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Semantic Segmentation using deep convolutional neural network pose more
complex challenge for any GPU intensive task. As it has to compute million of
parameters, it results to huge memory consumption. Moreover, extracting finer
features and conducting supervised training tends to increase the complexity.
With the introduction of Fully Convolutional Neural Network, which uses finer
strides and utilizes deconvolutional layers for upsampling, it has been a go to
for any image segmentation task. In this paper, we propose two segmentation
architecture which not only needs one-third the parameters to compute but also
gives better accuracy than the similar architectures. The model weights were
transferred from the popular neural net like VGG19 and VGG16 which were trained
on Imagenet classification data-set. Then we transform all the fully connected
layers to convolutional layers and use dilated convolution for decreasing the
parameters. Lastly, we add finer strides and attach four skip architectures
which are element-wise summed with the deconvolutional layers in steps. We
train and test on different sparse and fine data-sets like Pascal VOC2012,
Pascal-Context and NYUDv2 and show how better our model performs in this tasks.
On the other hand our model has a faster inference time and consumes less
memory for training and testing on NVIDIA Pascal GPUs, making it more efficient
and less memory consuming architecture for pixel-wise segmentation. | [
"cs.CV"
] |
We design a new provably efficient algorithm for episodic reinforcement
learning with generalized linear function approximation. We analyze the
algorithm under a new expressivity assumption that we call "optimistic
closure," which is strictly weaker than assumptions from prior analyses for the
linear setting. With optimistic closure, we prove that our algorithm enjoys a
regret bound of $\tilde{O}(\sqrt{d^3 T})$ where $d$ is the dimensionality of
the state-action features and $T$ is the number of episodes. This is the first
statistically and computationally efficient algorithm for reinforcement
learning with generalized linear functions. | [
"stat.ML",
"cs.LG"
] |
We present an image segmentation method that iteratively evolves a polygon.
At each iteration, the vertices of the polygon are displaced based on the local
value of a 2D shift map that is inferred from the input image via an
encoder-decoder architecture. The main training loss that is used is the
difference between the polygon shape and the ground truth segmentation mask.
The network employs a neural renderer to create the polygon from its vertices,
making the process fully differentiable. We demonstrate that our method
outperforms the state of the art segmentation networks and deep active contour
solutions in a variety of benchmarks, including medical imaging and aerial
images. Our code is available at https://github.com/shirgur/ACDRNet. | [
"cs.CV"
] |
This work extends the analysis of the theoretical results presented within
the paper Is Q-Learning Provably Efficient? by Jin et al. We include a survey
of related research to contextualize the need for strengthening the theoretical
guarantees related to perhaps the most important threads of model-free
reinforcement learning. We also expound upon the reasoning used in the proofs
to highlight the critical steps leading to the main result showing that
Q-learning with UCB exploration achieves a sample efficiency that matches the
optimal regret that can be achieved by any model-based approach. | [
"cs.LG",
"cs.AI",
"math.OC",
"stat.ML"
] |
Image captioning has increasingly large domains of application, and fashion
is not an exception. Having automatic item descriptions is of great interest
for fashion web platforms hosting sometimes hundreds of thousands of images.
This paper is one of the first tackling image captioning for fashion images. To
contribute addressing dataset diversity issues, we introduced the InFashAIv1
dataset containing almost 16.000 African fashion item images with their titles,
prices and general descriptions. We also used the well known DeepFashion
dataset in addition to InFashAIv1. Captions are generated using the Show and
Tell model made of CNN encoder and RNN Decoder. We showed that jointly training
the model on both datasets improves captions quality for African style fashion
images, suggesting a transfer learning from Western style data. The InFashAIv1
dataset is released on Github to encourage works with more diversity inclusion. | [
"cs.CV",
"cs.AI",
"I.2.10; I.2.7; I.4.10"
] |
We propose a fully distributed actor-critic algorithm approximated by deep
neural networks, named \textit{Diff-DAC}, with application to single-task and
to average multitask reinforcement learning (MRL). Each agent has access to
data from its local task only, but it aims to learn a policy that performs well
on average for the whole set of tasks. During the learning process, agents
communicate their value-policy parameters to their neighbors, diffusing the
information across the network, so that they converge to a common policy, with
no need for a central node. The method is scalable, since the computational and
communication costs per agent grow with its number of neighbors. We derive
Diff-DAC's from duality theory and provide novel insights into the standard
actor-critic framework, showing that it is actually an instance of the dual
ascent method that approximates the solution of a linear program. Experiments
suggest that Diff-DAC can outperform the single previous distributed MRL
approach (i.e., Dist-MTLPS) and even the centralized architecture. | [
"cs.LG",
"cs.MA",
"math.OC",
"stat.ML"
] |
Correspondences between k-tuples of points are key in multiple view geometry
and motion analysis. Regular transformations are posed by homographies between
two projective planes that serves as structural models for images. Such
transformations can not include degenerate situations. Fundamental or essential
matrices expand homographies with structural information by using degenerate
bilinear maps. The projectivization of the endomorphisms of a three-dimensional
vector space includes all of them. Hence, they are able to explain a wider
range of eventually degenerate transformations between arbitrary pairs of
views. To include these degenerate situations, this paper introduces a
completion of bilinear maps between spaces given by an equivariant
compactification of regular transformations. This completion is extensible to
the varieties of fundamental and essential matrices, where most methods based
on regular transformations fail. The construction of complete endomorphisms
manages degenerate projection maps using a simultaneous action on source and
target spaces. In such way, this mathematical construction provides a robust
framework to relate corresponding views in multiple view geometry. | [
"cs.CV",
"cs.RO"
] |
Deep reinforcement learning provides a promising approach for vision-based
control of real-world robots. However, the generalization of such models
depends critically on the quantity and variety of data available for training.
This data can be difficult to obtain for some types of robotic systems, such as
fragile, small-scale quadrotors. Simulated rendering and physics can provide
for much larger datasets, but such data is inherently of lower quality: many of
the phenomena that make the real-world autonomous flight problem challenging,
such as complex physics and air currents, are modeled poorly or not at all, and
the systematic differences between simulation and the real world are typically
impossible to eliminate. In this work, we investigate how data from both
simulation and the real world can be combined in a hybrid deep reinforcement
learning algorithm. Our method uses real-world data to learn about the dynamics
of the system, and simulated data to learn a generalizable perception system
that can enable the robot to avoid collisions using only a monocular camera. We
demonstrate our approach on a real-world nano aerial vehicle collision
avoidance task, showing that with only an hour of real-world data, the
quadrotor can avoid collisions in new environments with various lighting
conditions and geometry. Code, instructions for building the aerial vehicles,
and videos of the experiments can be found at github.com/gkahn13/GtS | [
"cs.LG",
"cs.RO",
"stat.ML"
] |
Purpose: Accurate estimation of the position and orientation (pose) of
surgical instruments is crucial for delicate minimally invasive temporal bone
surgery. Current techniques lack in accuracy and/or line-of-sight constraints
(conventional tracking systems) or expose the patient to prohibitive ionizing
radiation (intra-operative CT). A possible solution is to capture the
instrument with a c-arm at irregular intervals and recover the pose from the
image.
Methods: i3PosNet infers the position and orientation of instruments from
images using a pose estimation network. Said framework considers localized
patches and outputs pseudo-landmarks. The pose is reconstructed from
pseudo-landmarks by geometric considerations.
Results: We show i3PosNet reaches errors less than 0.05mm. It outperforms
conventional image registration-based approaches reducing average and maximum
errors by at least two thirds. i3PosNet trained on synthetic images generalizes
to real x-rays without any further adaptation.
Conclusion: The translation of Deep Learning based methods to surgical
applications is difficult, because large representative datasets for training
and testing are not available. This work empirically shows sub-millimeter pose
estimation trained solely based on synthetic training data. | [
"cs.CV",
"eess.IV"
] |
Despite the advancement in the domain of audio and audio-visual speech
recognition, visual speech recognition systems are still quite under-explored
due to the visual ambiguity of some phonemes. In this work, we propose a new
lip-reading model that combines three contributions. First, the model front-end
adopts a spatio-temporal attention mechanism to help extract the informative
data from the input visual frames. Second, the model back-end utilizes a
sequence-level and frame-level Knowledge Distillation (KD) techniques that
allow leveraging audio data during the visual model training. Third, a data
preprocessing pipeline is adopted that includes facial landmarks
detection-based lip-alignment. On LRW lip-reading dataset benchmark, a
noticeable accuracy improvement is demonstrated; the spatio-temporal attention,
Knowledge Distillation, and lip-alignment contributions achieved 88.43%,
88.64%, and 88.37% respectively. | [
"cs.CV",
"cs.AI"
] |
We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for
highly accurate real-time visual odometry estimation of large-scale
environments from stereo cameras. It jointly optimizes for all the model
parameters within the active window, including the intrinsic/extrinsic camera
parameters of all keyframes and the depth values of all selected pixels. In
particular, we propose a novel approach to integrate constraints from static
stereo into the bundle adjustment pipeline of temporal multi-view stereo.
Real-time optimization is realized by sampling pixels uniformly from image
regions with sufficient intensity gradient. Fixed-baseline stereo resolves
scale drift. It also reduces the sensitivities to large optical flow and to
rolling shutter effect which are known shortcomings of direct image alignment
methods. Quantitative evaluation demonstrates that the proposed Stereo DSO
outperforms existing state-of-the-art visual odometry methods both in terms of
tracking accuracy and robustness. Moreover, our method delivers a more precise
metric 3D reconstruction than previous dense/semi-dense direct approaches while
providing a higher reconstruction density than feature-based methods. | [
"cs.CV"
] |
Neural networks have led to major improvements in image classification but
suffer from being non-robust to adversarial changes, unreliable uncertainty
estimates on out-distribution samples and their inscrutable black-box
decisions. In this work we propose RATIO, a training procedure for Robustness
via Adversarial Training on In- and Out-distribution, which leads to robust
models with reliable and robust confidence estimates on the out-distribution.
RATIO has similar generative properties to adversarial training so that visual
counterfactuals produce class specific features. While adversarial training
comes at the price of lower clean accuracy, RATIO achieves state-of-the-art
$l_2$-adversarial robustness on CIFAR10 and maintains better clean accuracy. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Recent literature found that convolutional neural networks (CNN) with large
filters perform well in some applications such as image semantic segmentation.
