augmentation techniques for enhancing low/zero-sample
learning in satellite imagery. In addition to extending synthetic data
generation approaches, we propose a hierarchical detection approach to improve
the utility of synthetic training samples. We consider existing techniques for
producing synthetic imagery--3D models and neural style transfer--as well as
introducing our own adversarially trained reskinning network, the
GAN-Reskinner, to blend 3D models. Additionally, we test the value of synthetic
data in a two-stage, hierarchical detection/classification model of our own
construction. To test the effectiveness of synthetic imagery, we employ it in
the training of detection models and our two stage model, and evaluate the
resulting models on real satellite images. All modalities of synthetic data are
tested extensively on practical, geospatial analysis problems. Our experiments
show that synthetic data developed using our approach can often enhance
detection performance, particularly when combined with some real training
images. When the only source of data is synthetic, our GAN-Reskinner often
boosts performance over conventionally rendered 3D models and in all cases the
hierarchical model outperforms the baseline end-to-end detection architecture. | [
"cs.CV",
"cs.LG",
"I.2.10"
] |
As the foundation of driverless vehicle and intelligent robots, Simultaneous
Localization and Mapping(SLAM) has attracted much attention these days.
However, non-geometric modules of traditional SLAM algorithms are limited by
data association tasks and have become a bottleneck preventing the development
of SLAM. To deal with such problems, many researchers seek to Deep Learning for
help. But most of these studies are limited to virtual datasets or specific
environments, and even sacrifice efficiency for accuracy. Thus, they are not
practical enough.
We propose DF-SLAM system that uses deep local feature descriptors obtained
by the neural network as a substitute for traditional hand-made features.
Experimental results demonstrate its improvements in efficiency and stability.
DF-SLAM outperforms popular traditional SLAM systems in various scenes,
including challenging scenes with intense illumination changes. Its versatility
and mobility fit well into the need for exploring new environments. Since we
adopt a shallow network to extract local descriptors and remain others the same
as original SLAM systems, our DF-SLAM can still run in real-time on GPU. | [
"cs.CV",
"cs.AI",
"cs.RO"
] |
Single image haze removal is a very challenging and ill-posed problem. The
existing haze removal methods in literature, including the recently introduced
deep learning methods, model the problem of haze removal as that of estimating
intermediate parameters, viz., scene transmission map and atmospheric light.
These are used to compute the haze-free image from the hazy input image. Such
an approach only focuses on accurate estimation of intermediate parameters,
while the aesthetic quality of the haze-free image is unaccounted for in the
optimization framework. Thus, errors in the estimation of intermediate
parameters often lead to generation of inferior quality haze-free images. In
this paper, we present CANDY (Conditional Adversarial Networks based Dehazing
of hazY images), a fully end-to-end model which directly generates a clean
haze-free image from a hazy input image. CANDY also incorporates the visual
quality of haze-free image into the optimization function; thus, generating a
superior quality haze-free image. To the best of our knowledge, this is the
first work in literature to propose a fully end-to-end model for single image
haze removal. Also, this is the first work to explore the newly introduced
concept of generative adversarial networks for the problem of single image haze
removal. The proposed model CANDY was trained on a synthetically created haze
image dataset, while evaluation was performed on challenging synthetic as well
as real haze image datasets. The extensive evaluation and comparison results of
CANDY reveal that it significantly outperforms existing state-of-the-art haze
removal methods in literature, both quantitatively as well as qualitatively. | [
"cs.CV"
] |
Although much progress has been made recently in 3D face reconstruction, most
previous work has been devoted to predicting accurate and fine-grained 3D
shapes. In contrast, relatively little work has focused on generating
high-fidelity face textures. Compared with the prosperity of photo-realistic 2D
face image generation, high-fidelity 3D face texture generation has yet to be
studied. In this paper, we proposed a novel UV map generation model that
predicts the UV map from a single face image. The model consists of a UV
sampler and a UV generator. By selectively sampling the input face image's
pixels and adjusting their relative locations, the UV sampler generates an
incomplete UV map that could faithfully reconstruct the original face. Missing
textures in the incomplete UV map are further full-filled by the UV generator.
The training is based on pseudo ground truth blended by the 3DMM texture and
the input face texture, thus weakly supervised. To deal with the artifacts in
the imperfect pseudo UV map, multiple partial UV map discriminators are
leveraged. | [
"cs.CV"
] |
A background model describes a scene without any foreground objects and has a
number of applications, ranging from video surveillance to computational
photography. Recent studies have introduced the method of Dynamic Mode
Decomposition (DMD) for robustly separating video frames into a background
model and foreground components. While the method introduced operates by
converting color images to grayscale, we in this study propose a technique to
obtain the background model in the color domain. The effectiveness of our
technique is demonstrated using a publicly available Scene Background
Initialisation (SBI) dataset. Our results both qualitatively and quantitatively
show that DMD can successfully obtain a colored background model. | [
"cs.CV"
] |
Mutual Information (MI) plays an important role in representation learning.
However, MI is unfortunately intractable in continuous and high-dimensional
settings. Recent advances establish tractable and scalable MI estimators to
discover useful representation. However, most of the existing methods are not
capable of providing an accurate estimation of MI with low-variance when the MI
is large. We argue that directly estimating the gradients of MI is more
appealing for representation learning than estimating MI in itself. To this
end, we propose the Mutual Information Gradient Estimator (MIGE) for
representation learning based on the score estimation of implicit
distributions. MIGE exhibits a tight and smooth gradient estimation of MI in
the high-dimensional and large-MI settings. We expand the applications of MIGE
in both unsupervised learning of deep representations based on InfoMax and the
Information Bottleneck method. Experimental results have indicated significant
performance improvement in learning useful representation. | [
"stat.ML",
"cs.CV",
"cs.LG"
] |
Deep learning (DL) is transforming industry as decision-making processes are
being automated by deep neural networks (DNNs) trained on real-world data.
Driven partly by rapidly-expanding literature on DNN approximation theory
showing they can approximate a rich variety of functions, such tools are
increasingly being considered for problems in scientific computing. Yet, unlike
traditional algorithms in this field, little is known about DNNs from the
principles of numerical analysis, e.g., stability, accuracy, computational
efficiency and sample complexity. In this paper we introduce a computational
framework for examining DNNs in practice, and use it to study empirical
performance with regard to these issues. We study performance of DNNs of
different widths & depths on test functions in various dimensions, including
smooth and piecewise smooth functions. We also compare DL against best-in-class
methods for smooth function approx. based on compressed sensing (CS). Our main
conclusion from these experiments is that there is a crucial gap between the
approximation theory of DNNs and their practical performance, with trained DNNs
performing relatively poorly on functions for which there are strong
approximation results (e.g. smooth functions), yet performing well in
comparison to best-in-class methods for other functions. To analyze this gap
further, we provide some theoretical insights. We establish a practical
existence theorem, asserting existence of a DNN architecture and training
procedure that offers the same performance as CS. This establishes a key
theoretical benchmark, showing the gap can be closed, albeit via a strategy
guaranteed to perform as well as, but no better than, current best-in-class
schemes. Nevertheless, it demonstrates the promise of practical DNN approx., by
highlighting potential for better schemes through careful design of DNN
architectures and training strategies. | [
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML"
] |
Deep convolutional neural networks accuracy is heavily impacted by rotations
of the input data. In this paper, we propose a convolutional predictor that is
invariant to rotations in the input. This architecture is capable of predicting
the angular orientation without angle-annotated data. Furthermore, the
predictor maps continuously the random rotation of the input to a circular
space of the prediction. For this purpose, we use the roto-translation
properties existing in the Scattering Transform Networks with a series of 3D
Convolutions. We validate the results by training with upright and randomly
rotated samples. This allows further applications of this work on fields like
automatic re-orientation of randomly oriented datasets. | [
"cs.CV",
"eess.IV"
] |
This paper presents SO-Net, a permutation invariant architecture for deep
learning with orderless point clouds. The SO-Net models the spatial
distribution of point cloud by building a Self-Organizing Map (SOM). Based on
the SOM, SO-Net performs hierarchical feature extraction on individual points
and SOM nodes, and ultimately represents the input point cloud by a single
feature vector. The receptive field of the network can be systematically
adjusted by conducting point-to-node k nearest neighbor search. In recognition
tasks such as point cloud reconstruction, classification, object part
segmentation and shape retrieval, our proposed network demonstrates performance
that is similar with or better than state-of-the-art approaches. In addition,
the training speed is significantly faster than existing point cloud
recognition networks because of the parallelizability and simplicity of the
proposed architecture. Our code is available at the project website.
https://github.com/lijx10/SO-Net | [
"cs.CV"
] |
Generative Adversarial Network (GAN) and its variants have shown promising
results in generating synthetic data. However, the issues with GANs are: (i)
the learning happens around the training samples and the model often ends up
remembering them, consequently, compromising the privacy of individual samples
- this becomes a major concern when GANs are applied to training data including
personally identifiable information, (ii) the randomness in generated data -
there is no control over the specificity of generated samples. To address these
issues, we propose imdpGAN - an information maximizing differentially private
Generative Adversarial Network. It is an end-to-end framework that
simultaneously achieves privacy protection and learns latent representations.
With experiments on MNIST dataset, we show that imdpGAN preserves the privacy
of the individual data point, and learns latent codes to control the
specificity of the generated samples. We perform binary classification on digit
pairs to show the utility versus privacy trade-off. The classification accuracy
decreases as we increase privacy levels in the framework. We also
experimentally show that the training process of imdpGAN is stable but
experience a 10-fold time increase as compared with other GAN frameworks.
Finally, we extend imdpGAN framework to CelebA dataset to show how the privacy
and learned representations can be used to control the specificity of the
output. | [
"cs.CV",
"cs.AI",
"cs.CR"
] |
Segmenting objects in images and separating sound sources in audio are
challenging tasks, in part because traditional approaches require large amounts
of labeled data. In this paper we develop a neural network model for visual
object segmentation and sound source separation that learns from natural videos
through self-supervision. The model is an extension of recently proposed work
that maps image pixels to sounds. Here, we introduce a learning approach to
disentangle concepts in the neural networks, and assign semantic categories to
network feature channels to enable independent image segmentation and sound
source separation after audio-visual training on videos. Our evaluations show
that the disentangled model outperforms several baselines in semantic
segmentation and sound source separation. | [
"cs.CV",
"cs.SD",
"eess.AS",
"eess.IV"
] |
State-of-the-art two-stage object detectors apply a classifier to a sparse
set of object proposals, relying on region-wise features extracted by RoIPool
or RoIAlign as inputs. The region-wise features, in spite of aligning well with
the proposal locations, may still lack the crucial context information which is
necessary for filtering out noisy background detections, as well as recognizing
objects possessing no distinctive appearances. To address this issue, we
present a simple but effective Hierarchical Context Embedding (HCE) framework,
which can be applied as a plug-and-play component, to facilitate the
classification ability of a series of region-based detectors by mining
contextual cues. Specifically, to advance the recognition of context-dependent
object categories, we propose an image-level categorical embedding module which
leverages the holistic image-level context to learn object-level concepts.
Then, novel RoI features are generated by exploiting hierarchically embedded
context information beneath both whole images and interested regions, which are
also complementary to conventional RoI features. Moreover, to make full use of
our hierarchical contextual RoI features, we propose the early-and-late fusion
strategies (i.e., feature fusion and confidence fusion), which can be combined
to boost the classification accuracy of region-based detectors. Comprehensive
experiments demonstrate that our HCE framework is flexible and generalizable,
leading to significant and consistent improvements upon various region-based
detectors, including FPN, Cascade R-CNN and Mask R-CNN. | [
"cs.CV"
] |
We study the vision transformer structure in the mobile level in this paper,
and find a dramatic performance drop. We analyze the reason behind this
phenomenon, and propose a novel irregular patch embedding module and adaptive
patch fusion module to improve the performance. We conjecture that the vision
transformer blocks (which consist of multi-head attention and feed-forward
network) are more suitable to handle high-level information than low-level
features. The irregular patch embedding module extracts patches that contain
rich high-level information with different receptive fields. The transformer
blocks can obtain the most useful information from these irregular patches.