Winograd transformation helps to reduce the number of multiplications in a
convolution but suffers from numerical instability when the convolution filter
size gets large. This work proposes a nested Winograd algorithm to iteratively
decompose a large filter into a sequence of 3x3 tiles which can then be
accelerated with a 3x3 Winograd algorithm. Compared with the state-of-art
OLA-Winograd algorithm, the proposed algorithm reduces the multiplications by
1.41 to 3.29 times for computing 5x5 to 9x9 convolutions. | [
"cs.CV",
"cs.AI"
] |
This paper presents a novel method to distill knowledge from a deep pose
regressor network for efficient Visual Odometry (VO). Standard distillation
relies on "dark knowledge" for successful knowledge transfer. As this knowledge
is not available in pose regression and the teacher prediction is not always
accurate, we propose to emphasize the knowledge transfer only when we trust the
teacher. We achieve this by using teacher loss as a confidence score which
places variable relative importance on the teacher prediction. We inject this
confidence score to the main training task via Attentive Imitation Loss (AIL)
and when learning the intermediate representation of the teacher through
Attentive Hint Training (AHT) approach. To the best of our knowledge, this is
the first work which successfully distill the knowledge from a deep pose
regression network. Our evaluation on the KITTI and Malaga dataset shows that
we can keep the student prediction close to the teacher with up to 92.95%
parameter reduction and 2.12x faster in computation time. | [
"cs.CV",
"cs.LG",
"cs.RO"
] |
Discrete structure rules for validating molecular structures are usually
limited to fulfillment of the octet rule or similar simple deterministic
heuristics. We propose a model, inspired by language modeling from natural
language processing, with the ability to learn from a collection of undirected
molecular graphs, enabling fitting of any underlying structure rule present in
the collection. We introduce an adaption to the popular Transformer model,
which can learn relationships between atoms and bonds. To our knowledge, the
Transformer adaption is the first model that is trained to solve the
unsupervised task of recovering partially observed molecules. In this work, we
assess how different degrees of information impact performance w.r.t. to
fitting the QM9 dataset, which conforms to the octet rule, and to fitting the
ZINC dataset, which contains hypervalent molecules and ions requiring the model
to learn a more complex structure rule. More specifically, we test a full
discrete graph with bond order information, a full discrete graph with only
connectivity, a bag-of-neighbors, a bag-of-atoms, and a count-based unigram
statistics. These results provide encouraging evidence that neural networks,
even when only connectivity is available, can learn arbitrary molecular
structure rules specific to a dataset, as the Transformer adaption surpasses a
strong octet rule baseline on the ZINC dataset. | [
"cs.LG",
"stat.ML"
] |
There has been significant interest in the use of fully-connected graphical
models and deep-structured graphical models for the purpose of structured
inference. However, fully-connected and deep-structured graphical models have
been largely explored independently, leaving the unification of these two
concepts ripe for exploration. A fundamental challenge with unifying these two
types of models is in dealing with computational complexity. In this study, we
investigate the feasibility of unifying fully-connected and deep-structured
models in a computationally tractable manner for the purpose of structured
inference. To accomplish this, we introduce a deep-structured fully-connected
random field (DFRF) model that integrates a series of intermediate sparse
auto-encoding layers placed between state layers to significantly reduce
computational complexity. The problem of image segmentation was used to
illustrate the feasibility of using the DFRF for structured inference in a
computationally tractable manner. Results in this study show that it is
feasible to unify fully-connected and deep-structured models in a
computationally tractable manner for solving structured inference problems such
as image segmentation. | [
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT",
"stat.ME"
] |
Widespread adoption of high-temperature polymer electrolyte membrane fuel
cells (HT-PEMFCs) and HT-PEM electrochemical hydrogen pumps (HT-PEM ECHPs)
requires models and computational tools that provide accurate scale-up and
optimization. Knowledge-based modeling has limitations as it is time consuming
and requires information about the system that is not always available (e.g.,
material properties and interfacial behavior between different materials).
Data-driven modeling on the other hand, is easier to implement, but often
necessitates large datasets that could be difficult to obtain. In this
contribution, knowledge-based modeling and data-driven modeling are uniquely
combined by implementing a Few-Shot Learning (FSL) approach. A knowledge-based
model originally developed for a HT-PEMFC was used to generate simulated data
(887,735 points) and used to pretrain a neural network source model.
Furthermore, the source model developed for HT-PEMFCs was successfully applied
to HT-PEM ECHPs - a different electrochemical system that utilizes similar
materials to the fuel cell. Experimental datasets from both HT-PEMFCs and
HT-PEM ECHPs with different materials and operating conditions (~50 points
each) were used to train 8 target models via FSL. Models for the unseen data
reached high accuracies in all cases (rRMSE between 1.04 and 3.73% for HT-PEMCs
and between 6.38 and 8.46% for HT-PEM ECHPs). | [
"cs.LG",
"physics.chem-ph",
"I.6.5; J.6"
] |
Learning segmentation from noisy labels is an important task for medical
image analysis due to the difficulty in acquiring highquality annotations. Most
existing methods neglect the pixel correlation and structural prior in
segmentation, often producing noisy predictions around object boundaries. To
address this, we adopt a superpixel representation and develop a robust
iterative learning strategy that combines noise-aware training of segmentation
network and noisy label refinement, both guided by the superpixels. This design
enables us to exploit the structural constraints in segmentation labels and
effectively mitigate the impact of label noise in learning. Experiments on two
benchmarks show that our method outperforms recent state-of-the-art approaches,
and achieves superior robustness in a wide range of label noises. Code is
available at https://github.com/gaozhitong/SP_guided_Noisy_Label_Seg. | [
"cs.CV"
] |
Convolutional neural networks are able to perform a hierarchical learning
process starting with local features. However, a limited attention is paid to
enhancing such elementary level features like edges. We propose and evaluate
two wavelet-based edge feature enhancement methods to preprocess the input
images to convolutional neural networks. The first method develops feature
enhanced representations by decomposing the input images using wavelet
transform and limited reconstructing subsequently. The second method develops
such feature enhanced inputs to the network using local modulus maxima of
wavelet coefficients. For each method, we have developed a new preprocessing
layer by implementing each purposed method and have appended to the network
architecture. Our empirical evaluations demonstrate that the proposed methods
are outperforming the baselines and previously published work with significant
accuracy gains. | [
"cs.CV"
] |
This thesis scrutinizes common assumptions underlying traditional machine
learning approaches to fairness in consequential decision making. After
challenging the validity of these assumptions in real-world applications, we
propose ways to move forward when they are violated. First, we show that group
fairness criteria purely based on statistical properties of observed data are
fundamentally limited. Revisiting this limitation from a causal viewpoint we
develop a more versatile conceptual framework, causal fairness criteria, and
first algorithms to achieve them. We also provide tools to analyze how
sensitive a believed-to-be causally fair algorithm is to misspecifications of
the causal graph. Second, we overcome the assumption that sensitive data is
readily available in practice. To this end we devise protocols based on secure
multi-party computation to train, validate, and contest fair decision
algorithms without requiring users to disclose their sensitive data or decision
makers to disclose their models. Finally, we also accommodate the fact that
outcome labels are often only observed when a certain decision has been made.
We suggest a paradigm shift away from training predictive models towards
directly learning decisions to relax the traditional assumption that labels can
always be recorded. The main contribution of this thesis is the development of
theoretically substantiated and practically feasible methods to move research
on fair machine learning closer to real-world applications. | [
"cs.LG",
"cs.AI",
"cs.CY"
] |
This paper focuses on finding reinforcement learning policies for control
systems with hard state and action constraints. Despite its success in many
domains, reinforcement learning is challenging to apply to problems with hard
constraints, especially if both the state variables and actions are
constrained. Previous works seeking to ensure constraint satisfaction, or
safety, have focused on adding a projection step to a learned policy. Yet, this
approach requires solving an optimization problem at every policy execution
step, which can lead to significant computational costs.
To tackle this problem, this paper proposes a new approach, termed Vertex
Networks (VNs), with guarantees on safety during exploration and on learned
control policies by incorporating the safety constraints into the policy
network architecture. Leveraging the geometric property that all points within
a convex set can be represented as the convex combination of its vertices, the
proposed algorithm first learns the convex combination weights and then uses
these weights along with the pre-calculated vertices to output an action. The
output action is guaranteed to be safe by construction. Numerical examples
illustrate that the proposed VN algorithm outperforms vanilla reinforcement
learning in a variety of benchmark control tasks. | [
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] |
Much of the current work on reinforcement learning studies episodic settings,
where the agent is reset between trials to an initial state distribution, often
with well-shaped reward functions. Non-episodic settings, where the agent must
learn through continuous interaction with the world without resets, and where
the agent receives only delayed and sparse reward signals, is substantially
more difficult, but arguably more realistic considering real-world environments
do not present the learner with a convenient "reset mechanism" and easy reward
shaping. In this paper, instead of studying algorithmic improvements that can
address such non-episodic and sparse reward settings, we instead study the
kinds of environment properties that can make learning under such conditions
easier. Understanding how properties of the environment impact the performance
of reinforcement learning agents can help us to structure our tasks in ways
that make learning tractable. We first discuss what we term "environment
shaping" -- modifications to the environment that provide an alternative to
reward shaping, and may be easier to implement. We then discuss an even simpler
property that we refer to as "dynamism," which describes the degree to which
the environment changes independent of the agent's actions and can be measured
by environment transition entropy. Surprisingly, we find that even this
property can substantially alleviate the challenges associated with
non-episodic RL in sparse reward settings. We provide an empirical evaluation
on a set of new tasks focused on non-episodic learning with sparse rewards.
Through this study, we hope to shift the focus of the community towards
analyzing how properties of the environment can affect learning and the
ultimate type of behavior that is learned via RL. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
We propose a framework for indexing of grain and sub-grain structures in
electron backscatter diffraction (EBSD) images of polycrystalline materials.
The framework is based on a previously introduced physics-based forward model
by Callahan and De Graef (2013) relating measured patterns to grain
orientations (Euler angle). The forward model is tuned to the microscope and
the sample symmetry group. We discretize the domain of the forward model onto a
dense grid of Euler angles and for each measured pattern we identify the most
similar patterns in the dictionary. These patterns are used to identify
boundaries, detect anomalies, and index crystal orientations. The statistical
distribution of these closest matches is used in an unsupervised binary
decision tree (DT) classifier to identify grain boundaries and anomalous
regions. The DT classifies a pattern as an anomaly if it has an abnormally low
similarity to any pattern in the dictionary. It classifies a pixel as being
near a grain boundary if the highly ranked patterns in the dictionary differ
significantly over the pixels 3x3 neighborhood. Indexing is accomplished by
computing the mean orientation of the closest dictionary matches to each
pattern. The mean orientation is estimated using a maximum likelihood approach
that models the orientation distribution as a mixture of Von Mises-Fisher
distributions over the quaternionic 3-sphere. The proposed dictionary matching
approach permits segmentation, anomaly detection, and indexing to be performed
in a unified manner with the additional benefit of uncertainty quantification.
We demonstrate the proposed dictionary-based approach on a Ni-base IN100 alloy. | [
"cs.CV",
"physics.data-an",
"stat.AP"
] |
This paper introduces a fast and efficient network architecture, NeXtVLAD, to
aggregate frame-level features into a compact feature vector for large-scale
video classification. Briefly speaking, the basic idea is to decompose a
high-dimensional feature into a group of relatively low-dimensional vectors
with attention before applying NetVLAD aggregation over time. This NeXtVLAD
approach turns out to be both effective and parameter efficient in aggregating
temporal information. In the 2nd Youtube-8M video understanding challenge, a
single NeXtVLAD model with less than 80M parameters achieves a GAP score of
0.87846 in private leaderboard. A mixture of 3 NeXtVLAD models results in
0.88722, which is ranked 3rd over 394 teams. The code is publicly available at
https://github.com/linrongc/youtube-8m. | [
"cs.CV"
] |
With the success of deep learning in classifying short trimmed videos, more
attention has been focused on temporally segmenting and classifying activities
in long untrimmed videos. State-of-the-art approaches for action segmentation
utilize several layers of temporal convolution and temporal pooling. Despite
the capabilities of these approaches in capturing temporal dependencies, their
predictions suffer from over-segmentation errors. In this paper, we propose a
multi-stage architecture for the temporal action segmentation task that
overcomes the limitations of the previous approaches. The first stage generates
an initial prediction that is refined by the next ones. In each stage we stack
several layers of dilated temporal convolutions covering a large receptive
field with few parameters. While this architecture already performs well, lower
layers still suffer from a small receptive field. To address this limitation,
we propose a dual dilated layer that combines both large and small receptive
fields. We further decouple the design of the first stage from the refining
stages to address the different requirements of these stages. Extensive
evaluation shows the effectiveness of the proposed model in capturing
long-range dependencies and recognizing action segments. Our models achieve
state-of-the-art results on three datasets: 50Salads, Georgia Tech Egocentric
Activities (GTEA), and the Breakfast dataset. | [
"cs.CV"
] |
Fashion is the way we present ourselves to the world and has become one of
the world's largest industries. Fashion, mainly conveyed by vision, has thus
attracted much attention from computer vision researchers in recent years.
Given the rapid development, this paper provides a comprehensive survey of more
than 200 major fashion-related works covering four main aspects for enabling
intelligent fashion: (1) Fashion detection includes landmark detection, fashion
parsing, and item retrieval, (2) Fashion analysis contains attribute
recognition, style learning, and popularity prediction, (3) Fashion synthesis
involves style transfer, pose transformation, and physical simulation, and (4)
Fashion recommendation comprises fashion compatibility, outfit matching, and
hairstyle suggestion. For each task, the benchmark datasets and the evaluation
protocols are summarized. Furthermore, we highlight promising directions for
future research. | [
"cs.CV"
] |
This paper proposes an exploration method for deep reinforcement learning
based on parameter space noise. Recent studies have experimentally shown that
parameter space noise results in better exploration than the commonly used
action space noise. Previous methods devised a way to update the diagonal
covariance matrix of a noise distribution and did not consider the direction of
the noise vector and its correlation. In addition, fast updates of the noise
distribution are required to facilitate policy learning. We propose a method
that deforms the noise distribution according to the accumulated returns and
the noises that have led to the returns. Moreover, this method switches
isotropic exploration and directional exploration in parameter space with
regard to obtained rewards. We validate our exploration strategy in the OpenAI
Gym continuous environments and modified environments with sparse rewards. The
proposed method achieves results that are competitive with a previous method at
baseline tasks. Moreover, our approach exhibits better performance in sparse
reward environments by exploration with the switching strategy. | [
"stat.ML",
"cs.LG"
] |
Unintended biases in machine learning (ML) models are among the major
concerns that must be addressed to maintain public trust in ML. In this paper,
we address process fairness of ML models that consists in reducing the
dependence of models on sensitive features, without compromising their
performance. We revisit the framework FixOut that is inspired in the approach
"fairness through unawareness" to build fairer models. We introduce several
improvements such as automating the choice of FixOut's parameters. Also, FixOut
was originally proposed to improve fairness of ML models on tabular data. We
also demonstrate the feasibility of FixOut's workflow for models on textual
data. We present several experimental results that illustrate the fact that
FixOut improves process fairness on different classification settings. | [
"cs.LG",
"cs.AI",
"cs.CY",
"I.2.0; J.1; J.4"
] |
We introduce the framework of continuous--depth graph neural networks (GNNs).
Graph neural ordinary differential equations (GDEs) are formalized as the
counterpart to GNNs where the input-output relationship is determined by a
continuum of GNN layers, blending discrete topological structures and
differential equations. The proposed framework is shown to be compatible with
various static and autoregressive GNN models. Results prove general
effectiveness of GDEs: in static settings they offer computational advantages
by incorporating numerical methods in their forward pass; in dynamic settings,
on the other hand, they are shown to improve performance by exploiting the
geometry of the underlying dynamics. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Generative Adversarial Networks (GANs) have shown remarkable success in
producing realistic-looking images in the computer vision area. Recently,
GAN-based techniques are shown to be promising for spatio-temporal-based
applications such as trajectory prediction, events generation and time-series
data imputation. While several reviews for GANs in computer vision have been
presented, no one has considered addressing the practical applications and
challenges relevant to spatio-temporal data. In this paper, we have conducted a
comprehensive review of the recent developments of GANs for spatio-temporal
data. We summarise the application of popular GAN architectures for
spatio-temporal data and the common practices for evaluating the performance of
spatio-temporal applications with GANs. Finally, we point out future research
directions to benefit researchers in this area. | [
"cs.LG",
"cs.IR",
"eess.IV"
] |
We propose a novel high-performance and interpretable canonical deep tabular
data learning architecture, TabNet. TabNet uses sequential attention to choose
which features to reason from at each decision step, enabling interpretability
and more efficient learning as the learning capacity is used for the most
salient features. We demonstrate that TabNet outperforms other neural network
and decision tree variants on a wide range of non-performance-saturated tabular
datasets and yields interpretable feature attributions plus insights into the
global model behavior. Finally, for the first time to our knowledge, we
demonstrate self-supervised learning for tabular data, significantly improving
performance with unsupervised representation learning when unlabeled data is
abundant. | [
"cs.LG",
"stat.ML"
] |
Unmanned Aerial Vehicles (UAVs), have intrigued different people from all
walks of life, because of their pervasive computing capabilities. UAV equipped
with vision techniques, could be leveraged to establish navigation autonomous
control for UAV itself. Also, object detection from UAV could be used to
broaden the utilization of drone to provide ubiquitous surveillance and
monitoring services towards military operation, urban administration and
agriculture management. As the data-driven technologies evolved, machine
learning algorithm, especially the deep learning approach has been intensively
utilized to solve different traditional computer vision research problems.
Modern Convolutional Neural Networks based object detectors could be divided
into two major categories: one-stage object detector and two-stage object
detector. In this study, we utilize some representative CNN based object
detectors to execute the computer vision task over Stanford Drone Dataset
(SDD). State-of-the-art performance has been achieved in utilizing focal loss
dense detector RetinaNet based approach for object detection from UAV in a fast
and accurate manner. | [
"cs.CV"
] |
Pixel-wise segmentation is one of the most data and annotation hungry tasks
in our field. Providing representative and accurate annotations is often
mission-critical especially for challenging medical applications. In this
paper, we propose a semi-weakly supervised segmentation algorithm to overcome
this barrier. Our approach is based on a new formulation of deep supervision
and student-teacher model and allows for easy integration of different
supervision signals. In contrast to previous work, we show that care has to be
taken how deep supervision is integrated in lower layers and we present
multi-label deep supervision as the most important secret ingredient for
success. With our novel training regime for segmentation that flexibly makes
use of images that are either fully labeled, marked with bounding boxes, just
global labels, or not at all, we are able to cut the requirement for expensive
labels by 94.22% - narrowing the gap to the best fully supervised baseline to
only 5% mean IoU. Our approach is validated by extensive experiments on retinal
fluid segmentation and we provide an in-depth analysis of the anticipated
effect each annotation type can have in boosting segmentation performance. | [
"cs.CV",
"I.4.6; I.2.6; I.5.4; I.5.1"
] |
Recently, vision transformers and MLP-based models have been developed in
order to address some of the prevalent weaknesses in convolutional neural
networks. Due to the novelty of transformers being used in this domain along
with the self-attention mechanism, it remains unclear to what degree these
architectures are robust to corruptions. Despite some works proposing that data
augmentation remains essential for a model to be robust against corruptions, we
propose to explore the impact that the architecture has on corruption
robustness. We find that vision transformer architectures are inherently more
robust to corruptions than the ResNet-50 and MLP-Mixers. We also find that
vision transformers with 5 times fewer parameters than a ResNet-50 have more
shape bias. Our code is available to reproduce. | [
"cs.CV",
"cs.LG"
] |
In addition to the best model architecture and hyperparameters, a full AutoML
solution requires selecting appropriate hardware automatically. This can be
framed as a multi-objective optimization problem: there is not a single best
hardware configuration but a set of optimal ones achieving different trade-offs
between cost and runtime. In practice, some choices may be overly costly or
take days to train. To lift this burden, we adopt a multi-objective approach
that selects and adapts the hardware configuration automatically alongside
neural architectures and their hyperparameters. Our method builds on Hyperband
and extends it in two ways. First, we replace the stopping rule used in
Hyperband by a non-dominated sorting rule to preemptively stop unpromising
configurations. Second, we leverage hyperparameter evaluations from related
tasks via transfer learning by building a probabilistic estimate of the Pareto
front that finds promising configurations more efficiently than random search.
We show in extensive NAS and HPO experiments that both ingredients bring
significant speed-ups and cost savings, with little to no impact on accuracy.
In three benchmarks where hardware is selected in addition to hyperparameters,
we obtain runtime and cost reductions of at least 5.8x and 8.8x, respectively.
Furthermore, when applying our multi-objective method to the tuning of
hyperparameters only, we obtain a 10\% improvement in runtime while maintaining
the same accuracy on two popular NAS benchmarks. | [
"cs.LG"
] |
For reliable environment perception, the use of temporal information is
essential in some situations. Especially for object detection, sometimes a
situation can only be understood in the right perspective through temporal
information. Since image-based object detectors are currently based almost
exclusively on CNN architectures, an extension of their feature extraction with
temporal features seems promising.
Within this work we investigate different architectural components for a
CNN-based temporal information extraction. We present a Temporal Feature
Network which is based on the insights gained from our architectural
investigations. This network is trained from scratch without any ImageNet
information based pre-training as these images are not available with temporal
information. The object detector based on this network is evaluated against the
non-temporal counterpart as baseline and achieves competitive results in an
evaluation on the KITTI object detection dataset. | [
"cs.CV"
] |
In this paper we investigate image generation guided by hand sketch. When the
input sketch is badly drawn, the output of common image-to-image translation
follows the input edges due to the hard condition imposed by the translation
process. Instead, we propose to use sketch as weak constraint, where the output
edges do not necessarily follow the input edges. We address this problem using
a novel joint image completion approach, where the sketch provides the image
context for completing, or generating the output image. We train a generated
adversarial network, i.e, contextual GAN to learn the joint distribution of
sketch and the corresponding image by using joint images. Our contextual GAN
has several advantages. First, the simple joint image representation allows for
simple and effective learning of joint distribution in the same image-sketch
space, which avoids complicated issues in cross-domain learning. Second, while
the output is related to its input overall, the generated features exhibit more
freedom in appearance and do not strictly align with the input features as
previous conditional GANs do. Third, from the joint image's point of view,
image and sketch are of no difference, thus exactly the same deep joint image
completion network can be used for image-to-sketch generation. Experiments
evaluated on three different datasets show that our contextual GAN can generate
more realistic images than state-of-the-art conditional GANs on challenging
inputs and generalize well on common categories. | [
"cs.CV"
] |
Image segmentation is the most challenging issue in computer vision
applications. And most difficulties for crops management in agriculture are the
lack of appropriate methods for detecting the leaf damage for pests treatment.
In this paper we proposed an automatic method for leaf damage detection and
severity estimation of coffee leaf by avoiding defoliation. After enhancing the
contrast of the original image using LUT based gamma correction, the image is
processed to remove the background, and the output leaf is clustered using
Fuzzy c-means segmentation in V channel of YUV color space to maximize all leaf
damage detection, and finally, the severity of leaf is estimated in terms of
ratio for leaf pixel distribution between the normal and the detected leaf
damage. The results in each proposed method was compared to the current
researches and the accuracy is obvious either in the background removal or
damage detection. | [
"cs.CV"
] |
Convolutional Neural Networks have achieved unprecedented success in image
classification, recognition, or detection applications. However, their
large-scale deployment in embedded devices is still limited by the huge
computational requirements, i.e., millions of MAC operations per layer. In this
article, MinConvNets where the multiplications in the forward propagation are
approximated by minimum comparator operations are introduced. Hardware
implementation of minimum operation is much simpler than multipliers. Firstly,
a methodology to find approximate operations based on statistical correlation
is presented. We show that it is possible to replace multipliers by minimum
operations in the forward propagation under certain constraints, i.e. given
similar mean and variances of the feature and the weight vectors. A modified
training method which guarantees the above constraints is proposed. And it is
shown that equivalent precision can be achieved during inference with
MinConvNets by using transfer learning from well trained exact CNNs. | [
"cs.LG",
"cs.NE"
] |
Medical image segmentation is inherently an ambiguous task due to factors
such as partial volumes and variations in anatomical definitions. While in most
cases the segmentation uncertainty is around the border of structures of
interest, there can also be considerable inter-rater differences. The class of
conditional variational autoencoders (cVAE) offers a principled approach to
inferring distributions over plausible segmentations that are conditioned on
input images. Segmentation uncertainty estimated from samples of such
distributions can be more informative than using pixel level probability
scores. In this work, we propose a novel conditional generative model that is
based on conditional Normalizing Flow (cFlow). The basic idea is to increase
the expressivity of the cVAE by introducing a cFlow transformation step after
the encoder. This yields improved approximations of the latent posterior
distribution, allowing the model to capture richer segmentation variations.