Then the processed patches pass the adaptive patch merging module to get the
final features for the classifier. With our proposed improvements, the
traditional uniform vision transformer structure can achieve state-of-the-art
results in mobile level. We improve the DeiT baseline by more than 9\% under
the mobile-level settings and surpass other transformer architectures like Swin
and CoaT by a large margin. | [
"cs.CV"
] |
Transformers have seen an unprecedented rise in Natural Language Processing
and Computer Vision tasks. However, in audio tasks, they are either infeasible
to train due to extremely large sequence length of audio waveforms or reach
competitive performance after feature extraction through Fourier-based methods,
incurring a loss-floor. In this work, we introduce an architecture, Audiomer,
where we combine 1D Residual Networks with Performer Attention to achieve
state-of-the-art performance in Keyword Spotting with raw audio waveforms,
out-performing all previous methods while also being computationally cheaper,
much more parameter and data-efficient. Audiomer allows for deployment in
compute-constrained devices and training on smaller datasets. | [
"cs.LG",
"cs.CL",
"cs.SD",
"eess.AS"
] |
Humans effortlessly "program" one another by communicating goals and desires
in natural language. In contrast, humans program robotic behaviours by
indicating desired object locations and poses to be achieved, by providing RGB
images of goal configurations, or supplying a demonstration to be imitated.
None of these methods generalize across environment variations, and they convey
the goal in awkward technical terms. This work proposes joint learning of
natural language grounding and instructable behavioural policies reinforced by
perceptual detectors of natural language expressions, grounded to the sensory
inputs of the robotic agent. Our supervision is narrated visual
demonstrations(NVD), which are visual demonstrations paired with verbal
narration (as opposed to being silent). We introduce a dataset of NVD where
teachers perform activities while describing them in detail. We map the
teachers' descriptions to perceptual reward detectors, and use them to train
corresponding behavioural policies in simulation.We empirically show that our
instructable agents (i) learn visual reward detectors using a small number of
examples by exploiting hard negative mined configurations from demonstration
dynamics, (ii) develop pick-and place policies using learned visual reward
detectors, (iii) benefit from object-factorized state representations that
mimic the syntactic structure of natural language goal expressions, and (iv)
can execute behaviours that involve novel objects in novel locations at test
time, instructed by natural language. | [
"cs.CV",
"cs.RO"
] |
Many complex multi-agent systems such as robot swarms control and autonomous
vehicle coordination can be modeled as Multi-Agent Reinforcement Learning
(MARL) tasks. QMIX, a popular MARL algorithm base on the monotonicity
constraint, has been used as a baseline for the benchmark environments, e.g.,
Starcraft Multi-Agent Challenge (SMAC), Predator-Prey (PP). Recent variants of
QMIX target relaxing the monotonicity constraint of QMIX to improve the
expressive power of QMIX, allowing for performance improvement in SMAC.
However, we find that such performance improvements of the variants are
significantly affected by various implementation tricks. In this paper, we
revisit the monotonicity constraint of QMIX, (1) we design a novel model RMC to
further investigate the monotonicity constraint; the results show that
monotonicity constraint can improve sample efficiency in some purely
cooperative tasks. (2) we then re-evaluate the performance of QMIX and these
variants by a grid hyperparameter search for the tricks; the results show QMIX
achieves the best performance among them; (3) we analyze the monotonic mixing
network from a theoretical perspective and show that it can represent any tasks
which can be interpreted as purely cooperative. These analyses demonstrate that
relaxing the monotonicity constraint of the mixing network will not always
improve the performance of QMIX, which breaks our previous impressions of the
monotonicity constraints. We open-source the code at
\url{https://github.com/hijkzzz/pymarl2}. | [
"cs.LG",
"cs.AI",
"cs.MA"
] |
The goals of this research were to search for Convolutional Neural Network
(CNN) architectures, suitable for an on-device processor with limited computing
resources, performing at substantially lower Network Architecture Search (NAS)
costs. A new algorithm entitled an Early Exit Population Initialisation (EE-PI)
for Evolutionary Algorithm (EA) was developed to achieve both goals. The EE-PI
reduces the total number of parameters in the search process by filtering the
models with fewer parameters than the maximum threshold. It will look for a new
model to replace those models with parameters more than the threshold. Thereby,
reducing the number of parameters, memory usage for model storage and
processing time while maintaining the same performance or accuracy. The search
time was reduced to 0.52 GPU day. This is a huge and significant achievement
compared to the NAS of 4 GPU days achieved using NSGA-Net, 3,150 GPU days by
the AmoebaNet model, and the 2,000 GPU days by the NASNet model. As well, Early
Exit Evolutionary Algorithm networks (EEEA-Nets) yield network architectures
with minimal error and computational cost suitable for a given dataset as a
class of network algorithms. Using EEEA-Net on CIFAR-10, CIFAR-100, and
ImageNet datasets, our experiments showed that EEEA-Net achieved the lowest
error rate among state-of-the-art NAS models, with 2.46% for CIFAR-10, 15.02%
for CIFAR-100, and 23.8% for ImageNet dataset. Further, we implemented this
image recognition architecture for other tasks, such as object detection,
semantic segmentation, and keypoint detection tasks, and, in our experiments,
EEEA-Net-C2 outperformed MobileNet-V3 on all of these various tasks. (The
algorithm code is available at https://github.com/chakkritte/EEEA-Net). | [
"cs.CV",
"cs.LG",
"cs.NE"
] |
The success of deep learning based models for computer vision applications
requires large scale human annotated data which are often expensive to
generate. Self-supervised learning, a subset of unsupervised learning, handles
this problem by learning meaningful features from unlabeled image or video
data. In this paper, we propose a self-supervised learning approach to learn
transferable features from MR video clips by enforcing the model to learn
anatomical features. The pretext task models are designed to predict the
correct ordering of the jumbled image patches that the MR video frames are
divided into. To the best of our knowledge, none of the supervised learning
models performing injury classification task from MR video provide any
explanation for the decisions made by the models and hence makes our work the
first of its kind on MR video data. Experiments on the pretext task show that
this proposed approach enables the model to learn spatial context invariant
features which help for reliable and explainable performance in downstream
tasks like classification of Anterior Cruciate Ligament tear injury from knee
MRI. The efficiency of the novel Convolutional Neural Network proposed in this
paper is reflected in the experimental results obtained in the downstream task. | [
"cs.CV"
] |
As constituent parts of image objects, superpixels can improve several
higher-level operations. However, image segmentation methods might have their
accuracy seriously compromised for reduced numbers of superpixels. We have
investigated a solution based on the Iterative Spanning Forest (ISF) framework.
In this work, we present Dynamic ISF (DISF) -- a method based on the following
steps. (a) It starts from an image graph and a seed set with considerably more
pixels than the desired number of superpixels. (b) The seeds compete among
themselves, and each seed conquers its most closely connected pixels, resulting
in an image partition (spanning forest) with connected superpixels. In step
(c), DISF assigns relevance values to seeds based on superpixel analysis and
removes the most irrelevant ones. Steps (b) and (c) are repeated until the
desired number of superpixels is reached. DISF has the chance to reconstruct
relevant edges after each iteration, when compared to region merging
algorithms. As compared to other seed-based superpixel methods, DISF is more
likely to find relevant seeds. It also introduces dynamic arc-weight estimation
in the ISF framework for more effective superpixel delineation, and we
demonstrate all results on three datasets with distinct object properties. | [
"cs.CV"
] |
Prognostics or early detection of incipient faults is an important industrial
challenge for condition-based and preventive maintenance. Physics-based
approaches to modeling fault progression are infeasible due to multiple
interacting components, uncontrolled environmental factors and observability
constraints. Moreover, such approaches to prognostics do not generalize to new
domains. Consequently, domain-agnostic data-driven machine learning approaches
to prognostics are desirable. Damage progression is a path-dependent process
and explicitly modeling the temporal patterns is critical for accurate
estimation of both the current damage state and its progression leading to
total failure. In this paper, we present a novel data-driven approach to
prognostics that employs a novel textual representation of multivariate
temporal sensor observations for predicting the future health state of the
monitored equipment early in its life. This representation enables us to
utilize well-understood concepts from text-mining for modeling, prediction and
understanding distress patterns in a domain agnostic way. The approach has been
deployed and successfully tested on large scale multivariate time-series data
from commercial aircraft engines. We report experiments on well-known publicly
available benchmark datasets and simulation datasets. The proposed approach is
shown to be superior in terms of prediction accuracy, lead time to prediction
and interpretability. | [
"stat.ML",
"cs.LG"
] |
Synthetic data generation has become essential in last years for feeding
data-driven algorithms, which surpassed traditional techniques performance in
almost every computer vision problem. Gathering and labelling the amount of
data needed for these data-hungry models in the real world may become
unfeasible and error-prone, while synthetic data give us the possibility of
generating huge amounts of data with pixel-perfect annotations. However, most
synthetic datasets lack from enough realism in their rendered images. In that
context UnrealROX generation tool was presented in 2019, allowing to generate
highly realistic data, at high resolutions and framerates, with an efficient
pipeline based on Unreal Engine, a cutting-edge videogame engine. UnrealROX
enabled robotic vision researchers to generate realistic and visually plausible
data with full ground truth for a wide variety of problems such as class and
instance semantic segmentation, object detection, depth estimation, visual
grasping, and navigation. Nevertheless, its workflow was very tied to generate
image sequences from a robotic on-board camera, making hard to generate data
for other purposes. In this work, we present UnrealROX+, an improved version of
UnrealROX where its decoupled and easy-to-use data acquisition system allows to
quickly design and generate data in a much more flexible and customizable way.
Moreover, it is packaged as an Unreal plug-in, which makes it more comfortable
to use with already existing Unreal projects, and it also includes new features
such as generating albedo or a Python API for interacting with the virtual
environment from Deep Learning frameworks. | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] |
Inspired by sophisticated echolocation abilities found in nature, we train a
generative adversarial network to predict plausible depth maps and grayscale
layouts from sound. To achieve this, our sound-to-vision model processes
binaural echo-returns from chirping sounds. We build upon previous work with
BatVision that consists of a sound-to-vision model and a self-collected dataset
using our mobile robot and low-cost hardware. We improve on the previous model
by introducing several changes to the model, which leads to a better depth and
grayscale estimation, and increased perceptual quality. Rather than using raw
binaural waveforms as input, we generate generalized cross-correlation (GCC)
features and use these as input instead. In addition, we change the model
generator and base it on residual learning and use spectral normalization in
the discriminator. We compare and present both quantitative and qualitative
improvements over our previous BatVision model. | [
"cs.CV",
"cs.RO",
"cs.SD",
"eess.AS"
] |
Person re-identification involves the recognition over time of individuals
captured using multiple distributed sensors. With the advent of powerful deep
learning methods able to learn discriminant representations for visual
recognition, cross-modal person re-identification based on different sensor
modalities has become viable in many challenging applications in, e.g.,
autonomous driving, robotics and video surveillance. Although some methods have
been proposed for re-identification between infrared and RGB images, few
address depth and RGB images. In addition to the challenges for each modality
associated with occlusion, clutter, misalignment, and variations in pose and
illumination, there is a considerable shift across modalities since data from
RGB and depth images are heterogeneous. In this paper, a new cross-modal
distillation network is proposed for robust person re-identification between
RGB and depth sensors. Using a two-step optimization process, the proposed
method transfers supervision between modalities such that similar structural
features are extracted from both RGB and depth modalities, yielding a
discriminative mapping to a common feature space. Our experiments investigate
the influence of the dimensionality of the embedding space, compares transfer
learning from depth to RGB and vice versa, and compares against other
state-of-the-art cross-modal re-identification methods. Results obtained with
BIWI and RobotPKU datasets indicate that the proposed method can successfully
transfer descriptive structural features from the depth modality to the RGB
modality. It can significantly outperform state-of-the-art conventional methods
and deep neural networks for cross-modal sensing between RGB and depth, with no
impact on computational complexity. | [
"cs.CV",
"eess.IV"
] |
We introduce a Gaussian process-based model for handling of non-stationarity.