With this we show that the quality and diversity of samples obtained from our
conditional generative model is enhanced. Performance of our model, which we
call cFlow Net, is evaluated on two medical imaging datasets demonstrating
substantial improvements in both qualitative and quantitative measures when
compared to a recent cVAE based model. | [
"stat.ML",
"cs.CV",
"cs.LG"
] |
In supervised learning, smoothing label or prediction distribution in neural
network training has been proven useful in preventing the model from being
over-confident, and is crucial for learning more robust visual representations.
This observation motivates us to explore ways to make predictions flattened in
unsupervised learning. Considering that human-annotated labels are not adopted
in unsupervised learning, we introduce a straightforward approach to perturb
input image space in order to soften the output prediction space indirectly,
meanwhile, assigning new label values in the unsupervised frameworks
accordingly. Despite its conceptual simplicity, we show empirically that with
the simple solution -- Unsupervised image mixtures (Un-Mix), we can learn more
robust visual representations from the transformed input. Extensive experiments
are conducted on CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet and standard
ImageNet with popular unsupervised methods SimCLR, BYOL, MoCo V1&V2, etc. Our
proposed image mixture and label assignment strategy can obtain consistent
improvement by 1~3% following exactly the same hyperparameters and training
procedures of the base methods. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Salient object detection models often demand a considerable amount of
computation cost to make precise prediction for each pixel, making them hardly
applicable on low-power devices. In this paper, we aim to relieve the
contradiction between computation cost and model performance by improving the
network efficiency to a higher degree. We propose a flexible convolutional
module, namely generalized OctConv (gOctConv), to efficiently utilize both
in-stage and cross-stages multi-scale features, while reducing the
representation redundancy by a novel dynamic weight decay scheme. The effective
dynamic weight decay scheme stably boosts the sparsity of parameters during
training, supports learnable number of channels for each scale in gOctConv,
allowing 80% of parameters reduce with negligible performance drop. Utilizing
gOctConv, we build an extremely light-weighted model, namely CSNet, which
achieves comparable performance with about 0.2% parameters (100k) of large
models on popular salient object detection benchmarks. | [
"cs.CV"
] |
The graph structure of biomedical data differs from those in typical
knowledge graph benchmark tasks. A particular property of biomedical data is
the presence of long-range dependencies, which can be captured by patterns
described as logical rules. We propose a novel method that combines these rules
with a neural multi-hop reasoning approach that uses reinforcement learning. We
conduct an empirical study based on the real-world task of drug repurposing by
formulating this task as a link prediction problem. We apply our method to the
biomedical knowledge graph Hetionet and show that our approach outperforms
several baseline methods. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Convolutional neural networks have been successfully applied to semantic
segmentation problems. However, there are many problems that are inherently not
pixel-wise classification problems but are nevertheless frequently formulated
as semantic segmentation. This ill-posed formulation consequently necessitates
hand-crafted scenario-specific and computationally expensive post-processing
methods to convert the per pixel probability maps to final desired outputs.
Generative adversarial networks (GANs) can be used to make the semantic
segmentation network output to be more realistic or better
structure-preserving, decreasing the dependency on potentially complex
post-processing. In this work, we propose EL-GAN: a GAN framework to mitigate
the discussed problem using an embedding loss. With EL-GAN, we discriminate
based on learned embeddings of both the labels and the prediction at the same
time. This results in more stable training due to having better discriminative
information, benefiting from seeing both `fake' and `real' predictions at the
same time. This substantially stabilizes the adversarial training process. We
use the TuSimple lane marking challenge to demonstrate that with our proposed
framework it is viable to overcome the inherent anomalies of posing it as a
semantic segmentation problem. Not only is the output considerably more similar
to the labels when compared to conventional methods, the subsequent
post-processing is also simpler and crosses the competitive 96% accuracy
threshold. | [
"cs.CV"
] |
Arguably one of the top success stories of deep learning is transfer
learning. The finding that pre-training a network on a rich source set (eg.,
ImageNet) can help boost performance once fine-tuned on a usually much smaller
target set, has been instrumental to many applications in language and vision.
Yet, very little is known about its usefulness in 3D point cloud understanding.
We see this as an opportunity considering the effort required for annotating
data in 3D. In this work, we aim at facilitating research on 3D representation
learning. Different from previous works, we focus on high-level scene
understanding tasks. To this end, we select a suite of diverse datasets and
tasks to measure the effect of unsupervised pre-training on a large source set
of 3D scenes. Our findings are extremely encouraging: using a unified triplet
of architecture, source dataset, and contrastive loss for pre-training, we
achieve improvement over recent best results in segmentation and detection
across 6 different benchmarks for indoor and outdoor, real and synthetic
datasets -- demonstrating that the learned representation can generalize across
domains. Furthermore, the improvement was similar to supervised pre-training,
suggesting that future efforts should favor scaling data collection over more
detailed annotation. We hope these findings will encourage more research on
unsupervised pretext task design for 3D deep learning. | [
"cs.CV"
] |
Motivated by vision-based reinforcement learning (RL) problems, in particular
Atari games from the recent benchmark Aracade Learning Environment (ALE), we
consider spatio-temporal prediction problems where future (image-)frames are
dependent on control variables or actions as well as previous frames. While not
composed of natural scenes, frames in Atari games are high-dimensional in size,
can involve tens of objects with one or more objects being controlled by the
actions directly and many other objects being influenced indirectly, can
involve entry and departure of objects, and can involve deep partial
observability. We propose and evaluate two deep neural network architectures
that consist of encoding, action-conditional transformation, and decoding
layers based on convolutional neural networks and recurrent neural networks.
Experimental results show that the proposed architectures are able to generate
visually-realistic frames that are also useful for control over approximately
100-step action-conditional futures in some games. To the best of our
knowledge, this paper is the first to make and evaluate long-term predictions
on high-dimensional video conditioned by control inputs. | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
We address the challenging problem of image captioning by revisiting the
representation of image scene graph. At the core of our method lies the
decomposition of a scene graph into a set of sub-graphs, with each sub-graph
capturing a semantic component of the input image. We design a deep model to
select important sub-graphs, and to decode each selected sub-graph into a
single target sentence. By using sub-graphs, our model is able to attend to
different components of the image. Our method thus accounts for accurate,
diverse, grounded and controllable captioning at the same time. We present
extensive experiments to demonstrate the benefits of our comprehensive
captioning model. Our method establishes new state-of-the-art results in
caption diversity, grounding, and controllability, and compares favourably to
latest methods in caption quality. Our project website can be found at
http://pages.cs.wisc.edu/~yiwuzhong/Sub-GC.html. | [
"cs.CV"
] |
We present a deep person re-identification approach that combines
semantically selective, deep data augmentation with clustering-based network
compression to generate high performance, light and fast inference networks. In
particular, we propose to augment limited training data via sampling from a
deep convolutional generative adversarial network (DCGAN), whose discriminator
is constrained by a semantic classifier to explicitly control the domain
specificity of the generation process. Thereby, we encode information in the
classifier network which can be utilized to steer adversarial synthesis, and
which fuels our CondenseNet ID-network training. We provide a quantitative and
qualitative analysis of the approach and its variants on a number of datasets,
obtaining results that outperform the state-of-the-art on the LIMA dataset for
long-term monitoring in indoor living spaces. | [
"cs.CV"
] |
Recent state-of-the-art image segmentation algorithms are mostly based on
deep neural networks, thanks to their high performance and fast computation
time. However, these methods are usually trained in a supervised manner, which
requires large number of high quality ground-truth segmentation masks. On the
other hand, classical image segmentation approaches such as level-set methods
are formulated in a self-supervised manner by minimizing energy functions such
as Mumford-Shah functional, so they are still useful to help generation of
segmentation masks without labels. Unfortunately, these algorithms are usually
computationally expensive and often have limitation in semantic segmentation.
In this paper, we propose a novel loss function based on Mumford-Shah
functional that can be used in deep-learning based image segmentation without
or with small labeled data. This loss function is based on the observation that
the softmax layer of deep neural networks has striking similarity to the
characteristic function in the Mumford-Shah functional. We show that the new
loss function enables semi-supervised and unsupervised segmentation. In
addition, our loss function can be also used as a regularized function to
enhance supervised semantic segmentation algorithms. Experimental results on
multiple datasets demonstrate the effectiveness of the proposed method. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for Video
Frame Interpolation (VFI). Many recent flow-based VFI methods first estimate
the bi-directional optical flows, then scale and reverse them to approximate
intermediate flows, leading to artifacts on motion boundaries. RIFE uses a
neural network named IFNet that can directly estimate the intermediate flows
from coarse-to-fine with much better speed. We design a privileged distillation
scheme for training intermediate flow model, which leads to a large performance
improvement. Experiments demonstrate that RIFE is flexible and can achieve
state-of-the-art performance on several public benchmarks. The code is
available at \url{https://github.com/hzwer/arXiv2020-RIFE} | [
"cs.CV",
"cs.LG"
] |
Weakly-supervised image segmentation is an important task in computer vision.
A key problem is how to obtain high quality objects location from image-level
category. Classification activation mapping is a common method which can be
used to generate high-precise object location cues. However these location cues
are generally very sparse and small such that they can not provide effective
information for image segmentation. In this paper, we propose a saliency guided
image segmentation network to resolve this problem. We employ a self-attention
saliency method to generate subtle saliency maps, and render the location cues
grow as seeds by seeded region growing method to expand pixel-level labels
extent. In the process of seeds growing, we use the saliency values to weight
the similarity between pixels to control the growing. Therefore saliency
information could help generate discriminative object regions, and the effects
of wrong salient pixels can be suppressed efficiently. Experimental results on
a common segmentation dataset PASCAL VOC2012 demonstrate the effectiveness of
our method. | [
"cs.CV"
] |
Superpixel algorithms, which group pixels similar in color and other
low-level properties, are increasingly used for pre-processing in image
segmentation. Commonly important criteria for the computation of superpixels
are boundary adherence, speed, and regularity.
Boundary adherence and regularity are typically contradictory goals. Most
recent algorithms have focused on improving boundary adherence. Motivated by
improving superpixel regularity, we propose a diagram-based superpixel
generation method called Power-SLIC.
On the BSDS500 data set, Power-SLIC outperforms other state-of-the-art
algorithms in terms of compactness and boundary precision, and its boundary
adherence is the most robust against varying levels of Gaussian noise. In terms
of speed, Power-SLIC is competitive with SLIC. | [
"cs.CV",
"cs.CG",
"cs.LG"
] |
Segmentation is one of the most important tasks in image processing. It
consist in classify the pixels into two or more groups depending on their
intensity levels and a threshold value. The quality of the segmentation depends
on the method applied to select the threshold. The use of the classical
implementations for multilevel thresholding is computationally expensive since
they exhaustively search the best values to optimize the objective function.
Under such conditions, the use of optimization evolutionary approaches has been
extended. The Electromagnetism Like algorithm (EMO) is an evolutionary method
which mimics the attraction repulsion mechanism among charges to evolve the
members of a population. Different to other algorithms, EMO exhibits
interesting search capabilities whereas maintains a low computational overhead.
In this paper, a multilevel thresholding (MT) algorithm based on the EMO is
introduced. The approach combines the good search capabilities of EMO algorithm
with objective functions proposed by the popular MT methods of Otsu and Kapur.
The algorithm takes random samples from a feasible search space inside the
image histogram. Such samples build each particle in the EMO context whereas
its quality is evaluated considering the objective that is function employed by
the Otsu or Kapur method. Guided by these objective values the set of candidate
solutions are evolved through the EMO operators until an optimal solution is
found. The approach generates a multilevel segmentation algorithm which can
effectively identify the threshold values of a digital image in a reduced
number of iterations. Experimental results show performance evidence of the
implementation of EMO for digital image segmentation. | [
"cs.CV"
] |
A common issue of deep neural networks-based methods for the problem of
Single Image Super-Resolution (SISR), is the recovery of finer texture details
when super-resolving at large upscaling factors. This issue is particularly
related to the choice of the objective loss function. In particular, recent
works proposed the use of a VGG loss which consists in minimizing the error
between the generated high resolution images and ground-truth in the feature
space of a Convolutional Neural Network (VGG19), pre-trained on the very
"large" ImageNet dataset. When considering the problem of super-resolving
images with a distribution "far" from the ImageNet images distribution
(\textit{e.g.,} satellite images), their proposed \textit{fixed} VGG loss is no
longer relevant. In this paper, we present a general framework named
\textit{Generative Collaborative Networks} (GCN), where the idea consists in
optimizing the \textit{generator} (the mapping of interest) in the feature
space of a \textit{features extractor} network. The two networks (generator and
extractor) are \textit{collaborative} in the sense that the latter "helps" the
former, by constructing discriminative and relevant features (not necessarily
\textit{fixed} and possibly learned \textit{mutually} with the generator). We
evaluate the GCN framework in the context of SISR, and we show that it results
in a method that is adapted to super-resolution domains that are "far" from the
ImageNet domain. | [
"cs.CV"
] |
Conventional uncertainty quantification methods usually lacks the capability
of dealing with high-dimensional problems due to the curse of dimensionality.
This paper presents a semi-supervised learning framework for dimension
reduction and reliability analysis. An autoencoder is first adopted for mapping
the high-dimensional space into a low-dimensional latent space, which contains
a distinguishable failure surface. Then a deep feedforward neural network (DFN)
is utilized to learn the mapping relationship and reconstruct the latent space,
while the Gaussian process (GP) modeling technique is used to build the
surrogate model of the transformed limit state function. During the training
process of the DFN, the discrepancy between the actual and reconstructed latent
space is minimized through semi-supervised learning for ensuring the accuracy.
Both labeled and unlabeled samples are utilized for defining the loss function
of the DFN. Evolutionary algorithm is adopted to train the DFN, then the Monte
Carlo simulation method is used for uncertainty quantification and reliability
analysis based on the proposed framework. The effectiveness is demonstrated
through a mathematical example. | [
"stat.ML",
"cs.LG",
"cs.NE"
] |
Domain generalization refers to the problem where we aim to train a model on
data from a set of source domains so that the model can generalize to unseen
target domains. Naively training a model on the aggregate set of data (pooled
from all source domains) has been shown to perform suboptimally, since the
information learned by that model might be domain-specific and generalize
imperfectly to target domains. To tackle this problem, a predominant approach
is to find and learn some domain-invariant information in order to use it for
the prediction task. In this paper, we propose a theoretically grounded method
to learn a domain-invariant representation by enforcing the representation
network to be invariant under all transformation functions among domains. We
also show how to use generative adversarial networks to learn such domain
transformations to implement our method in practice. We demonstrate the
effectiveness of our method on several widely used datasets for the domain
generalization problem, on all of which we achieve competitive results with
state-of-the-art models. | [
"cs.LG"
] |
One of the essential tasks in connectomics is the morphology analysis of
neurons and organelles like mitochondria to shed light on their biological
properties. However, these biological objects often have tangled parts or
complex branching patterns, which make it hard to abstract, categorize, and
manipulate their morphology. In this paper, we develop a novel topological
nomenclature system to name these objects like the appellation for chemical
compounds to promote neuroscience analysis based on their skeletal structures.
We first convert the volumetric representation into the topology-preserving
reduced graph to untangle the objects. Next, we develop nomenclature rules for
pyramidal neurons and mitochondria from the reduced graph and finally learn the
feature embedding for shape manipulation. In ablation studies, we
quantitatively show that graphs generated by our proposed method align with the
perception of experts. On 3D shape retrieval and decomposition tasks, we
qualitatively demonstrate that the encoded topological nomenclature features
achieve better results than state-of-the-art shape descriptors. To advance
neuroscience, we will release a 3D segmentation dataset of mitochondria and
pyramidal neurons reconstructed from a 100um cube electron microscopy volume
with their reduced graph and topological nomenclature annotations. Code is
publicly available at https://github.com/donglaiw/ibexHelper. | [
"cs.CV"
] |
This paper introduces an information theoretic co-training objective for
unsupervised learning. We consider the problem of predicting the future. Rather
than predict future sensations (image pixels or sound waves) we predict
"hypotheses" to be confirmed by future sensations. More formally, we assume a
population distribution on pairs $(x,y)$ where we can think of $x$ as a past
sensation and $y$ as a future sensation. We train both a predictor model
$P_\Phi(z|x)$ and a confirmation model $P_\Psi(z|y)$ where we view $z$ as
hypotheses (when predicted) or facts (when confirmed). For a population
distribution on pairs $(x,y)$ we focus on the problem of measuring the mutual
information between $x$ and $y$. By the data processing inequality this mutual
information is at least as large as the mutual information between $x$ and $z$
under the distribution on triples $(x,z,y)$ defined by the confirmation model
$P_\Psi(z|y)$. The information theoretic training objective for $P_\Phi(z|x)$
and $P_\Psi(z|y)$ can be viewed as a form of co-training where we want the
prediction from $x$ to match the confirmation from $y$. | [
"cs.LG",
"stat.ML"
] |
An object's geocentric pose, defined as the height above ground and
orientation with respect to gravity, is a powerful representation of real-world
structure for object detection, segmentation, and localization tasks using RGBD
images. For close-range vision tasks, height and orientation have been derived
directly from stereo-computed depth and more recently from monocular depth
predicted by deep networks. For long-range vision tasks such as Earth
observation, depth cannot be reliably estimated with monocular images. Inspired
by recent work in monocular height above ground prediction and optical flow
prediction from static images, we develop an encoding of geocentric pose to
address this challenge and train a deep network to compute the representation
densely, supervised by publicly available airborne lidar. We exploit these
attributes to rectify oblique images and remove observed object parallax to
dramatically improve the accuracy of localization and to enable accurate
alignment of multiple images taken from very different oblique viewpoints. We
demonstrate the value of our approach by extending two large-scale public
datasets for semantic segmentation in oblique satellite images. All of our data
and code are publicly available. | [
"cs.CV"
] |
Existing image-to-image translation (I2IT) methods are either constrained to
low-resolution images or long inference time due to their heavy computational
burden on the convolution of high-resolution feature maps. In this paper, we
focus on speeding-up the high-resolution photorealistic I2IT tasks based on
closed-form Laplacian pyramid decomposition and reconstruction. Specifically,
we reveal that the attribute transformations, such as illumination and color
manipulation, relate more to the low-frequency component, while the content
details can be adaptively refined on high-frequency components. We consequently
propose a Laplacian Pyramid Translation Network (LPTN) to simultaneously
perform these two tasks, where we design a lightweight network for translating
the low-frequency component with reduced resolution and a progressive masking
strategy to efficiently refine the high-frequency ones. Our model avoids most
of the heavy computation consumed by processing high-resolution feature maps
and faithfully preserves the image details. Extensive experimental results on
various tasks demonstrate that the proposed method can translate 4K images in
real-time using one normal GPU while achieving comparable transformation
performance against existing methods. Datasets and codes are available:
https://github.com/csjliang/LPTN. | [
"cs.CV"
] |
Self-supervised learning has shown great potentials in improving the deep
learning model in an unsupervised manner by constructing surrogate supervision
signals directly from the unlabeled data. Different from existing works, we
present a novel way to obtain the surrogate supervision signal based on
high-level feature maps under consistency regularization. In this paper, we
propose a Spatio-Temporal Consistency Regularization between different output
features generated from a siamese network including a clean path fed with
original video and a noise path fed with the corresponding augmented video.
Based on the Spatio-Temporal characteristics of video, we develop two
video-based data augmentation methods, i.e., Spatio-Temporal Transformation and
Intra-Video Mixup. Consistency of the former one is proposed to model
transformation consistency of features, while the latter one aims at retaining
spatial invariance to extract action-related features. Extensive experiments
demonstrate that our method achieves substantial improvements compared with
state-of-the-art self-supervised learning methods for action recognition. When
using our method as an additional regularization term and combine with current
surrogate supervision signals, we achieve 22% relative improvement over the
previous state-of-the-art on HMDB51 and 7% on UCF101. | [
"cs.CV"
] |
The goal of the inverse reinforcement learning (IRL) problem is to recover
the reward functions from expert demonstrations. However, the IRL problem like
any ill-posed inverse problem suffers the congenital defect that the policy may
be optimal for many reward functions, and expert demonstrations may be optimal
for many policies. In this work, we generalize the IRL problem to a well-posed
expectation optimization problem stochastic inverse reinforcement learning
(SIRL) to recover the probability distribution over reward functions. We adopt
the Monte Carlo expectation-maximization (MCEM) method to estimate the
parameter of the probability distribution as the first solution to the SIRL
problem. The solution is succinct, robust, and transferable for a learning task
and can generate alternative solutions to the IRL problem. Through our
formulation, it is possible to observe the intrinsic property for the IRL
problem from a global viewpoint, and our approach achieves a considerable
performance on the objectworld. | [
"cs.LG",
"cs.AI",
"stat.ML",
"I.2.6"
] |
Advanced methods of applying deep learning to structured data such as graphs
have been proposed in recent years. In particular, studies have focused on
generalizing convolutional neural networks to graph data, which includes
redefining the convolution and the downsampling (pooling) operations for
graphs. The method of generalizing the convolution operation to graphs has been
proven to improve performance and is widely used. However, the method of
applying downsampling to graphs is still difficult to perform and has room for
improvement. In this paper, we propose a graph pooling method based on
self-attention. Self-attention using graph convolution allows our pooling
method to consider both node features and graph topology. To ensure a fair
comparison, the same training procedures and model architectures were used for
the existing pooling methods and our method. The experimental results
demonstrate that our method achieves superior graph classification performance
on the benchmark datasets using a reasonable number of parameters. | [
"cs.LG",
"stat.ML",
"I.2.6"
] |
Deep reinforcement learning has proven to be successful for learning tasks in
simulated environments, but applying same techniques for robots in real-world
domain is more challenging, as they require hours of training. To address this,
transfer learning can be used to train the policy first in a simulated
environment and then transfer it to physical agent. As the simulation never
matches reality perfectly, the physics, visuals and action spaces by necessity
differ between these environments to some degree. In this work, we study how
general video games can be directly used instead of fine-tuned simulations for
the sim-to-real transfer. Especially, we study how the agent can learn the new
action space autonomously, when the game actions do not match the robot
actions. Our results show that the different action space can be learned by
re-training only part of neural network and we obtain above 90% mean success
rate in simulation and robot experiments. | [
"cs.LG",
"cs.AI",
"cs.RO"
] |
We propose the use of unsupervised learning to train projection networks that
project onto the latent space of an already trained generator. We apply our
method to a trained StyleGAN, and use our projection network to perform image
super-resolution and clustering of images into semantically identifiable
groups. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Videos contain various types and strengths of motions that may look
unnaturally discontinuous in time when the recorded frame rate is low. This
paper reviews the first AIM challenge on video temporal super-resolution (frame
interpolation) with a focus on the proposed solutions and results. From
low-frame-rate (15 fps) video sequences, the challenge participants are asked
to submit higher-framerate (60 fps) video sequences by estimating temporally
intermediate frames. We employ the REDS VTSR dataset derived from diverse
videos captured in a hand-held camera for training and evaluation purposes. The
competition had 62 registered participants, and a total of 8 teams competed in
the final testing phase. The challenge winning methods achieve the
state-of-the-art in video temporal superresolution. | [
"cs.CV"
] |
Recent progress in AutoML has lead to state-of-the-art methods (e.g.,
AutoSKLearn) that can be readily used by non-experts to approach any supervised
learning problem. Whereas these methods are quite effective, they are still
limited in the sense that they work for tabular (matrix formatted) data only.