The warping is achieved non-parametrically, through imposing a prior on the
relative change of distance between subsequent observation inputs. The model
allows the use of general gradient optimization algorithms for training and
incurs only a small computational overhead on training and prediction. The
model finds its applications in forecasting in non-stationary time series with
either gradually varying volatility, presence of change points, or a
combination thereof. We evaluate the model on synthetic and real-world time
series data comparing against both baseline and known state-of-the-art
approaches and show that the model exhibits state-of-the-art forecasting
performance at a lower implementation and computation cost. | [
"stat.ML",
"cs.LG"
] |
Decomposing knowledge into interchangeable pieces promises a generalization
advantage when there are changes in distribution. A learning agent interacting
with its environment is likely to be faced with situations requiring novel
combinations of existing pieces of knowledge. We hypothesize that such a
decomposition of knowledge is particularly relevant for being able to
generalize in a systematic manner to out-of-distribution changes. To study
these ideas, we propose a particular training framework in which we assume that
the pieces of knowledge an agent needs and its reward function are stationary
and can be re-used across tasks. An attention mechanism dynamically selects
which modules can be adapted to the current task, and the parameters of the
selected modules are allowed to change quickly as the learner is confronted
with variations in what it experiences, while the parameters of the attention
mechanisms act as stable, slowly changing, meta-parameters. We focus on pieces
of knowledge captured by an ensemble of modules sparsely communicating with
each other via a bottleneck of attention. We find that meta-learning the
modular aspects of the proposed system greatly helps in achieving faster
adaptation in a reinforcement learning setup involving navigation in a
partially observed grid world with image-level input. We also find that
reversing the role of parameters and meta-parameters does not work nearly as
well, suggesting a particular role for fast adaptation of the dynamically
selected modules. | [
"cs.LG",
"cs.AI"
] |
In this paper, we present an integrated system for automatically generating
and editing face images through face swapping, attribute-based editing, and
random face parts synthesis. The proposed system is based on a deep neural
network that variationally learns the face and hair regions with large-scale
face image datasets. Different from conventional variational methods, the
proposed network represents the latent spaces individually for faces and hairs.
We refer to the proposed network as region-separative generative adversarial
network (RSGAN). The proposed network independently handles face and hair
appearances in the latent spaces, and then, face swapping is achieved by
replacing the latent-space representations of the faces, and reconstruct the
entire face image with them. This approach in the latent space robustly
performs face swapping even for images which the previous methods result in
failure due to inappropriate fitting or the 3D morphable models. In addition,
the proposed system can further edit face-swapped images with the same network
by manipulating visual attributes or by composing them with randomly generated
face or hair parts. | [
"cs.CV",
"cs.GR"
] |
We propose an end-to-end model which generates captions for images embedded
in news articles. News images present two key challenges: they rely on
real-world knowledge, especially about named entities; and they typically have
linguistically rich captions that include uncommon words. We address the first
challenge by associating words in the caption with faces and objects in the
image, via a multi-modal, multi-head attention mechanism. We tackle the second
challenge with a state-of-the-art transformer language model that uses
byte-pair-encoding to generate captions as a sequence of word parts. On the
GoodNews dataset, our model outperforms the previous state of the art by a
factor of four in CIDEr score (13 to 54). This performance gain comes from a
unique combination of language models, word representation, image embeddings,
face embeddings, object embeddings, and improvements in neural network design.
We also introduce the NYTimes800k dataset which is 70% larger than GoodNews,
has higher article quality, and includes the locations of images within
articles as an additional contextual cue. | [
"cs.CV",
"cs.CL",
"I.4.0; I.2.7"
] |
This paper studies video inpainting detection, which localizes an inpainted
region in a video both spatially and temporally. In particular, we introduce
VIDNet, Video Inpainting Detection Network, which contains a two-stream
encoder-decoder architecture with attention module. To reveal artifacts encoded
in compression, VIDNet additionally takes in Error Level Analysis frames to
augment RGB frames, producing multimodal features at different levels with an
encoder. Exploring spatial and temporal relationships, these features are
further decoded by a Convolutional LSTM to predict masks of inpainted regions.
In addition, when detecting whether a pixel is inpainted or not, we present a
quad-directional local attention module that borrows information from its
surrounding pixels from four directions. Extensive experiments are conducted to
validate our approach. We demonstrate, among other things, that VIDNet not only
outperforms by clear margins alternative inpainting detection methods but also
generalizes well on novel videos that are unseen during training. | [
"cs.CV"
] |
Recent machine learning models have shown that including attention as a
component results in improved model accuracy and interpretability, despite the
concept of attention in these approaches only loosely approximating the brain's
attention mechanism. Here we extend this work by building a more brain-inspired
deep network model of the primate ATTention Network (ATTNet) that learns to
shift its attention so as to maximize the reward. Using deep reinforcement
learning, ATTNet learned to shift its attention to the visual features of a
target category in the context of a search task. ATTNet's dorsal layers also
learned to prioritize these shifts of attention so as to maximize success of
the ventral pathway classification and receive greater reward. Model behavior
was tested against the fixations made by subjects searching images for the same
cued category. Both subjects and ATTNet showed evidence for attention being
preferentially directed to target goals, behaviorally measured as oculomotor
guidance to targets. More fundamentally, ATTNet learned to shift its attention
to target like objects and spatially route its visual inputs to accomplish the
task. This work makes a step toward a better understanding of the role of
attention in the brain and other computational systems. | [
"cs.CV",
"q-bio.NC"
] |
We investigate training Generative Adversarial Networks, GANs, with less
data. Subsets of the training dataset can express empirical sample diversity
while reducing training resource requirements, e.g. time and memory. We ask how
much data reduction impacts generator performance and gauge the additive value
of generator ensembles. In addition to considering stand-alone GAN training and
ensembles of generator models, we also consider reduced data training on an
evolutionary GAN training framework named Redux-Lipizzaner. Redux-Lipizzaner
makes GAN training more robust and accurate by exploiting overlapping
neighborhood-based training on a spatial 2D grid. We conduct empirical
experiments on Redux-Lipizzaner using the MNIST and CelebA data sets. | [
"cs.LG",
"cs.NE"
] |
Transfer learning methods for reinforcement learning (RL) domains facilitate
the acquisition of new skills using previously acquired knowledge. The vast
majority of existing approaches assume that the agents have the same design,
e.g. same shape and action spaces. In this paper we address the problem of
transferring previously acquired skills amongst morphologically different
agents (MDAs). For instance, assuming that a bipedal agent has been trained to
move forward, could this skill be transferred on to a one-leg hopper so as to
make its training process for the same task more sample efficient? We frame
this problem as one of subspace learning whereby we aim to infer latent factors
representing the control mechanism that is common between MDAs. We propose a
novel paired variational encoder-decoder model, PVED, that disentangles the
control of MDAs into shared and agent-specific factors. The shared factors are
then leveraged for skill transfer using RL. Theoretically, we derive a theorem
indicating how the performance of PVED depends on the shared factors and agent
morphologies. Experimentally, PVED has been extensively validated on four
MuJoCo environments. We demonstrate its performance compared to a
state-of-the-art approach and several ablation cases, visualize and interpret
the hidden factors, and identify avenues for future improvements. | [
"cs.LG",
"stat.ML"
] |
3D object detection plays a crucial role in environmental perception for
autonomous vehicles, which is the prerequisite of decision and control. This
paper analyses partition-based methods' inherent drawbacks. In the partition
operation, a single instance such as a pedestrian is sliced into several
pieces, which we call it the partition effect. We propose the Spatial-Attention
Graph Convolution (S-AT GCN), forming the Feature Enhancement (FE) layers to
overcome this drawback. The S-AT GCN utilizes the graph convolution and the
spatial attention mechanism to extract local geometrical structure features.
This allows the network to have more meaningful features for the foreground.
Our experiments on the KITTI 3D object and bird's eye view detection show that
S-AT Conv and FE layers are effective, especially for small objects. FE layers
boost the pedestrian class performance by 3.62\% and cyclist class by 4.21\% 3D
mAP. The time cost of these extra FE layers are limited. PointPillars with FE
layers can achieve 48 PFS, satisfying the real-time requirement. | [
"cs.CV"
] |
Recently, fully-connected and convolutional neural networks have been trained
to achieve state-of-the-art performance on a wide variety of tasks such as
speech recognition, image classification, natural language processing, and
bioinformatics. For classification tasks, most of these "deep learning" models
employ the softmax activation function for prediction and minimize
cross-entropy loss. In this paper, we demonstrate a small but consistent
advantage of replacing the softmax layer with a linear support vector machine.
Learning minimizes a margin-based loss instead of the cross-entropy loss. While
there have been various combinations of neural nets and SVMs in prior art, our
results using L2-SVMs show that by simply replacing softmax with linear SVMs
gives significant gains on popular deep learning datasets MNIST, CIFAR-10, and
the ICML 2013 Representation Learning Workshop's face expression recognition
challenge. | [
"cs.LG",
"stat.ML"
] |
Standard segmentation of medical images based on full-supervised
convolutional networks demands accurate dense annotations. Such learning
framework is built on laborious manual annotation with restrict demands for
expertise, leading to insufficient high-quality labels. To overcome such
limitation and exploit massive weakly labeled data, we relaxed the rigid
labeling requirement and developed a semi-supervised learning framework based
on a teacher-student fashion for organ and lesion segmentation with partial
dense-labeled supervision and supplementary loose bounding-box supervision
which are easier to acquire. Observing the geometrical relation of an organ and
its inner lesions in most cases, we propose a hierarchical organ-to-lesion
(O2L) attention module in a teacher segmentor to produce pseudo-labels. Then a
student segmentor is trained with combinations of manual-labeled and
pseudo-labeled annotations. We further proposed a localization branch realized
via an aggregation of high-level features in a deep decoder to predict
locations of organ and lesion, which enriches student segmentor with precise
localization information. We validated each design in our model on LiTS
challenge datasets by ablation study and showed its state-of-the-art
performance compared with recent methods. We show our model is robust to the
quality of bounding box and achieves comparable performance compared with
full-supervised learning methods. | [
"cs.CV"
] |
Data-driven generative 3D face models are used to compactly encode facial
shape data into meaningful parametric representations. A desirable property of
these models is their ability to effectively decouple natural sources of
variation, in particular identity and expression. While factorized
representations have been proposed for that purpose, they are still limited in
the variability they can capture and may present modeling artifacts when
applied to tasks such as expression transfer. In this work, we explore a new
direction with Generative Adversarial Networks and show that they contribute to
better face modeling performances, especially in decoupling natural factors,
while also achieving more diverse samples. To train the model we introduce a
novel architecture that combines a 3D generator with a 2D discriminator that
leverages conventional CNNs, where the two components are bridged by a geometry
mapping layer. We further present a training scheme, based on auxiliary
classifiers, to explicitly disentangle identity and expression attributes.