This paper describes one step forward in trying to automate the design of
supervised learning methods in the context of text mining. We introduce a meta
learning methodology for automatically obtaining a representation for text
mining tasks starting from raw text. We report experiments considering 60
different textual representations and more than 80 text mining datasets
associated to a wide variety of tasks. Experimental results show the proposed
methodology is a promising solution to obtain highly effective off the shell
text classification pipelines. | [
"cs.LG",
"cs.CL",
"stat.ML"
] |
Domain adaptation (DA) aims to transfer the knowledge learned from a source
domain to an unlabeled target domain. Some recent works tackle source-free
domain adaptation (SFDA) where only a source pre-trained model is available for
adaptation to the target domain. However, those methods do not consider keeping
source performance which is of high practical value in real world applications.
In this paper, we propose a new domain adaptation paradigm called Generalized
Source-free Domain Adaptation (G-SFDA), where the learned model needs to
perform well on both the target and source domains, with only access to current
unlabeled target data during adaptation. First, we propose local structure
clustering (LSC), aiming to cluster the target features with its semantically
similar neighbors, which successfully adapts the model to the target domain in
the absence of source data. Second, we propose sparse domain attention (SDA),
it produces a binary domain specific attention to activate different feature
channels for different domains, meanwhile the domain attention will be utilized
to regularize the gradient during adaptation to keep source information. In the
experiments, for target performance our method is on par with or better than
existing DA and SFDA methods, specifically it achieves state-of-the-art
performance (85.4%) on VisDA, and our method works well for all domains after
adapting to single or multiple target domains. Code is available in
https://github.com/Albert0147/G-SFDA. | [
"cs.CV"
] |
We propose ST-DETR, a Spatio-Temporal Transformer-based architecture for
object detection from a sequence of temporal frames. We treat the temporal
frames as sequences in both space and time and employ the full attention
mechanisms to take advantage of the features correlations over both dimensions.
This treatment enables us to deal with frames sequence as temporal object
features traces over every location in the space. We explore two possible
approaches; the early spatial features aggregation over the temporal dimension,
and the late temporal aggregation of object query spatial features. Moreover,
we propose a novel Temporal Positional Embedding technique to encode the time
sequence information. To evaluate our approach, we choose the Moving Object
Detection (MOD)task, since it is a perfect candidate to showcase the importance
of the temporal dimension. Results show a significant 5% mAP improvement on the
KITTI MOD dataset over the 1-step spatial baseline. | [
"cs.CV"
] |
Long-term visual localization is the problem of estimating the camera pose of
a given query image in a scene whose appearance changes over time. It is an
important problem in practice, for example, encountered in autonomous driving.
In order to gain robustness to such changes, long-term localization approaches
often use segmantic segmentations as an invariant scene representation, as the
semantic meaning of each scene part should not be affected by seasonal and
other changes. However, these representations are typically not very
discriminative due to the limited number of available classes. In this paper,
we propose a new neural network, the Fine-Grained Segmentation Network (FGSN),
that can be used to provide image segmentations with a larger number of labels
and can be trained in a self-supervised fashion. In addition, we show how FGSNs
can be trained to output consistent labels across seasonal changes. We
demonstrate through extensive experiments that integrating the fine-grained
segmentations produced by our FGSNs into existing localization algorithms leads
to substantial improvements in localization performance. | [
"cs.CV",
"68T45"
] |
We present the first differentially private algorithms for reinforcement
learning, which apply to the task of evaluating a fixed policy. We establish
two approaches for achieving differential privacy, provide a theoretical
analysis of the privacy and utility of the two algorithms, and show promising
results on simple empirical examples. | [
"cs.LG",
"stat.ML"
] |
Supervised deep learning requires a large amount of training samples with
annotations (e.g. label class for classification task, pixel- or voxel-wised
label map for segmentation tasks), which are expensive and time-consuming to
obtain. During the training of a deep neural network, the annotated samples are
fed into the network in a mini-batch way, where they are often regarded of
equal importance. However, some of the samples may become less informative
during training, as the magnitude of the gradient start to vanish for these
samples. In the meantime, other samples of higher utility or hardness may be
more demanded for the training process to proceed and require more
exploitation. To address the challenges of expensive annotations and loss of
sample informativeness, here we propose a novel training framework which
adaptively selects informative samples that are fed to the training process.
The adaptive selection or sampling is performed based on a hardness-aware
strategy in the latent space constructed by a generative model. To evaluate the
proposed training framework, we perform experiments on three different
datasets, including MNIST and CIFAR-10 for image classification task and a
medical image dataset IVUS for biophysical simulation task. On all three
datasets, the proposed framework outperforms a random sampling method, which
demonstrates the effectiveness of proposed framework. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
We address the problem of motion estimation in images operating in the
frequency domain. A method is presented which extends phase correlation to
handle multiple motions present in an area. Our scheme is based on a novel
Bilateral-Phase Correlation (BLPC) technique that incorporates the concept and
principles of Bilateral Filters retaining the motion boundaries by taking into
account the difference both in value and distance in a manner very similar to
Gaussian convolution. The optical flow is obtained by applying the proposed
method at certain locations selected based on the present motion differences
and then performing non-uniform interpolation in a multi-scale iterative
framework. Experiments with several well-known datasets with and without
ground-truth show that our scheme outperforms recently proposed
state-of-the-art phase correlation based optical flow methods. | [
"cs.CV"
] |
Conditional image synthesis aims to create an image according to some
multi-modal guidance in the forms of textual descriptions, reference images,
and image blocks to preserve, as well as their combinations. In this paper,
instead of investigating these control signals separately, we propose a new
two-stage architecture, UFC-BERT, to unify any number of multi-modal controls.
In UFC-BERT, both the diverse control signals and the synthesized image are
uniformly represented as a sequence of discrete tokens to be processed by
Transformer. Different from existing two-stage autoregressive approaches such
as DALL-E and VQGAN, UFC-BERT adopts non-autoregressive generation (NAR) at the
second stage to enhance the holistic consistency of the synthesized image, to
support preserving specified image blocks, and to improve the synthesis speed.
Further, we design a progressive algorithm that iteratively improves the
non-autoregressively generated image, with the help of two estimators developed
for evaluating the compliance with the controls and evaluating the fidelity of
the synthesized image, respectively. Extensive experiments on a newly collected
large-scale clothing dataset M2C-Fashion and a facial dataset Multi-Modal
CelebA-HQ verify that UFC-BERT can synthesize high-fidelity images that comply
with flexible multi-modal controls. | [
"cs.CV"
] |
In this paper, we focus on the unsupervised multi-view feature selection
which tries to handle high dimensional data in the field of multi-view
learning. Although some graph-based methods have achieved satisfactory
performance, they ignore the underlying data structure across different views.
Besides, their pre-defined laplacian graphs are sensitive to the noises in the
original data space, and fail to get the optimal neighbor assignment. To
address the above problems, we propose a novel unsupervised multi-view feature
selection model based on graph learning, and the contributions are threefold:
(1) during the feature selection procedure, the consensus similarity graph
shared by different views is learned. Therefore, the proposed model can reveal
the data relationship from the feature subset. (2) a reasonable rank constraint
is added to optimize the similarity matrix to obtain more accurate information;
(3) an auto-weighted framework is presented to assign view weights adaptively,
and an effective alternative iterative algorithm is proposed to optimize the
problem. Experiments on various datasets demonstrate the superiority of the
proposed method compared with the state-of-the-art methods. | [
"cs.LG",
"cs.AI"
] |
A neural network model of a differential equation, namely neural ODE, has
enabled us to learn continuous-time dynamical systems and probabilistic
distributions with a high accuracy. It uses the same network repeatedly during
a numerical integration. Hence, the backpropagation algorithm requires a memory
footprint proportional to the number of uses times the network size. This is
true even if a checkpointing scheme divides the computational graph into
sub-graphs. Otherwise, the adjoint method obtains a gradient by a numerical
integration backward in time with a minimal memory footprint; however, it
suffers from numerical errors. This study proposes the symplectic adjoint
method, which obtains the exact gradient (up to rounding error) with a
footprint proportional to the number of uses plus the network size. The
experimental results demonstrate the symplectic adjoint method occupies the
smallest footprint in most cases, functions faster in some cases, and is robust
to a rounding error among competitive methods. | [
"cs.LG"
] |
With the popularity of 3D sensors in self-driving and other robotics
applications, extensive research has focused on designing novel neural network
architectures for accurate 3D point cloud completion. However, unlike in point
cloud classification and reconstruction, the role of adversarial samples in3D
point cloud completion has seldom been explored. In this work, we show that
training with adversarial samples can improve the performance of neural
networks on 3D point cloud completion tasks. We propose a novel approach to
generate adversarial samples that benefit both the performance of clean and
adversarial samples. In contrast to the PGD-k attack, our method generates
adversarial samples that keep the geometric features in clean samples and
contain few outliers. In particular, we use principal directions to constrain
the adversarial perturbations for each input point. The gradient components in
the mean direction of principal directions are taken as adversarial
perturbations. In addition, we also investigate the effect of using the minimum
curvature direction. Besides, we adopt attack strength accumulation and
auxiliary Batch Normalization layers method to speed up the training process
and alleviate the distribution mismatch between clean and adversarial samples.
Experimental results show that training with the adversarial samples crafted by
our method effectively enhances the performance of PCN on the ShapeNet dataset. | [
"cs.CV"
] |
Point cloud registration is a key task in many computational fields. Previous
correspondence matching based methods require the inputs to have distinctive
geometric structures to fit a 3D rigid transformation according to point-wise
sparse feature matches. However, the accuracy of transformation heavily relies
on the quality of extracted features, which are prone to errors with respect to
partiality and noise. In addition, they can not utilize the geometric knowledge
of all the overlapping regions. On the other hand, previous global feature
based approaches can utilize the entire point cloud for the registration,
however they ignore the negative effect of non-overlapping points when
aggregating global features. In this paper, we present OMNet, a global feature
based iterative network for partial-to-partial point cloud registration. We
learn overlapping masks to reject non-overlapping regions, which converts the
partial-to-partial registration to the registration of the same shape.