Through quantitative and qualitative results on standard face datasets, we
illustrate the benefits of our model and demonstrate that it outperforms
competing state of the art methods in terms of decoupling and diversity. | [
"cs.CV",
"cs.LG"
] |
Many learning-based 3D shape semantic segmentation methods assign labels to
shape atoms (e.g. points in a point cloud or faces in a mesh) with a
single-pass approach trained in an end-to-end fashion. Such methods achieve
impressive performance but require large amounts of labeled training data. This
paradigm entangles two separable subproblems: (1) decomposing a shape into
regions and (2) assigning semantic labels to these regions. We claim that
disentangling these subproblems reduces the labeled data burden: (1) region
decomposition requires no semantic labels and could be performed in an
unsupervised fashion, and (2) labeling shape regions instead of atoms results
in a smaller search space and should be learnable with less labeled training
data. In this paper, we investigate this second claim by presenting the
Neurally-Guided Shape Parser (NGSP), a method that learns how to assign
semantic labels to regions of an over-segmented 3D shape. We solve this problem
via MAP inference, modeling the posterior probability of a labeling assignment
conditioned on an input shape. We employ a Monte Carlo importance sampling
approach guided by a neural proposal network, a search-based approach made
feasible by assuming the input shape is decomposed into discrete regions. We
evaluate NGSP on the task of hierarchical semantic segmentation on manufactured
3D shapes from PartNet. We find that NGSP delivers significant performance
improvements over baselines that learn to label shape atoms and then aggregate
predictions for each shape region, especially in low-data regimes. Finally, we
demonstrate that NGSP is robust to region granularity, as it maintains strong
segmentation performance even as the regions undergo significant corruption. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Classifiers that can be implemented on chip with minimal computational and
memory resources are essential for edge computing in emerging applications such
as medical and IoT devices. This paper introduces a machine learning model
based on oblique decision trees to enable resource-efficient classification on
a neural implant. By integrating model compression with probabilistic routing
and implementing cost-aware learning, our proposed model could significantly
reduce the memory and hardware cost compared to state-of-the-art models, while
maintaining the classification accuracy. We trained the resource-efficient
oblique tree with power-efficient regularization (ResOT-PE) on three neural
classification tasks to evaluate the performance, memory, and hardware
requirements. On seizure detection task, we were able to reduce the model size
by 3.4X and the feature extraction cost by 14.6X compared to the ensemble of
boosted trees, using the intracranial EEG from 10 epilepsy patients. In a
second experiment, we tested the ResOT-PE model on tremor detection for
Parkinson's disease, using the local field potentials from 12 patients
implanted with a deep-brain stimulation (DBS) device. We achieved a comparable
classification performance as the state-of-the-art boosted tree ensemble, while
reducing the model size and feature extraction cost by 10.6X and 6.8X,
respectively. We also tested on a 6-class finger movement detection task using
ECoG recordings from 9 subjects, reducing the model size by 17.6X and feature
computation cost by 5.1X. The proposed model can enable a low-power and
memory-efficient implementation of classifiers for real-time neurological
disease detection and motor decoding. | [
"cs.LG",
"eess.SP",
"stat.ML"
] |
The power of neural networks lies in their ability to generalize to unseen
data, yet the underlying reasons for this phenomenon remain elusive. Numerous
rigorous attempts have been made to explain generalization, but available
bounds are still quite loose, and analysis does not always lead to true
understanding. The goal of this work is to make generalization more intuitive.
Using visualization methods, we discuss the mystery of generalization, the
geometry of loss landscapes, and how the curse (or, rather, the blessing) of
dimensionality causes optimizers to settle into minima that generalize well. | [
"cs.LG",
"cs.NE",
"stat.ML"
] |
In computer vision, image segmentation is always selected as a major research
topic by researchers. Due to its vital rule in image processing, there always
arises the need of a better image segmentation method. Clustering is an
unsupervised study with its application in almost every field of science and
engineering. Many researchers used clustering in image segmentation process.
But still there requires improvement of such approaches. In this paper, a novel
approach for clustering based image segmentation is proposed. Here, we give
importance on color space and choose lab for this task. The famous hard
clustering algorithm K-means is used, but as its performance is dependent on
choosing a proper distance measure, so, we go for cosine distance measure. Then
the segmented image is filtered with sobel filter. The filtered image is
analyzed with marker watershed algorithm to have the final segmented result of
our original image. The MSE and PSNR values are evaluated to observe the
performance. | [
"cs.CV"
] |
We present a novel approach to weakly supervised object detection. Instead of
annotated images, our method only requires two short videos to learn to detect
a new object: 1) a video of a moving object and 2) one or more "negative"
videos of the scene without the object. The key idea of our algorithm is to
train the object detector to produce physically plausible object motion when
applied to the first video and to not detect anything in the second video. With
this approach, our method learns to locate objects without any object location
annotations. Once the model is trained, it performs object detection on single
images. We evaluate our method in three robotics settings that afford learning
objects from motion: observing moving objects, watching demonstrations of
object manipulation, and physically interacting with objects (see a video
summary at https://youtu.be/BH0Hv3zZG_4). | [
"cs.CV",
"cs.LG",
"cs.RO",
"stat.ML"
] |
Recent works have made great progress in semantic segmentation by exploiting
contextual information in a local or global manner with dilated convolutions,
pyramid pooling or self-attention mechanism. In order to avoid potential
misleading contextual information aggregation in previous works, we propose a
class-wise dynamic graph convolution (CDGC) module to adaptively propagate
information. The graph reasoning is performed among pixels in the same class.
Based on the proposed CDGC module, we further introduce the Class-wise Dynamic
Graph Convolution Network(CDGCNet), which consists of two main parts including
the CDGC module and a basic segmentation network, forming a coarse-to-fine
paradigm. Specifically, the CDGC module takes the coarse segmentation result as
class mask to extract node features for graph construction and performs dynamic
graph convolutions on the constructed graph to learn the feature aggregation
and weight allocation. Then the refined feature and the original feature are
fused to get the final prediction. We conduct extensive experiments on three
popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012
and COCO Stuff, and achieve state-of-the-art performance on all three
benchmarks. | [
"cs.CV"
] |
Randomized smoothing has established state-of-the-art provable robustness
against $\ell_2$ norm adversarial attacks with high probability. However, the
introduced Gaussian data augmentation causes a severe decrease in natural
accuracy. We come up with a question, "Is it possible to construct a smoothed
classifier without randomization while maintaining natural accuracy?". We find
the answer is definitely yes. We study how to transform any classifier into a
certified robust classifier based on a popular and elegant mathematical tool,
Bernstein polynomial. Our method provides a deterministic algorithm for
decision boundary smoothing. We also introduce a distinctive approach of
norm-independent certified robustness via numerical solutions of nonlinear
systems of equations. Theoretical analyses and experimental results indicate
that our method is promising for classifier smoothing and robustness
certification. | [
"cs.LG",
"cs.CR"
] |
In AI research and industry, machine learning is the most widely used tool.
One of the most important machine learning algorithms is Gradient Boosting
Decision Tree, i.e. GBDT whose training process needs considerable
computational resources and time. To shorten GBDT training time, many works
tried to apply GBDT on Parameter Server. However, those GBDT algorithms are
synchronous parallel algorithms which fail to make full use of Parameter
Server. In this paper, we examine the possibility of using asynchronous
parallel methods to train GBDT model and name this algorithm as asynch-SGBDT
(asynchronous parallel stochastic gradient boosting decision tree). Our
theoretical and experimental results indicate that the scalability of
asynch-SGBDT is influenced by the sample diversity of datasets, sampling rate,
step length and the setting of GBDT tree. Experimental results also show
asynch-SGBDT training process reaches a linear speedup in asynchronous parallel
manner when datasets and GBDT trees meet high scalability requirements. | [
"cs.LG",
"cs.DC",
"stat.ML"
] |
Existing learning models often utilise CT-scan images to predict lung
diseases. These models are posed by high uncertainties that affect lung
segmentation and visual feature learning. We introduce MARL, a novel Multimodal
Attentional Representation Learning model architecture that learns useful
features from multimodal data under uncertainty. We feed the proposed model
with both the lung CT-scan images and their perspective historical patients'
biological records collected over times. Such rich data offers to analyse both
spatial and temporal aspects of the disease. MARL employs Fuzzy-based image
spatial segmentation to overcome uncertainties in CT-scan images. We then
utilise a pre-trained Convolutional Neural Network (CNN) to learn visual
representation vectors from images. We augment patients' data with statistical
features from the segmented images. We develop a Long Short-Term Memory (LSTM)
network to represent the augmented data and learn sequential patterns of
disease progressions. Finally, we inject both CNN and LSTM feature vectors to
an attention layer to help focus on the best learning features. We evaluated
MARL on regression of lung disease progression and status classification. MARL
outperforms state-of-the-art CNN architectures, such as EfficientNet and
DenseNet, and baseline prediction models. It achieves a 91% R^2 score, which is
higher than the other models by a range of 8% to 27%. Also, MARL achieves 97%
and 92% accuracy for binary and multi-class classification, respectively. MARL
improves the accuracy of state-of-the-art CNN models with a range of 19% to
57%. The results show that combining spatial and sequential temporal features
produces better discriminative feature. | [
"cs.CV"
] |
We introduce a new method for training generative adversarial networks by
applying the Wasserstein-2 metric proximal on the generators. The approach is
based on Wasserstein information geometry. It defines a parametrization
invariant natural gradient by pulling back optimal transport structures from
probability space to parameter space. We obtain easy-to-implement iterative
regularizers for the parameter updates of implicit deep generative models. Our
experiments demonstrate that this method improves the speed and stability of
training in terms of wall-clock time and Fr\'echet Inception Distance. | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA"
] |
The recent thrust on digital agriculture (DA) has renewed significant
research interest in the automated delineation of agricultural fields. Most
prior work addressing this problem have focused on detecting medium to large
fields, while there is strong evidence that around 40\% of the fields
world-wide and 70% of the fields in Asia and Africa are small. The lack of
adequate labeled images for small fields, huge variations in their color,
texture, and shape, and faint boundary lines separating them make it difficult
to develop an end-to-end learning model for detecting such fields. Hence, in
this paper, we present a multi-stage approach that uses a combination of
machine learning and image processing techniques. In the first stage, we
leverage state-of-the-art edge detection algorithms such as holistically-nested
edge detection (HED) to extract first-level contours and polygons. In the
second stage, we propose image-processing techniques to identify polygons that
are non-fields, over-segmentations, or noise and eliminate them. The next stage
tackles under-segmentations using a combination of a novel ``cut-point'' based
technique and localized second-level edge detection to obtain individual
parcels. Since a few small, non-cropped but vegetated or constructed pockets
can be interspersed in areas that are predominantly croplands, in the final
stage, we train a classifier for identifying each parcel from the previous
stage as an agricultural field or not. In an evaluation using high-resolution
imagery, we show that our approach has a high F-Score of 0.84 in areas with
large fields and reasonable accuracy with an F-Score of 0.73 in areas with
small fields, which is encouraging. | [
"cs.CV",
"eess.IV"
] |
Answering complex logical queries on large-scale incomplete knowledge graphs
(KGs) is a fundamental yet challenging task. Recently, a promising approach to
this problem has been to embed KG entities as well as the query into a vector
space such that entities that answer the query are embedded close to the query.
However, prior work models queries as single points in the vector space, which
is problematic because a complex query represents a potentially large set of
its answer entities, but it is unclear how such a set can be represented as a
single point. Furthermore, prior work can only handle queries that use
conjunctions ($\wedge$) and existential quantifiers ($\exists$). Handling
queries with logical disjunctions ($\vee$) remains an open problem. Here we
propose query2box, an embedding-based framework for reasoning over arbitrary
queries with $\wedge$, $\vee$, and $\exists$ operators in massive and
incomplete KGs. Our main insight is that queries can be embedded as boxes
(i.e., hyper-rectangles), where a set of points inside the box corresponds to a
set of answer entities of the query. We show that conjunctions can be naturally
represented as intersections of boxes and also prove a negative result that
handling disjunctions would require embedding with dimension proportional to
the number of KG entities. However, we show that by transforming queries into a
Disjunctive Normal Form, query2box is capable of handling arbitrary logical
queries with $\wedge$, $\vee$, $\exists$ in a scalable manner. We demonstrate
the effectiveness of query2box on three large KGs and show that query2box
achieves up to 25% relative improvement over the state of the art. | [
"cs.LG",
"cs.CL",
"stat.ML"
] |
Scene flow represents the 3D motion of every point in the dynamic
environments. Like the optical flow that represents the motion of pixels in 2D
images, 3D motion representation of scene flow benefits many applications, such
as autonomous driving and service robot. This paper studies the problem of
scene flow estimation from two consecutive 3D point clouds. In this paper, a
novel hierarchical neural network with double attention is proposed for
learning the correlation of point features in adjacent frames and refining
scene flow from coarse to fine layer by layer. The proposed network has a new
more-for-less hierarchical architecture. The more-for-less means that the
number of input points is greater than the number of output points for scene
flow estimation, which brings more input information and balances the precision
and resource consumption. In this hierarchical architecture, scene flow of
different levels is generated and supervised respectively. A novel attentive
embedding module is introduced to aggregate the features of adjacent points
using a double attention method in a patch-to-patch manner. The proper layers
for flow embedding and flow supervision are carefully considered in our network
designment. Experiments show that the proposed network outperforms the
state-of-the-art performance of 3D scene flow estimation on the FlyingThings3D
and KITTI Scene Flow 2015 datasets. We also apply the proposed network to
realistic LiDAR odometry task, which is an key problem in autonomous driving.
The experiment results demonstrate that our proposed network can outperform the
ICP-based method and shows the good practical application ability. | [
"cs.CV"
] |
This paper introduces a generative model equivariant to Euclidean symmetries:
E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the
discriminative E(n) graph neural networks and integrate them as a differential
equation to obtain an invertible equivariant function: a continuous-time
normalizing flow. We demonstrate that E-NFs considerably outperform baselines
and existing methods from the literature on particle systems such as DW4 and
LJ13, and on molecules from QM9 in terms of log-likelihood. To the best of our
knowledge, this is the first flow that jointly generates molecule features and
positions in 3D. | [
"cs.LG",
"physics.chem-ph",
"stat.ML"
] |
While many works focus on 3D reconstruction from images, in this paper, we
focus on 3D shape reconstruction and completion from a variety of 3D inputs,
which are deficient in some respect: low and high resolution voxels, sparse and
dense point clouds, complete or incomplete. Processing of such 3D inputs is an
increasingly important problem as they are the output of 3D scanners, which are
becoming more accessible, and are the intermediate output of 3D computer vision
algorithms. Recently, learned implicit functions have shown great promise as
they produce continuous reconstructions. However, we identified two limitations
in reconstruction from 3D inputs: 1) details present in the input data are not
retained, and 2) poor reconstruction of articulated humans. To solve this, we
propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs,
can handle multiple topologies, and complete shapes for missing or sparse input
data retaining the nice properties of recent learned implicit functions, but
critically they can also retain detail when it is present in the input data,
and can reconstruct articulated humans. Our work differs from prior work in two
crucial aspects. First, instead of using a single vector to encode a 3D shape,
we extract a learnable 3-dimensional multi-scale tensor of deep features, which
is aligned with the original Euclidean space embedding the shape. Second,
instead of classifying x-y-z point coordinates directly, we classify deep
features extracted from the tensor at a continuous query point. We show that
this forces our model to make decisions based on global and local shape
structure, as opposed to point coordinates, which are arbitrary under Euclidean
transformations. Experiments demonstrate that IF-Nets clearly outperform prior
work in 3D object reconstruction in ShapeNet, and obtain significantly more
accurate 3D human reconstructions. | [
"cs.CV",
"cs.LG"
] |
We propose an end-to-end learning framework for segmenting generic objects in
both images and videos. Given a novel image or video, our approach produces a
pixel-level mask for all "object-like" regions---even for object categories
never seen during training. We formulate the task as a structured prediction
problem of assigning an object/background label to each pixel, implemented
using a deep fully convolutional network. When applied to a video, our model
further incorporates a motion stream, and the network learns to combine both
appearance and motion and attempts to extract all prominent objects whether
they are moving or not. Beyond the core model, a second contribution of our
approach is how it leverages varying strengths of training annotations.
Pixel-level annotations are quite difficult to obtain, yet crucial for training
a deep network approach for segmentation. Thus we propose ways to exploit
weakly labeled data for learning dense foreground segmentation. For images, we
show the value in mixing object category examples with image-level labels
together with relatively few images with boundary-level annotations. For video,
we show how to bootstrap weakly annotated videos together with the network
trained for image segmentation. Through experiments on multiple challenging
image and video segmentation benchmarks, our method offers consistently strong
results and improves the state-of-the-art for fully automatic segmentation of
generic (unseen) objects. In addition, we demonstrate how our approach benefits
image retrieval and image retargeting, both of which flourish when given our
high-quality foreground maps. Code, models, and videos are
at:http://vision.cs.utexas.edu/projects/pixelobjectness/ | [
"cs.CV"
] |
Transformers have made much progress in dealing with visual tasks. However,
existing vision transformers still do not possess an ability that is important
to visual input: building the attention among features of different scales. The
reasons for this problem are two-fold: (1) Input embeddings of each layer are
equal-scale without cross-scale features; (2) Some vision transformers
sacrifice the small-scale features of embeddings to lower the cost of the
self-attention module. To make up this defect, we propose Cross-scale Embedding
Layer (CEL) and Long Short Distance Attention (LSDA). In particular, CEL blends
each embedding with multiple patches of different scales, providing the model
with cross-scale embeddings. LSDA splits the self-attention module into a
short-distance and long-distance one, also lowering the cost but keeping both
small-scale and large-scale features in embeddings. Through these two designs,
we achieve cross-scale attention. Besides, we propose dynamic position bias for
vision transformers to make the popular relative position bias apply to
variable-sized images. Based on these proposed modules, we construct our vision
architecture called CrossFormer. Experiments show that CrossFormer outperforms
other transformers on several representative visual tasks, especially object
detection and segmentation. The code has been released:
https://github.com/cheerss/CrossFormer. | [
"cs.CV",
"cs.LG"
] |
Based on decision trees, many fields have arguably made tremendous progress
in recent years. In simple words, decision trees use the strategy of
"divide-and-conquer" to divide the complex problem on the dependency between
input features and labels into smaller ones. While decision trees have a long
history, recent advances have greatly improved their performance in
computational advertising, recommender system, information retrieval, etc. We
introduce common tree-based models (e.g., Bayesian CART, Bayesian regression
splines) and training techniques (e.g., mixed integer programming, alternating
optimization, gradient descent). Along the way, we highlight probabilistic
characteristics of tree-based models and explain their practical and
theoretical benefits. Except machine learning and data mining, we try to show
theoretical advances on tree-based models from other fields such as statistics
and operation research. We list the reproducible resource at the end of each
method. | [
"cs.LG",
"stat.ML"
] |
Self-supervised learning has shown great potentials in improving the video
representation ability of deep neural networks by getting supervision from the
data itself. However, some of the current methods tend to cheat from the
background, i.e., the prediction is highly dependent on the video background
instead of the motion, making the model vulnerable to background changes. To
mitigate the model reliance towards the background, we propose to remove the
background impact by adding the background. That is, given a video, we randomly
select a static frame and add it to every other frames to construct a
distracting video sample. Then we force the model to pull the feature of the
distracting video and the feature of the original video closer, so that the
model is explicitly restricted to resist the background influence, focusing
more on the motion changes. We term our method as \emph{Background Erasing}
(BE). It is worth noting that the implementation of our method is so simple and
neat and can be added to most of the SOTA methods without much efforts.
Specifically, BE brings 16.4% and 19.1% improvements with MoCo on the severely
biased datasets UCF101 and HMDB51, and 14.5% improvement on the less biased
dataset Diving48. | [
"cs.CV"
] |
We develop a Bayesian approach to learning from sequential data by using
Gaussian processes (GPs) with so-called signature kernels as covariance
functions. This allows to make sequences of different length comparable and to
rely on strong theoretical results from stochastic analysis. Signatures capture
sequential structure with tensors that can scale unfavourably in sequence
length and state space dimension. To deal with this, we introduce a sparse
variational approach with inducing tensors. We then combine the resulting GP
with LSTMs and GRUs to build larger models that leverage the strengths of each
of these approaches and benchmark the resulting GPs on multivariate time series
(TS) classification datasets. Code available at
https://github.com/tgcsaba/GPSig. | [
"stat.ML",
"cs.LG",
"math.PR"
] |
Artificial neural network has achieved the state-of-art performance in fault
detection on the Tennessee Eastman process, but it often requires enormous
memory to fund its massive parameters. In order to implement online real-time
fault detection, three deep compression techniques (pruning, clustering, and
quantization) are applied to reduce the computational burden. We have
extensively studied 7 different combinations of compression techniques, all
methods achieve high model compression rates over 64% while maintain high fault
detection accuracy. The best result is applying all three techniques, which
reduces the model sizes by 91.5% and remains a high accuracy over 94%. This
result leads to a smaller storage requirement in production environments, and
makes the deployment smoother in real world. | [
"cs.LG"
] |
Graph neural networks have recently achieved remarkable success in
representing graph-structured data, with rapid progress in both the node
embedding and graph pooling methods. Yet, they mostly focus on capturing
information from the nodes considering their connectivity, and not much work
has been done in representing the edges, which are essential components of a
graph. However, for tasks such as graph reconstruction and generation, as well
as graph classification tasks for which the edges are important for
discrimination, accurately representing edges of a given graph is crucial to
the success of the graph representation learning. To this end, we propose a
novel edge representation learning framework based on Dual Hypergraph
Transformation (DHT), which transforms the edges of a graph into the nodes of a
hypergraph. This dual hypergraph construction allows us to apply message
passing techniques for node representations to edges. After obtaining edge
representations from the hypergraphs, we then cluster or drop edges to obtain
holistic graph-level edge representations. We validate our edge representation
learning method with hypergraphs on diverse graph datasets for graph
representation and generation performance, on which our method largely
outperforms existing graph representation learning methods. Moreover, our edge
representation learning and pooling method also largely outperforms
state-of-the-art graph pooling methods on graph classification, not only
because of its accurate edge representation learning, but also due to its
lossless compression of the nodes and removal of irrelevant edges for effective
message passing. | [
"cs.LG"
] |
We propose a simple and efficient method for exploiting synthetic images when
training a Deep Network to predict a 3D pose from an image. The ability of
using synthetic images for training a Deep Network is extremely valuable as it
is easy to create a virtually infinite training set made of such images, while
capturing and annotating real images can be very cumbersome. However, synthetic
images do not resemble real images exactly, and using them for training can
result in suboptimal performance. It was recently shown that for exemplar-based
approaches, it is possible to learn a mapping from the exemplar representations
of real images to the exemplar representations of synthetic images. In this
paper, we show that this approach is more general, and that a network can also
be applied after the mapping to infer a 3D pose: At run time, given a real
image of the target object, we first compute the features for the image, map
them to the feature space of synthetic images, and finally use the resulting
features as input to another network which predicts the 3D pose. Since this
network can be trained very effectively by using synthetic images, it performs
very well in practice, and inference is faster and more accurate than with an
exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for
3D object pose estimation from color images, and the NYU dataset for 3D hand
pose estimation from depth maps. We show that it allows us to outperform the
state-of-the-art on both datasets. | [
"cs.CV"
] |
Reconciling symbolic and distributed representations is a crucial challenge
that can potentially resolve the limitations of current deep learning.