Moreover, the previously used data is sampled only once from the CAD models for
each object, resulting in the same point clouds for the source and reference.
We propose a more practical manner of data generation where a CAD model is
sampled twice for the source and reference, avoiding the previously prevalent
over-fitting issue. Experimental results show that our method achieves
state-of-the-art performance compared to traditional and deep learning based
methods. Code is available at https://github.com/megvii-research/OMNet. | [
"cs.CV"
] |
Video object detection is challenging because objects that are easily
detected in one frame may be difficult to detect in another frame within the
same clip. Recently, there have been major advances for doing object detection
in a single image. These methods typically contain three phases: (i) object
proposal generation (ii) object classification and (iii) post-processing. We
propose a modification of the post-processing phase that uses high-scoring
object detections from nearby frames to boost scores of weaker detections
within the same clip. We show that our method obtains superior results to
state-of-the-art single image object detection techniques. Our method placed
3rd in the video object detection (VID) task of the ImageNet Large Scale Visual
Recognition Challenge 2015 (ILSVRC2015). | [
"cs.CV"
] |
In this paper, we propose a new deep learning-based approach for
disentangling face identity representations from expressive 3D faces. Given a
3D face, our approach not only extracts a disentangled identity representation
but also generates a realistic 3D face with a neutral expression while
predicting its identity. The proposed network consists of three components; (1)
a Graph Convolutional Autoencoder (GCA) to encode the 3D faces into latent
representations, (2) a Generative Adversarial Network (GAN) that translates the
latent representations of expressive faces into those of neutral faces, (3) and
an identity recognition sub-network taking advantage of the neutralized latent
representations for 3D face recognition. The whole network is trained in an
end-to-end manner. Experiments are conducted on three publicly available
datasets showing the effectiveness of the proposed approach. | [
"cs.CV"
] |
We formulate object segmentation in video as a graph partitioning problem in
space and time, in which nodes are pixels and their relations form local
neighborhoods. We claim that the strongest cluster in this pixel-level graph
represents the salient object segmentation. We compute the main cluster using a
novel and fast 3D filtering technique that finds the spectral clustering
solution, namely the principal eigenvector of the graph's adjacency matrix,
without building the matrix explicitly - which would be intractable. Our method
is based on the power iteration for finding the principal eigenvector of a
matrix, which we prove is equivalent to performing a specific set of 3D
convolutions in the space-time feature volume. This allows us to avoid creating
the matrix and have a fast parallel implementation on GPU. We show that our
method is much faster than classical power iteration applied directly on the
adjacency matrix. Different from other works, ours is dedicated to preserving
object consistency in space and time at the level of pixels. For that, it
requires powerful pixel-wise features at the frame level. This makes it
perfectly suitable for incorporating the output of a backbone network or other
methods and fast-improving over their solution without supervision. In
experiments, we obtain consistent improvement, with the same set of
hyper-parameters, over the top state of the art methods on DAVIS-2016 dataset,
both in unsupervised and semi-supervised tasks. We also achieve top results on
the well-known SegTrackv2 dataset. | [
"cs.CV"
] |
We propose a Small, Accurate, and Fast Re-ID (SAFR) design for flexible
vehicle re-id under a variety of compute environments such as cloud, mobile,
edge, or embedded devices by only changing the re-id model backbone. Through
best-fit design choices, feature extraction, training tricks, global attention,
and local attention, we create a reid model design that optimizes
multi-dimensionally along model size, speed, & accuracy for deployment under
various memory and compute constraints. We present several variations of our
flexible SAFR model: SAFR-Large for cloud-type environments with large compute
resources, SAFR-Small for mobile devices with some compute constraints, and
SAFR-Micro for edge devices with severe memory & compute constraints.
SAFR-Large delivers state-of-the-art results with mAP 81.34 on the VeRi-776
vehicle re-id dataset (15% better than related work). SAFR-Small trades a 5.2%
drop in performance (mAP 77.14 on VeRi-776) for over 60% model compression and
150% speedup. SAFR-Micro, at only 6MB and 130MFLOPS, trades 6.8% drop in
accuracy (mAP 75.80 on VeRi-776) for 95% compression and 33x speedup compared
to SAFR-Large. | [
"cs.CV",
"cs.IR",
"cs.LG"
] |
We propose a theoretical approach towards the training numerical stability of
Variational AutoEncoders (VAE). Our work is motivated by recent studies
empowering VAEs to reach state of the art generative results on complex image
datasets. These very deep VAE architectures, as well as VAEs using more complex
output distributions, highlight a tendency to haphazardly produce high training
gradients as well as NaN losses. The empirical fixes proposed to train them
despite their limitations are neither fully theoretically grounded nor
generally sufficient in practice. Building on this, we localize the source of
the problem at the interface between the model's neural networks and their
output probabilistic distributions. We explain a common source of instability
stemming from an incautious formulation of the encoded Normal distribution's
variance, and apply the same approach on other, less obvious sources. We show
that by implementing small changes to the way we parameterize the Normal
distributions on which they rely, VAEs can securely be trained. | [
"cs.LG",
"cs.CV"
] |
In recent years, correlation filter based trackers (CF trackers) have
attracted much attention from the vision community because of their top
performance in both localization accuracy and efficiency. The society of visual
tracking, however, still needs to deal with the following difficulty on CF
trackers: avoiding or eliminating the boundary effect completely, in the
meantime, exploiting non-linear kernels and running efficiently. In this paper,
we propose a fast kernelized correlation filter without boundary effect
(nBEKCF) to solve this problem. To avoid the boundary effect thoroughly, a set
of \emph{real} and \emph{dense} patches is sampled through the traditional
sliding window and used as the training samples to train nBEKCF to fit a
Gaussian response map. Non-linear kernels can be applied naturally in nBEKCF
due to its different theoretical foundation from the existing CF trackers'. To
achieve the fast training and detection, a set of cyclic bases is introduced to
construct the filter. Two algorithms, ACSII and CCIM, are developed to
significantly accelerate the calculation of kernel correlation matrices. ACSII
and CCIM fully exploit the density of training samples and cyclic structure of
bases, and totally run in space domain. The efficiency of CCIM exceeds that of
the FFT counterpart remarkably in our task. Extensive experiments on six public
datasets, OTB-2013, OTB-2015, NfS, VOT2018, GOT10k, and TrackingNet, show that
compared to the CF trackers designed to relax the boundary effect, BACF and
SRDCF, our nBEKCF achieves higher localization accuracy without tricks, in the
meanwhile, runs at higher FPS. | [
"cs.CV"
] |
Machine Learning requires large amounts of labeled data to fit a model. Many
datasets are already publicly available, nevertheless forcing application
possibilities of machine learning to the domains of those public datasets. The
ever-growing penetration of machine learning algorithms in new application
areas requires solutions for the need for data in those new domains. This
thesis works on active learning as one possible solution to reduce the amount
of data that needs to be processed by hand, by processing only those datapoints
that specifically benefit the training of a strong model for the task. A newly
proposed framework for framing the active learning workflow as a reinforcement
learning problem is adapted for image classification and a series of three
experiments is conducted. Each experiment is evaluated and potential issues
with the approach are outlined. Each following experiment then proposes
improvements to the framework and evaluates their impact. After the last
experiment, a final conclusion is drawn, unfortunately rejecting this work's
hypothesis and outlining that the proposed framework at the moment is not
capable of improving active learning for image classification with a trained
reinforcement learning agent. | [
"cs.LG"
] |
Hardware and neural architecture co-search that automatically generates
Artificial Intelligence (AI) solutions from a given dataset is promising to
promote AI democratization; however, the amount of time that is required by
current co-search frameworks is in the order of hundreds of GPU hours for one
target hardware. This inhibits the use of such frameworks on commodity
hardware. The root cause of the low efficiency in existing co-search frameworks
is the fact that they start from a "cold" state (i.e., search from scratch). In
this paper, we propose a novel framework, namely HotNAS, that starts from a
"hot" state based on a set of existing pre-trained models (a.k.a. model zoo) to
avoid lengthy training time. As such, the search time can be reduced from 200
GPU hours to less than 3 GPU hours. In HotNAS, in addition to hardware design
space and neural architecture search space, we further integrate a compression
space to conduct model compressing during the co-search, which creates new
opportunities to reduce latency but also brings challenges. One of the key
challenges is that all of the above search spaces are coupled with each other,
e.g., compression may not work without hardware design support. To tackle this
issue, HotNAS builds a chain of tools to design hardware to support
compression, based on which a global optimizer is developed to automatically
co-search all the involved search spaces. Experiments on ImageNet dataset and
Xilinx FPGA show that, within the timing constraint of 5ms, neural
architectures generated by HotNAS can achieve up to 5.79% Top-1 and 3.97% Top-5
accuracy gain, compared with the existing ones. | [
"cs.LG",
"cs.NE",
"eess.SP",
"stat.ML"
] |
Generative adversarial networks (GANs) have shown remarkable success in
generation of unstructured data, such as, natural images. However, discovery
and separation of modes in the generated space, essential for several tasks
beyond naive data generation, is still a challenge. In this paper, we address
the problem of imposing desired modal properties on the generated space using a
latent distribution, engineered in accordance with the modal properties of the
true data distribution. This is achieved by training a latent space inversion
network in tandem with the generative network using a divergence loss. The
latent space is made to follow a continuous multimodal distribution generated
by reparameterization of a pair of continuous and discrete random variables. In
addition, the modal priors of the latent distribution are learned to match with
the true data distribution using minimal-supervision with negligible increment
in number of learnable parameters. We validate our method on multiple tasks
such as mode separation, conditional generation, and attribute discovery on
multiple real world image datasets and demonstrate its efficacy over other
state-of-the-art methods. | [
"cs.CV",
"cs.LG"
] |
Collaborative filtering (CF) aims to predict users' ratings on items
according to historical user-item preference data. In many real-world
applications, preference data are usually sparse, which would make models
overfit and fail to give accurate predictions. Recently, several research works
show that by transferring knowledge from some manually selected source domains,
the data sparseness problem could be mitigated. However for most cases, parts
of source domain data are not consistent with the observations in the target
domain, which may misguide the target domain model building. In this paper, we
propose a novel criterion based on empirical prediction error and its variance
to better capture the consistency across domains in CF settings. Consequently,
we embed this criterion into a boosting framework to perform selective
knowledge transfer. Comparing to several state-of-the-art methods, we show that
our proposed selective transfer learning framework can significantly improve
the accuracy of rating prediction tasks on several real-world recommendation
tasks. | [
"cs.LG",
"cs.IR",
"stat.ML"
] |
Selecting an optimizer is a central step in the contemporary deep learning
pipeline. In this paper, we demonstrate the sensitivity of optimizer
comparisons to the hyperparameter tuning protocol. Our findings suggest that
the hyperparameter search space may be the single most important factor
explaining the rankings obtained by recent empirical comparisons in the
literature. In fact, we show that these results can be contradicted when
hyperparameter search spaces are changed. As tuning effort grows without bound,
more general optimizers should never underperform the ones they can approximate
(i.e., Adam should never perform worse than momentum), but recent attempts to
compare optimizers either assume these inclusion relationships are not
practically relevant or restrict the hyperparameters in ways that break the
inclusions. In our experiments, we find that inclusion relationships between
optimizers matter in practice and always predict optimizer comparisons. In
particular, we find that the popular adaptive gradient methods never
underperform momentum or gradient descent. We also report practical tips around
tuning often ignored hyperparameters of adaptive gradient methods and raise
concerns about fairly benchmarking optimizers for neural network training. | [
"cs.LG",
"stat.ML"
] |
Channel pruning is one of the important methods for deep model compression.