Remarkable advances in this direction have been achieved recently via
generative object-centric representation models. While learning a recognition
model that infers object-centric symbolic representations like bounding boxes
from raw images in an unsupervised way, no such model can provide another
important ability of a generative model, i.e., generating (sampling) according
to the structure of learned world density. In this paper, we propose Generative
Neurosymbolic Machines, a generative model that combines the benefits of
distributed and symbolic representations to support both structured
representations of symbolic components and density-based generation. These two
crucial properties are achieved by a two-layer latent hierarchy with the global
distributed latent for flexible density modeling and the structured symbolic
latent map. To increase the model flexibility in this hierarchical structure,
we also propose the StructDRAW prior. In experiments, we show that the proposed
model significantly outperforms the previous structured representation models
as well as the state-of-the-art non-structured generative models in terms of
both structure accuracy and image generation quality. Our code, datasets, and
trained models are available at https://github.com/JindongJiang/GNM | [
"cs.LG"
] |
Automated ranking of pre-trained Deep Neural Networks (DNNs) reduces the
required time for selecting optimal pre-trained DNN and boost the
classification performance in transfer learning. In this paper, we introduce a
novel algorithm to rank pre-trained DNNs by applying a straightforward
distance-based complexity measure named Separation Index (SI) to the target
dataset. For this purpose, at first, a background about the SI is given and
then the automated ranking algorithm is explained. In this algorithm, the SI is
computed for the target dataset which passes from the feature extracting parts
of pre-trained DNNs. Then, by descending sort of the computed SIs, the
pre-trained DNNs are ranked, easily. In this ranking method, the best DNN makes
maximum SI on the target dataset and a few pre-trained DNNs may be rejected in
the case of their sufficiently low computed SIs. The efficiency of the proposed
algorithm is evaluated by using three challenging datasets including Linnaeus
5, Breast Cancer Images, and COVID-CT. For the two first case studies, the
results of the proposed algorithm exactly match with the ranking of the trained
DNNs by the accuracy on the target dataset. For the third case study, despite
using different preprocessing on the target data, the ranking of the algorithm
has a high correlation with the ranking resulted from classification accuracy. | [
"cs.LG",
"stat.ML"
] |
Explainable artificial intelligence has been gaining attention in the past
few years. However, most existing methods are based on gradients or
intermediate features, which are not directly involved in the decision-making
process of the classifier. In this paper, we propose a slot attention-based
classifier called SCOUTER for transparent yet accurate classification. Two
major differences from other attention-based methods include: (a) SCOUTER's
explanation is involved in the final confidence for each category, offering
more intuitive interpretation, and (b) all the categories have their
corresponding positive or negative explanation, which tells "why the image is
of a certain category" or "why the image is not of a certain category." We
design a new loss tailored for SCOUTER that controls the model's behavior to
switch between positive and negative explanations, as well as the size of
explanatory regions. Experimental results show that SCOUTER can give better
visual explanations in terms of various metrics while keeping good accuracy on
small and medium-sized datasets. | [
"cs.CV"
] |
Point clouds can be represented in many forms (views), typically, point-based
sets, voxel-based cells or range-based images(i.e., panoramic view). The
point-based view is geometrically accurate, but it is disordered, which makes
it difficult to find local neighbors efficiently. The voxel-based view is
regular, but sparse, and computation grows cubically when voxel resolution
increases. The range-based view is regular and generally dense, however
spherical projection makes physical dimensions distorted. Both voxel- and
range-based views suffer from quantization loss, especially for voxels when
facing large-scale scenes. In order to utilize different view's advantages and
alleviate their own shortcomings in fine-grained segmentation task, we propose
a novel range-point-voxel fusion network, namely RPVNet. In this network, we
devise a deep fusion framework with multiple and mutual information
interactions among these three views and propose a gated fusion module (termed
as GFM), which can adaptively merge the three features based on concurrent
inputs. Moreover, the proposed RPV interaction mechanism is highly efficient,
and we summarize it into a more general formulation. By leveraging this
efficient interaction and relatively lower voxel resolution, our method is also
proved to be more efficient. Finally, we evaluated the proposed model on two
large-scale datasets, i.e., SemanticKITTI and nuScenes, and it shows
state-of-the-art performance on both of them. Note that, our method currently
ranks 1st on SemanticKITTI leaderboard without any extra tricks. | [
"cs.CV"
] |
Elastic similarity measures are a class of similarity measures specifically
designed to work with time series data. When scoring the similarity between two
time series, they allow points that do not correspond in timestamps to be
aligned. This can compensate for misalignments in the time axis of time series
data, and for similar processes that proceed at variable and differing paces.
Elastic similarity measures are widely used in machine learning tasks such as
classification, clustering and outlier detection when using time series data.
There is a multitude of research on various univariate elastic similarity
measures. However, except for multivariate versions of the well known Dynamic
Time Warping (DTW) there is a lack of work to generalise other similarity
measures for multivariate cases. This paper adapts two existing strategies used
in multivariate DTW, namely, Independent and Dependent DTW, to several commonly
used elastic similarity measures.
Using 23 datasets from the University of East Anglia (UEA) multivariate
archive, for nearest neighbour classification, we demonstrate that each measure
outperforms all others on at least one dataset and that there are datasets for
which either the dependent versions of all measures are more accurate than
their independent counterparts or vice versa. This latter finding suggests that
these differences arise from a fundamental property of the data. We also show
that an ensemble of such nearest neighbour classifiers is highly competitive
with other state-of-the-art multivariate time series classifiers. | [
"cs.LG",
"stat.ML",
"I.5.0; I.5.2; I.5.3"
] |
Kernels for structured data are commonly obtained by decomposing objects into
their parts and adding up the similarities between all pairs of parts measured
by a base kernel. Assignment kernels are based on an optimal bijection between
the parts and have proven to be an effective alternative to the established
convolution kernels. We explore how the base kernel can be learned as part of
the classification problem. We build on the theory of valid assignment kernels
derived from hierarchies defined on the parts. We show that the weights of this
hierarchy can be optimized via multiple kernel learning. We apply this result
to learn vertex similarities for the Weisfeiler-Lehman optimal assignment
kernel for graph classification. We present first experimental results which
demonstrate the feasibility and effectiveness of the approach. | [
"cs.LG",
"stat.ML"
] |
Gait recognition refers to the identification of individuals based on
features acquired from their body movement during walking. Despite the recent
advances in gait recognition with deep learning, variations in data acquisition
and appearance, namely camera angles, subject pose, occlusions, and clothing,
are challenging factors that need to be considered for achieving accurate gait
recognition systems. In this paper, we propose a network that first learns to
extract gait convolutional energy maps (GCEM) from frame-level convolutional
features. It then adopts a bidirectional recurrent neural network to learn from
split bins of the GCEM, thus exploiting the relations between learned partial
spatiotemporal representations. We then use an attention mechanism to
selectively focus on important recurrently learned partial representations as
identity information in different scenarios may lie in different GCEM bins. Our
proposed model has been extensively tested on two large-scale CASIA-B and
OU-MVLP gait datasets using four different test protocols and has been compared
to a number of state-of-the-art and baseline solutions. Additionally, a
comprehensive experiment has been performed to study the robustness of our
model in the presence of six different synthesized occlusions. The experimental
results show the superiority of our proposed method, outperforming the
state-of-the-art, especially in scenarios where different clothing and carrying
conditions are encountered. The results also revealed that our model is more
robust against different occlusions as compared to the state-of-the-art
methods. | [
"cs.CV",
"cs.LG"
] |
Leveraging domain knowledge including fingerprints and functional groups in
molecular representation learning is crucial for chemical property prediction
and drug discovery. When modeling the relation between graph structure and
molecular properties implicitly, existing works can hardly capture structural
or property changes and complex structure, with much smaller atom vocabulary
and highly frequent atoms. In this paper, we propose the Contrastive
Knowledge-aware GNN (CKGNN) for self-supervised molecular representation
learning to fuse domain knowledge into molecular graph representation. We
explicitly encode domain knowledge via knowledge-aware molecular encoder under
the contrastive learning framework, ensuring that the generated molecular
embeddings equipped with chemical domain knowledge to distinguish molecules
with similar chemical formula but dissimilar functions. Extensive experiments
on 8 public datasets demonstrate the effectiveness of our model with a 6\%
absolute improvement on average against strong competitors. Ablation study and
further investigation also verify the best of both worlds: incorporation of
chemical domain knowledge into self-supervised learning. | [
"cs.LG",
"q-bio.QM"
] |
This paper is on image and face super-resolution. The vast majority of prior
work for this problem focus on how to increase the resolution of low-resolution
images which are artificially generated by simple bilinear down-sampling (or in
a few cases by blurring followed by down-sampling).We show that such methods
fail to produce good results when applied to real-world low-resolution, low
quality images. To circumvent this problem, we propose a two-stage process
which firstly trains a High-to-Low Generative Adversarial Network (GAN) to
learn how to degrade and downsample high-resolution images requiring, during
training, only unpaired high and low-resolution images. Once this is achieved,
the output of this network is used to train a Low-to-High GAN for image
super-resolution using this time paired low- and high-resolution images. Our
main result is that this network can be now used to efectively increase the
quality of real-world low-resolution images. We have applied the proposed
pipeline for the problem of face super-resolution where we report large
improvement over baselines and prior work although the proposed method is
potentially applicable to other object categories. | [
"cs.CV"
] |
Deep generative architectures provide a way to model not only images but also
complex, 3-dimensional objects, such as point clouds. In this work, we present
a novel method to obtain meaningful representations of 3D shapes that can be
used for challenging tasks including 3D points generation, reconstruction,
compression, and clustering. Contrary to existing methods for 3D point cloud
generation that train separate decoupled models for representation learning and
generation, our approach is the first end-to-end solution that allows to
simultaneously learn a latent space of representation and generate 3D shape out
of it. Moreover, our model is capable of learning meaningful compact binary
descriptors with adversarial training conducted on a latent space. To achieve
this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D
input and create 3D output. Thanks to our end-to-end training regime, the
resulting method called 3D Adversarial Autoencoder (3dAAE) obtains either
binary or continuous latent space that covers a much wider portion of training
data distribution. Finally, our quantitative evaluation shows that 3dAAE
provides state-of-the-art results for 3D points clustering and 3D object
retrieval. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Most of state of the art methods applied on time series consist of deep
learning methods that are too complex to be interpreted. This lack of
interpretability is a major drawback, as several applications in the real world
are critical tasks, such as the medical field or the autonomous driving field.
The explainability of models applied on time series has not gather much
attention compared to the computer vision or the natural language processing
fields. In this paper, we present an overview of existing explainable AI (XAI)
methods applied on time series and illustrate the type of explanations they
produce. We also provide a reflection on the impact of these explanation
methods to provide confidence and trust in the AI systems. | [
"cs.LG",
"cs.AI"
] |
We introduce an automated tool for deploying ultra low-latency, low-power
deep neural networks with convolutional layers on FPGAs. By extending the
hls4ml library, we demonstrate an inference latency of $5\,\mu$s using
convolutional architectures, targeting microsecond latency applications like
those at the CERN Large Hadron Collider. Considering benchmark models trained
on the Street View House Numbers Dataset, we demonstrate various methods for
model compression in order to fit the computational constraints of a typical
FPGA device used in trigger and data acquisition systems of particle detectors.
In particular, we discuss pruning and quantization-aware training, and
demonstrate how resource utilization can be significantly reduced with little
to no loss in model accuracy. We show that the FPGA critical resource
consumption can be reduced by 97% with zero loss in model accuracy, and by 99%
when tolerating a 6% accuracy degradation. | [
"cs.LG",
"cs.CV",
"hep-ex",
"physics.ins-det",
"stat.ML"
] |
We develop an approach for unsupervised learning of associations between
co-occurring perceptual events using a large graph. We applied this approach to
successfully solve the image captcha of China's railroad system. The approach
is based on the principle of suspicious coincidence. In this particular
problem, a user is presented with a deformed picture of a Chinese phrase and
eight low-resolution images. They must quickly select the relevant images in
order to purchase their train tickets. This problem presents several
challenges: (1) the teaching labels for both the Chinese phrases and the images
were not available for supervised learning, (2) no pre-trained deep
convolutional neural networks are available for recognizing these Chinese
phrases or the presented images, and (3) each captcha must be solved within a
few seconds. We collected 2.6 million captchas, with 2.6 million deformed
Chinese phrases and over 21 million images. From these data, we constructed an
association graph, composed of over 6 million vertices, and linked these
vertices based on co-occurrence information and feature similarity between
pairs of images. We then trained a deep convolutional neural network to learn a
projection of the Chinese phrases onto a 230-dimensional latent space. Using
label propagation, we computed the likelihood of each of the eight images
conditioned on the latent space projection of the deformed phrase for each
captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on
average. Our work, in answering this practical challenge, illustrates the power
of this class of unsupervised association learning techniques, which may be
related to the brain's general strategy for associating language stimuli with
visual objects on the principle of suspicious coincidence. | [
"cs.CV"
] |
Not all supervised learning problems are described by a pair of a fixed-size
input tensor and a label. In some cases, especially in medical image analysis,
a label corresponds to a bag of instances (e.g. image patches), and to classify
such bag, aggregation of information from all of the instances is needed. There
have been several attempts to create a model working with a bag of instances,
however, they are assuming that there are no dependencies within the bag and
the label is connected to at least one instance. In this work, we introduce
Self-Attention Attention-based MIL Pooling (SA-AbMILP) aggregation operation to
account for the dependencies between instances. We conduct several experiments
on MNIST, histological, microbiological, and retinal databases to show that
SA-AbMILP performs better than other models. Additionally, we investigate
kernel variations of Self-Attention and their influence on the results. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Solving heat transfer equations on chip becomes very critical in the upcoming
5G and AI chip-package-systems. However, batches of simulations have to be
performed for data driven supervised machine learning models. Data driven
methods are data hungry, to address this, Physics Informed Neural Networks
(PINN) have been proposed. However, vanilla PINN models solve one fixed heat
equation at a time, so the models have to be retrained for heat equations with
different source terms. Additionally, issues related to multi-objective
optimization have to be resolved while using PINN to minimize the PDE residual,
satisfy boundary conditions and fit the observed data etc. Therefore, this
paper investigates an unsupervised learning approach for solving heat transfer
equations on chip without using solution data and generalizing the trained
network for predicting solutions for heat equations with unseen source terms.
Specifically, a hybrid framework of Auto Encoder (AE) and Image Gradient (IG)
based network is designed. The AE is used to encode different source terms of
the heat equations. The IG based network implements a second order central
difference algorithm for structured grids and minimizes the PDE residual. The
effectiveness of the designed network is evaluated by solving heat equations
for various use cases. It is proved that with limited number of source terms to
train the AE network, the framework can not only solve the given heat transfer
problems with a single training process, but also make reasonable predictions
for unseen cases (heat equations with new source terms) without retraining. | [
"cs.LG",
"physics.app-ph",
"physics.comp-ph",
"stat.ML"
] |
A video-grounded dialogue system referred to as the Structured Co-reference
Graph Attention (SCGA) is presented for decoding the answer sequence to a
question regarding a given video while keeping track of the dialogue context.
Although recent efforts have made great strides in improving the quality of the
response, performance is still far from satisfactory. The two main challenging
issues are as follows: (1) how to deduce co-reference among multiple modalities
and (2) how to reason on the rich underlying semantic structure of video with
complex spatial and temporal dynamics. To this end, SCGA is based on (1)
Structured Co-reference Resolver that performs dereferencing via building a
structured graph over multiple modalities, (2) Spatio-temporal Video Reasoner
that captures local-to-global dynamics of video via gradually neighboring graph
attention. SCGA makes use of pointer network to dynamically replicate parts of
the question for decoding the answer sequence. The validity of the proposed
SCGA is demonstrated on AVSD@DSTC7 and AVSD@DSTC8 datasets, a challenging
video-grounded dialogue benchmarks, and TVQA dataset, a large-scale videoQA
benchmark. Our empirical results show that SCGA outperforms other
state-of-the-art dialogue systems on both benchmarks, while extensive ablation
study and qualitative analysis reveal performance gain and improved
interpretability. | [
"cs.CV"
] |
This paper presents a novel approach to the technical analysis of wireheading
in intelligent agents. Inspired by the natural analogues of wireheading and
their prevalent manifestations, we propose the modeling of such phenomenon in
Reinforcement Learning (RL) agents as psychological disorders. In a preliminary
step towards evaluating this proposal, we study the feasibility and dynamics of
emergent addictive policies in Q-learning agents in the tractable environment
of the game of Snake. We consider a slightly modified settings for this game,
in which the environment provides a "drug" seed alongside the original
"healthy" seed for the consumption of the snake. We adopt and extend an
RL-based model of natural addiction to Q-learning agents in this settings, and
derive sufficient parametric conditions for the emergence of addictive
behaviors in such agents. Furthermore, we evaluate our theoretical analysis
with three sets of simulation-based experiments. The results demonstrate the
feasibility of addictive wireheading in RL agents, and provide promising venues
of further research on the psychopathological modeling of complex AI safety
problems. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Efficient and effective learning is one of the ultimate goals of the deep
reinforcement learning (DRL), although the compromise has been made in most of
the time, especially for the application of robot manipulations. Learning is
always expensive for robot manipulation tasks and the learning effectiveness
could be affected by the system uncertainty. In order to solve above
challenges, in this study, we proposed a simple but powerful reward shaping
method, namely Dense2Sparse. It combines the advantage of fast convergence of
dense reward and the noise isolation of the sparse reward, to achieve a balance
between learning efficiency and effectiveness, which makes it suitable for
robot manipulation tasks. We evaluated our Dense2Sparse method with a series of
ablation experiments using the state representation model with system
uncertainty. The experiment results show that the Dense2Sparse method obtained
higher expected reward compared with the ones using standalone dense reward or
sparse reward, and it also has a superior tolerance of system uncertainty. | [
"cs.LG",
"stat.ML"
] |
The Generative Adversarial Network framework has shown success in implicitly
modeling data distributions and is able to generate realistic samples. Its
architecture is comprised of a generator, which produces fake data that
superficially seem to belong to the real data distribution, and a discriminator
which is to distinguish fake from genuine samples. The Noiseless Joint Plug &
Play model offers an extension to the framework by simultaneously training
autoencoders. This model uses a pre-trained encoder as a feature extractor,
feeding the generator with global information. Using the Plug & Play network as
baseline, we design a new model by adding discriminators to the Plug & Play
architecture. These additional discriminators are trained to discern real and
fake latent codes, which are the output of the encoder using genuine and
generated inputs, respectively. We proceed to investigate whether this approach
is viable. Experiments conducted for the MNIST manifold show that this indeed
is the case. | [
"cs.CV"
] |
Face representation learning solutions have recently achieved great success
for various applications such as verification and identification. However, face
recognition approaches that are based purely on RGB images rely solely on
intensity information, and therefore are more sensitive to facial variations,
notably pose, occlusions, and environmental changes such as illumination and
background. A novel depth-guided attention mechanism is proposed for deep
multi-modal face recognition using low-cost RGB-D sensors. Our novel attention
mechanism directs the deep network "where to look" for visual features in the
RGB image by focusing the attention of the network using depth features
extracted by a Convolution Neural Network (CNN). The depth features help the
network focus on regions of the face in the RGB image that contains more
prominent person-specific information. Our attention mechanism then uses this
correlation to generate an attention map for RGB images from the depth features
extracted by CNN. We test our network on four public datasets, showing that the
features obtained by our proposed solution yield better results on the
Lock3DFace, CurtinFaces, IIIT-D RGB-D, and KaspAROV datasets which include
challenging variations in pose, occlusion, illumination, expression, and
time-lapse. Our solution achieves average (increased) accuracies of 87.3\%
(+5.0\%), 99.1\% (+0.9\%), 99.7\% (+0.6\%) and 95.3\%(+0.5\%) for the four
datasets respectively, thereby improving the state-of-the-art. We also perform
additional experiments with thermal images, instead of depth images, showing
the high generalization ability of our solution when adopting other modalities
for guiding the attention mechanism instead of depth information | [
"cs.CV"
] |
Recently, deep neural networks have achieved impressive performance in terms
of both reconstruction accuracy and efficiency for single image
super-resolution (SISR). However, the network model of these methods is a fully
convolutional neural network, which is limit to exploit the differentiated
contextual information over the global region of the input image because of the
weight sharing in convolution height and width extent. In this paper, we
discuss a new SISR architecture where features are extracted in the
low-resolution (LR) space, and then we use a fully connected layer which learns
an array of differentiated upsampling weights to reconstruct the desired
high-resolution (HR) image from the final obtained LR features. By doing so, we
effectively exploit the differentiated contextual information over the whole
input image region, whilst maintaining the low computational complexity for the
overall SR operations. In addition, we introduce an edge difference constraint
into our loss function to preserve edges and texture structures. Extensive
experiments validate that our SISR method outperforms the existing
state-of-the-art methods. | [
"cs.CV"
] |
Scene text instances found in natural images carry explicit semantic
information that can provide important cues to solve a wide array of computer
vision problems. In this paper, we focus on leveraging multi-modal content in
the form of visual and textual cues to tackle the task of fine-grained image
classification and retrieval. First, we obtain the text instances from images
by employing a text reading system. Then, we combine textual features with
salient image regions to exploit the complementary information carried by the
two sources. Specifically, we employ a Graph Convolutional Network to perform
multi-modal reasoning and obtain relationship-enhanced features by learning a
common semantic space between salient objects and text found in an image. By
obtaining an enhanced set of visual and textual features, the proposed model
greatly outperforms the previous state-of-the-art in two different tasks,
fine-grained classification and image retrieval in the Con-Text and Drink
Bottle datasets. | [
"cs.CV"
] |
The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to
developing new solutions for analyzing graphs and sparse data. Sparse AI
analytics present unique scalability difficulties. The Sparse Deep Neural
Network (DNN) Challenge draws upon prior challenges from machine learning, high
performance computing, and visual analytics to create a challenge that is
reflective of emerging sparse AI systems. The sparse DNN challenge is based on
a mathematically well-defined DNN inference computation and can be implemented
in any programming environment. In 2019 several sparse DNN challenge
submissions were received from a wide range of authors and organizations. This
paper presents a performance analysis of the best performers of these
submissions. These submissions show that their state-of-the-art sparse DNN
execution time, $T_{\rm DNN}$, is a strong function of the number of DNN
operations performed, $N_{\rm op}$. The sparse DNN challenge provides a clear
picture of current sparse DNN systems and underscores the need for new
innovations to achieve high performance on very large sparse DNNs. | [
"cs.LG",
"cs.CV",
"cs.NE",
"stat.ML"
] |
Value functions are crucial for model-free Reinforcement Learning (RL) to
obtain a policy implicitly or guide the policy updates. Value estimation
heavily depends on the stochasticity of environmental dynamics and the quality
of reward signals. In this paper, we propose a two-step understanding of value
estimation from the perspective of future prediction, through decomposing the
value function into a reward-independent future dynamics part and a
policy-independent trajectory return part. We then derive a practical deep RL
algorithm from the above decomposition, consisting of a convolutional
trajectory representation model, a conditional variational dynamics model to
predict the expected representation of future trajectory and a convex
trajectory return model that maps a trajectory representation to its return.