Most of existing pruning methods mainly focus on classification. Few of them
conduct systematic research on object detection. However, object detection is
different from classification, which requires not only semantic information but
also localization information. In this paper, based on discrimination-aware
channel pruning (DCP) which is state-of-the-art pruning method for
classification, we propose a localization-aware auxiliary network to find out
the channels with key information for classification and regression so that we
can conduct channel pruning directly for object detection, which saves lots of
time and computing resources. In order to capture the localization information,
we first design the auxiliary network with a contextual ROIAlign layer which
can obtain precise localization information of the default boxes by pixel
alignment and enlarges the receptive fields of the default boxes when pruning
shallow layers. Then, we construct a loss function for object detection task
which tends to keep the channels that contain the key information for
classification and regression. Extensive experiments demonstrate the
effectiveness of our method. On MS COCO, we prune 70\% parameters of the SSD
based on ResNet-50 with modest accuracy drop, which outperforms
the-state-of-art method. | [
"cs.CV"
] |
Echo State Networks (ESNs) are known for their fast and precise one-shot
learning of time series. But they often need good hyper-parameter tuning for
best performance. For this good validation is key, but usually, a single
validation split is used. In this rather practical contribution we suggest
several schemes for cross-validating ESNs and introduce an efficient algorithm
for implementing them. The component that dominates the time complexity of the
already quite fast ESN training remains constant (does not scale up with $k$)
in our proposed method of doing $k$-fold cross-validation. The component that
does scale linearly with $k$ starts dominating only in some not very common
situations. Thus in many situations $k$-fold cross-validation of ESNs can be
done for virtually the same time complexity as a simple single split
validation. Space complexity can also remain the same. We also discuss when the
proposed validation schemes for ESNs could be beneficial and empirically
investigate them on several different real-world datasets. | [
"cs.LG",
"stat.ML",
"68T05 (Primary) 37M10, 15A06 (Secondary)",
"I.2.6"
] |
Feature pyramid network (FPN) is a critical component in modern object
detection frameworks. The performance gain in most of the existing FPN variants
is mainly attributed to the increase of computational burden. An attempt to
enhance the FPN is enriching the spatial information by expanding the receptive
fields, which is promising to largely improve the detection accuracy. In this
paper, we first investigate how expanding the receptive fields affect the
accuracy and computational costs of FPN. We explore a baseline model called
inception FPN in which each lateral connection contains convolution filters
with different kernel sizes. Moreover, we point out that not all objects need
such a complicated calculation and propose a new dynamic FPN (DyFPN). The
output features of DyFPN will be calculated by using the adaptively selected
branch according to a dynamic gating operation. Therefore, the proposed method
can provide a more efficient dynamic inference for achieving a better trade-off
between accuracy and computational cost. Extensive experiments conducted on
MS-COCO benchmark demonstrate that the proposed DyFPN significantly improves
performance with the optimal allocation of computation resources. For instance,
replacing inception FPN with DyFPN reduces about 40% of its FLOPs while
maintaining similar high performance. | [
"cs.CV"
] |
Ground filtering has remained a widely studied but incompletely resolved
bottleneck for decades in the automatic generation of high-precision digital
elevation model, due to the dramatic changes of topography and the complex
structures of objects. The recent breakthrough of supervised deep learning
algorithms in 3D scene understanding brings new solutions for better solving
such problems. However, there are few large-scale and scene-rich public
datasets dedicated to ground extraction, which considerably limits the
development of effective deep-learning-based ground filtering methods. To this
end, we present OpenGF, first Ultra-Large-Scale Ground Filtering dataset
covering over 47 $km^2$ of 9 different typical terrain scenes built upon open
ALS point clouds of 4 different countries around the world. OpenGF contains
more than half a billion finely labeled ground and non-ground points, thousands
of times the number of labeled points than the de facto standard ISPRS
filtertest dataset. We extensively evaluate the performance of state-of-the-art
rule-based algorithms and 3D semantic segmentation networks on our dataset and
provide a comprehensive analysis. The results have confirmed the capability of
OpenGF to train deep learning models effectively. This dataset is released at
https://github.com/Nathan-UW/OpenGF to promote more advancing research for
ground filtering and large-scale 3D geographic environment understanding. | [
"cs.CV"
] |
The problem of Scene flow estimation in depth videos has been attracting
attention of researchers of robot vision, due to its potential application in
various areas of robotics. The conventional scene flow methods are difficult to
use in reallife applications due to their long computational overhead. We
propose a conditional adversarial network SceneFlowGAN for scene flow
estimation. The proposed SceneFlowGAN uses loss function at two ends: both
generator and descriptor ends. The proposed network is the first attempt to
estimate scene flow using generative adversarial networks, and is able to
estimate both the optical flow and disparity from the input stereo images
simultaneously. The proposed method is experimented on a large RGB-D benchmark
sceneflow dataset. | [
"cs.CV",
"cs.LG",
"cs.RO",
"eess.IV"
] |
Although deep learning has been applied to successfully address many data
mining problems, relatively limited work has been done on deep learning for
anomaly detection. Existing deep anomaly detection methods, which focus on
learning new feature representations to enable downstream anomaly detection
methods, perform indirect optimization of anomaly scores, leading to
data-inefficient learning and suboptimal anomaly scoring. Also, they are
typically designed as unsupervised learning due to the lack of large-scale
labeled anomaly data. As a result, they are difficult to leverage prior
knowledge (e.g., a few labeled anomalies) when such information is available as
in many real-world anomaly detection applications.
This paper introduces a novel anomaly detection framework and its
instantiation to address these problems. Instead of representation learning,
our method fulfills an end-to-end learning of anomaly scores by a neural
deviation learning, in which we leverage a few (e.g., multiple to dozens)
labeled anomalies and a prior probability to enforce statistically significant
deviations of the anomaly scores of anomalies from that of normal data objects
in the upper tail. Extensive results show that our method can be trained
substantially more data-efficiently and achieves significantly better anomaly
scoring than state-of-the-art competing methods. | [
"cs.LG",
"stat.ML"
] |
Surgical data science is a new research field that aims to observe all
aspects of the patient treatment process in order to provide the right
assistance at the right time. Due to the breakthrough successes of deep
learning-based solutions for automatic image annotation, the availability of
reference annotations for algorithm training is becoming a major bottleneck in
the field. The purpose of this paper was to investigate the concept of
self-supervised learning to address this issue.
Our approach is guided by the hypothesis that unlabeled video data can be
used to learn a representation of the target domain that boosts the performance
of state-of-the-art machine learning algorithms when used for pre-training.
Core of the method is an auxiliary task based on raw endoscopic video data of
the target domain that is used to initialize the convolutional neural network
(CNN) for the target task. In this paper, we propose the re-colorization of
medical images with a generative adversarial network (GAN)-based architecture
as auxiliary task. A variant of the method involves a second pre-training step
based on labeled data for the target task from a related domain. We validate
both variants using medical instrument segmentation as target task.
The proposed approach can be used to radically reduce the manual annotation
effort involved in training CNNs. Compared to the baseline approach of
generating annotated data from scratch, our method decreases exploratively the
number of labeled images by up to 75% without sacrificing performance. Our
method also outperforms alternative methods for CNN pre-training, such as
pre-training on publicly available non-medical or medical data using the target
task (in this instance: segmentation).
As it makes efficient use of available (non-)public and (un-)labeled data,
the approach has the potential to become a valuable tool for CNN
(pre-)training. | [
"cs.CV"
] |
Urban areas provide us with a treasure trove of available data capturing
almost every aspect of a population's life. This work focuses on mobility data
and how it will help improve our understanding of urban mobility patterns.
Readily available and sizable farecard data captures trips in a public
transportation network. However, such data typically lacks temporal modalities
and as such the task of inferring trip semantic, station function, and user
profile is quite challenging. As existing approaches either focus on
station-level or user-level signals, they are prone to overfitting and generate
less credible and insightful results. To properly learn such characteristics
from trip data, we propose a Collective Learning Framework through Latent
Representation, which augments user-level learning with collective patterns
learned from station-level signals. This framework uses a novel, so-called
Tidal-Regularized Non-negative Matrix Factorization method, which incorporates
domain knowledge in the form of temporal passenger flow patterns in generic
Non-negative Matrix Factorization. To evaluate our model performance, a user
stability test based on the classical Rand Index is introduced as a metric to
benchmark different unsupervised learning models. We provide a qualitative
analysis of the station functions and user profiles for the Washington D.C.
metro and show how our method supports spatiotemporal intra-city mobility
exploration. | [
"cs.LG",
"cs.SI"
] |
Throughout the past five years, the susceptibility of neural networks to
minimal adversarial perturbations has moved from a peculiar phenomenon to a
core issue in Deep Learning. Despite much attention, however, progress towards
more robust models is significantly impaired by the difficulty of evaluating
the robustness of neural network models. Today's methods are either fast but
brittle (gradient-based attacks), or they are fairly reliable but slow (score-
and decision-based attacks). We here develop a new set of gradient-based
adversarial attacks which (a) are more reliable in the face of gradient-masking
than other gradient-based attacks, (b) perform better and are more query
efficient than current state-of-the-art gradient-based attacks, (c) can be
flexibly adapted to a wide range of adversarial criteria and (d) require
virtually no hyperparameter tuning. These findings are carefully validated
across a diverse set of six different models and hold for L0, L1, L2 and Linf
in both targeted as well as untargeted scenarios. Implementations will soon be
available in all major toolboxes (Foolbox, CleverHans and ART). We hope that
this class of attacks will make robustness evaluations easier and more
reliable, thus contributing to more signal in the search for more robust
machine learning models. | [
"stat.ML",
"cs.CR",
"cs.CV",
"cs.LG",
"cs.NE"
] |
Capsule networks (CapsNets) have recently shown promise to excel in most
computer vision tasks, especially pertaining to scene understanding. In this
paper, we explore CapsNet's capabilities in optical flow estimation, a task at
which convolutional neural networks (CNNs) have already outperformed other
approaches. We propose a CapsNet-based architecture, termed FlowCaps, which
attempts to a) achieve better correspondence matching via finer-grained,
motion-specific, and more-interpretable encoding crucial for optical flow
estimation, b) perform better-generalizable optical flow estimation, c) utilize
lesser ground truth data, and d) significantly reduce the computational
complexity in achieving good performance, in comparison to its
CNN-counterparts. | [
"cs.CV"
] |
Most of the proposed person re-identification algorithms conduct supervised
training and testing on single labeled datasets with small size, so directly
deploying these trained models to a large-scale real-world camera network may
lead to poor performance due to underfitting. It is challenging to
incrementally optimize the models by using the abundant unlabeled data
collected from the target domain. To address this challenge, we propose an
unsupervised incremental learning algorithm, TFusion, which is aided by the
transfer learning of the pedestrians' spatio-temporal patterns in the target
domain. Specifically, the algorithm firstly transfers the visual classifier
trained from small labeled source dataset to the unlabeled target dataset so as
to learn the pedestrians' spatial-temporal patterns. Secondly, a Bayesian
fusion model is proposed to combine the learned spatio-temporal patterns with
visual features to achieve a significantly improved classifier. Finally, we
propose a learning-to-rank based mutual promotion procedure to incrementally
optimize the classifiers based on the unlabeled data in the target domain.
Comprehensive experiments based on multiple real surveillance datasets are
conducted, and the results show that our algorithm gains significant
improvement compared with the state-of-art cross-dataset unsupervised person
re-identification algorithms. | [
"cs.CV"
] |
Subsets and Splits