Our algorithm is evaluated in MuJoCo continuous control tasks and shows
superior results under both common settings and delayed reward settings. | [
"cs.LG",
"stat.ML"
] |
Pose estimation is a fundamental building block for robotic applications such
as autonomous vehicles, UAV, and large scale augmented reality. It is also a
prohibitive factor for those applications to be in mass production, since the
state-of-the-art, centimeter-level pose estimation often requires long mapping
procedures and expensive localization sensors, e.g. LiDAR and high precision
GPS/IMU, etc. To overcome the cost barrier, we propose a neural network based
solution to localize a consumer degree RGB camera within a prior sparse LiDAR
map with comparable centimeter-level precision. We achieved it by introducing a
novel network module, which we call resistor module, to enforce the network
generalize better, predicts more accurately, and converge faster. Such results
are benchmarked by several datasets we collected in the large scale indoor
parking garage scenes. We plan to open both the data and the code for the
community to join the effort to advance this field. | [
"cs.CV",
"cs.RO",
"eess.IV"
] |
Video is an essential imaging modality for diagnostics, e.g. in ultrasound
imaging, for endoscopy, or movement assessment. However, video hasn't received
a lot of attention in the medical image analysis community. In the clinical
practice, it is challenging to utilise raw diagnostic video data efficiently as
video data takes a long time to process, annotate or audit. In this paper we
introduce a novel, fully automatic video summarization method that is tailored
to the needs of medical video data. Our approach is framed as reinforcement
learning problem and produces agents focusing on the preservation of important
diagnostic information. We evaluate our method on videos from fetal ultrasound
screening, where commonly only a small amount of the recorded data is used
diagnostically. We show that our method is superior to alternative video
summarization methods and that it preserves essential information required by
clinical diagnostic standards. | [
"cs.CV"
] |
Traditional clustering methods are limited when dealing with huge and
heterogeneous groups of gene expression data, which motivates the development
of bi-clustering methods. Bi-clustering methods are used to mine bi-clusters
whose subsets of samples (genes) are co-regulated under their test conditions.
Studies show that mining bi-clusters of consistent trends and trends with
similar degrees of fluctuations from the gene expression data is essential in
bioinformatics research. Unfortunately, traditional bi-clustering methods are
not fully effective in discovering such bi-clusters. Therefore, we propose a
novel bi-clustering method by involving here the theory of Granular Computing.
In the proposed scheme, the gene data matrix, considered as a group of time
series, is transformed into a series of ordered information granules. With the
information granules we build a characteristic matrix of the gene data to
capture the fluctuation trend of the expression value between consecutive
conditions to mine the ideal bi-clusters. The experimental results are in
agreement with the theoretical analysis, and show the excellent performance of
the proposed method. | [
"cs.CV",
"cs.AI",
"eess.SP"
] |
Value Iteration Networks (VINs) have emerged as a popular method to
incorporate planning algorithms within deep reinforcement learning, enabling
performance improvements on tasks requiring long-range reasoning and
understanding of environment dynamics. This came with several limitations,
however: the model is not incentivised in any way to perform meaningful
planning computations, the underlying state space is assumed to be discrete,
and the Markov decision process (MDP) is assumed fixed and known. We propose
eXecuted Latent Value Iteration Networks (XLVINs), which combine recent
developments across contrastive self-supervised learning, graph representation
learning and neural algorithmic reasoning to alleviate all of the above
limitations, successfully deploying VIN-style models on generic environments.
XLVINs match the performance of VIN-like models when the underlying MDP is
discrete, fixed and known, and provides significant improvements to model-free
baselines across three general MDP setups. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
The description of rocks is one of the most time-consuming tasks in the
everyday work of a geologist, especially when very accurate description is
required. We here present a method that reduces the time needed for accurate
description of rocks, enabling the geologist to work more efficiently. We
describe the application of methods based on color distribution analysis and
feature extraction. Then we focus on a new approach, used by us, which is based
on convolutional neural networks. We used several well-known neural network
architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their
performance. The precision of the algorithms is up to 95% on the validation set
with GoogLeNet architecture. The best of the proposed algorithms can describe
50 m of full-size core in one minute. | [
"cs.CV",
"cs.LG",
"I.4.8; I.4.10"
] |
Recently, referring image segmentation has aroused widespread interest.
Previous methods perform the multi-modal fusion between language and vision at
the decoding side of the network. And, linguistic feature interacts with visual
feature of each scale separately, which ignores the continuous guidance of
language to multi-scale visual features. In this work, we propose an encoder
fusion network (EFN), which transforms the visual encoder into a multi-modal
feature learning network, and uses language to refine the multi-modal features
progressively. Moreover, a co-attention mechanism is embedded in the EFN to
realize the parallel update of multi-modal features, which can promote the
consistent of the cross-modal information representation in the semantic space.
Finally, we propose a boundary enhancement module (BEM) to make the network pay
more attention to the fine structure. The experiment results on four benchmark
datasets demonstrate that the proposed approach achieves the state-of-the-art
performance under different evaluation metrics without any post-processing. | [
"cs.CV"
] |
Most object detectors contain two important components: a feature extractor
and an object classifier. The feature extractor has rapidly evolved with
significant research efforts leading to better deep convolutional
architectures. The object classifier, however, has not received much attention
and many recent systems (like SPPnet and Fast/Faster R-CNN) use simple
multi-layer perceptrons. This paper demonstrates that carefully designing deep
networks for object classification is just as important. We experiment with
region-wise classifier networks that use shared, region-independent
convolutional features. We call them "Networks on Convolutional feature maps"
(NoCs). We discover that aside from deep feature maps, a deep and convolutional
per-region classifier is of particular importance for object detection, whereas
latest superior image classification models (such as ResNets and GoogLeNets) do
not directly lead to good detection accuracy without using such a per-region
classifier. We show by experiments that despite the effective ResNets and
Faster R-CNN systems, the design of NoCs is an essential element for the
1st-place winning entries in ImageNet and MS COCO challenges 2015. | [
"cs.CV"
] |
Extraction and recognition of Bangla text from video frame images is
challenging due to complex color background, low-resolution etc. In this paper,
we propose an algorithm for extraction and recognition of Bangla text form such
video frames with complex background. Here, a two-step approach has been
proposed. First, the text line is segmented into words using information based
on line contours. First order gradient value of the text blocks are used to
find the word gap. Next, a local binarization technique is applied on each word
and text line is reconstructed using those words. Secondly, this binarized text
block is sent to OCR for recognition purpose. | [
"cs.CV"
] |
In this paper we discuss policy iteration methods for approximate solution of
a finite-state discounted Markov decision problem, with a focus on
feature-based aggregation methods and their connection with deep reinforcement
learning schemes. We introduce features of the states of the original problem,
and we formulate a smaller "aggregate" Markov decision problem, whose states
relate to the features. We discuss properties and possible implementations of
this type of aggregation, including a new approach to approximate policy
iteration. In this approach the policy improvement operation combines
feature-based aggregation with feature construction using deep neural networks
or other calculations. We argue that the cost function of a policy may be
approximated much more accurately by the nonlinear function of the features
provided by aggregation, than by the linear function of the features provided
by neural network-based reinforcement learning, thereby potentially leading to
more effective policy improvement. | [
"cs.LG",
"stat.ML",
"49, 90, 93"
] |
This paper describes a fast and accurate semantic image segmentation approach
that encodes not only the discriminative features from deep neural networks,
but also the high-order context compatibility among adjacent objects as well as
low level image features. We formulate the underlying problem as the
conditional random field that embeds local feature extraction, clique potential
construction, and guided filtering within the same framework, and provide an
efficient coarse-to-fine solver. At the coarse level, we combine local feature
representation and context interaction using a deep convolutional network, and
directly learn the interaction from high order cliques with a message passing
routine, avoiding time-consuming explicit graph inference for joint probability
distribution. At the fine level, we introduce a guided filtering interpretation
for the mean field algorithm, and achieve accurate object boundaries with 100+
faster than classic learning methods. The two parts are connected and jointly
trained in an end-to-end fashion. Experimental results on Pascal VOC 2012
dataset have shown that the proposed algorithm outperforms the
state-of-the-art, and that it achieves the rank 1 performance at the time of
submission, both of which prove the effectiveness of this unified framework for
semantic image segmentation. | [
"cs.CV"
] |
Stories are essential for genealogy research since they can help build
emotional connections with people. A lot of family stories are reserved in
historical photos and albums. Recent development on image captioning models
makes it feasible to "tell stories" for photos automatically. The attention
mechanism has been widely adopted in many state-of-the-art encoder-decoder
based image captioning models, since it can bridge the gap between the visual
part and the language part. Most existing captioning models implicitly trained
attention modules with word-likelihood loss. Meanwhile, lots of studies have
investigated intrinsic attentions for visual models using gradient-based
approaches. Ideally, attention maps predicted by captioning models should be
consistent with intrinsic attentions from visual models for any given visual
concept. However, no work has been done to align implicitly learned attention
maps with intrinsic visual attentions. In this paper, we proposed a novel model
that measured consistency between captioning predicted attentions and intrinsic
visual attentions. This alignment loss allows explicit attention correction
without using any expensive bounding box annotations. We developed and
evaluated our model on COCO dataset as well as a genealogical dataset from
Ancestry.com Operations Inc., which contains billions of historical photos. The
proposed model achieved better performances on all commonly used language
evaluation metrics for both datasets. | [
"cs.CV"
] |
A user's eyes provide means for Human Computer Interaction (HCI) research as
an important modal. The time to time scientific explorations of the eye has
already seen an upsurge of the benefits in HCI applications from gaze
estimation to the measure of attentiveness of a user looking at a screen for a
given time period. The eye tracking system as an assisting, interactive tool
can be incorporated by physically disabled individuals, fitted best for those
who have eyes as only a limited set of communication. The threefold objective
of this paper is - 1. To introduce a neural network based architecture to
predict users' gaze at 9 positions displayed in the 11.31{\deg} visual range on
the screen, through a low resolution based system such as a webcam in real time
by learning various aspects of eyes as an ocular feature set. 2.A collection of
coarsely supervised feature set obtained in real time which is also validated
through the user case study presented in the paper for 21 individuals ( 17 men
and 4 women ) from whom a 35k set of instances was derived with an accuracy
score of 82.36% and f1_score of 82.2% and 3.A detailed study over applicability
and underlying challenges of such systems. The experimental results verify the
feasibility and validity of the proposed eye gaze tracking model. | [
"cs.CV",
"cs.HC",
"cs.LG"
] |
ProductNet is a collection of high-quality product datasets for better
product understanding. Motivated by ImageNet, ProductNet aims at supporting
product representation learning by curating product datasets of high quality
with properly chosen taxonomy. In this paper, the two goals of building
high-quality product datasets and learning product representation support each
other in an iterative fashion: the product embedding is obtained via a
multi-modal deep neural network (master model) designed to leverage product
image and catalog information; and in return, the embedding is utilized via
active learning (local model) to vastly accelerate the annotation process. For
the labeled data, the proposed master model yields high categorization accuracy
(94.7% top-1 accuracy for 1240 classes), which can be used as search indices,
partition keys, and input features for machine learning models. The product
embedding, as well as the fined-tuned master model for a specific business
task, can also be used for various transfer learning tasks. | [
"cs.LG",
"cs.CL",
"cs.CV",
"stat.ML"
] |
Bootstrapping provides a flexible and effective approach for assessing the
quality of batch reinforcement learning, yet its theoretical property is less
understood. In this paper, we study the use of bootstrapping in off-policy
evaluation (OPE), and in particular, we focus on the fitted Q-evaluation (FQE)
that is known to be minimax-optimal in the tabular and linear-model cases. We
propose a bootstrapping FQE method for inferring the distribution of the policy
evaluation error and show that this method is asymptotically efficient and
distributionally consistent for off-policy statistical inference. To overcome
the computation limit of bootstrapping, we further adapt a subsampling
procedure that improves the runtime by an order of magnitude. We numerically
evaluate the bootrapping method in classical RL environments for confidence
interval estimation, estimating the variance of off-policy evaluator, and
estimating the correlation between multiple off-policy evaluators. | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] |
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