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Recent advances in Transformer models allow for unprecedented sequence
lengths, due to linear space and time complexity. In the meantime, relative
positional encoding (RPE) was proposed as beneficial for classical Transformers
and consists in exploiting lags instead of absolute positions for inference.
Still, RPE is not available for the recent linear-variants of the Transformer,
because it requires the explicit computation of the attention matrix, which is
precisely what is avoided by such methods. In this paper, we bridge this gap
and present Stochastic Positional Encoding as a way to generate PE that can be
used as a replacement to the classical additive (sinusoidal) PE and provably
behaves like RPE. The main theoretical contribution is to make a connection
between positional encoding and cross-covariance structures of correlated
Gaussian processes. We illustrate the performance of our approach on the
Long-Range Arena benchmark and on music generation. | [
"cs.LG",
"cs.CL",
"cs.SD",
"eess.AS",
"stat.ML"
] |
Discrete flow-based models are a recently proposed class of generative models
that learn invertible transformations for discrete random variables. Since they
do not require data dequantization and maximize an exact likelihood objective,
they can be used in a straight-forward manner for lossless compression. In this
paper, we introduce a new discrete flow-based model for categorical random
variables: Discrete Denoising Flows (DDFs). In contrast with other discrete
flow-based models, our model can be locally trained without introducing
gradient bias. We show that DDFs outperform Discrete Flows on modeling a toy
example, binary MNIST and Cityscapes segmentation maps, measured in
log-likelihood. | [
"cs.LG"
] |
Deep neural networks have exhibited promising performance in image
super-resolution (SR). Most SR models follow a hierarchical architecture that
contains both the cell-level design of computational blocks and the
network-level design of the positions of upsampling blocks. However, designing
SR models heavily relies on human expertise and is very labor-intensive. More
critically, these SR models often contain a huge number of parameters and may
not meet the requirements of computation resources in real-world applications.
To address the above issues, we propose a Hierarchical Neural Architecture
Search (HNAS) method to automatically design promising architectures with
different requirements of computation cost. To this end, we design a
hierarchical SR search space and propose a hierarchical controller for
architecture search. Such a hierarchical controller is able to simultaneously
find promising cell-level blocks and network-level positions of upsampling
layers. Moreover, to design compact architectures with promising performance,
we build a joint reward by considering both the performance and computation
cost to guide the search process. Extensive experiments on five benchmark
datasets demonstrate the superiority of our method over existing methods. | [
"cs.CV"
] |
Drones shooting can be applied in dynamic traffic monitoring, object
detecting and tracking, and other vision tasks. The variability of the shooting
location adds some intractable challenges to these missions, such as varying
scale, unstable exposure, and scene migration. In this paper, we strive to
tackle the above challenges and automatically understand the crowd from the
visual data collected from drones. First, to alleviate the background noise
generated in cross-scene testing, a double-stream crowd counting model is
proposed, which extracts optical flow and frame difference information as an
additional branch. Besides, to improve the model's generalization ability at
different scales and time, we randomly combine a variety of data transformation
methods to simulate some unseen environments. To tackle the crowd density
estimation problem under extreme dark environments, we introduce synthetic data
generated by game Grand Theft Auto V(GTAV). Experiment results show the
effectiveness of the virtual data. Our method wins the challenge with a mean
absolute error (MAE) of 12.70. Moreover, a comprehensive ablation study is
conducted to explore each component's contribution. | [
"cs.CV"
] |
We introduce a simple permutation equivariant layer for deep learning with
set structure.This type of layer, obtained by parameter-sharing, has a simple
implementation and linear-time complexity in the size of each set. We use deep
permutation-invariant networks to perform point-could classification and
MNIST-digit summation, where in both cases the output is invariant to
permutations of the input. In a semi-supervised setting, where the goal is make
predictions for each instance within a set, we demonstrate the usefulness of
this type of layer in set-outlier detection as well as semi-supervised learning
with clustering side-information. | [
"stat.ML",
"cs.LG",
"cs.NE"
] |
We propose a fast and accurate method of 6D object pose estimation for
bin-picking of mechanical parts by a robot manipulator. We extend the
single-shot approach to stereo vision by application of attention architecture.
Our convolutional neural network model regresses to object locations and
rotations from either a left image or a right image without depth information.
Then, a stereo feature matching module, designated as Stereo Grid Attention,
generates stereo grid matching maps. The important point of our method is only
to calculate disparity of the objects found by the attention from stereo
images, instead of calculating a point cloud over the entire image. The
disparity value is then used to calculate the depth to the objects by the
principle of triangulation. Our method also achieves a rapid processing speed
of pose estimation by the single-shot architecture and it is possible to
process a 1024 x 1024 pixels image in 75 milliseconds on the Jetson AGX Xavier
implemented with half-float model. Weakly textured mechanical parts are used to
exemplify the method. First, we create original synthetic datasets for training
and evaluating of the proposed model. This dataset is created by capturing and
rendering numerous 3D models of several types of mechanical parts in virtual
space. Finally, we use a robotic manipulator with an electromagnetic gripper to
pick up the mechanical parts in a cluttered state to verify the validity of our
method in an actual scene. When a raw stereo image is used by the proposed
method from our stereo camera to detect black steel screws, stainless screws,
and DC motor parts, i.e., cases, rotor cores and commutator caps, the
bin-picking tasks are successful with 76.3%, 64.0%, 50.5%, 89.1% and 64.2%
probability, respectively. | [
"cs.CV",
"cs.RO"
] |
Embeddings of high-dimensional data are widely used to explore data, to
verify analysis results, and to communicate information. Their explanation, in
particular with respect to the input attributes, is often difficult. With
linear projects like PCA the axes can still be annotated meaningfully. With
non-linear projections this is no longer possible and alternative strategies
such as attribute-based color coding are required. In this paper, we review
existing augmentation techniques and discuss their limitations. We present the
Non-Linear Embeddings Surveyor (NoLiES) that combines a novel augmentation
strategy for projected data (rangesets) with interactive analysis in a small
multiples setting. Rangesets use a set-based visualization approach for binned
attribute values that enable the user to quickly observe structure and detect
outliers. We detail the link between algebraic topology and rangesets and
demonstrate the utility of NoLiES in case studies with various challenges
(complex attribute value distribution, many attributes, many data points) and a
real-world application to understand latent features of matrix completion in
thermodynamics. | [
"cs.LG",
"cs.AI"
] |
3D scene flow estimation is a vital tool in perceiving our environment given
depth or range sensors. Unlike optical flow, the data is usually sparse and in
most cases partially occluded in between two temporal samplings. Here we
propose a new scene flow architecture called OGSF-Net which tightly couples the
learning for both flow and occlusions between frames. Their coupled symbiosis
results in a more accurate prediction of flow in space. Unlike a traditional
multi-action network, our unified approach is fused throughout the network,
boosting performances for both occlusion detection and flow estimation. Our
architecture is the first to gauge the occlusion in 3D scene flow estimation on
point clouds. In key datasets such as Flyingthings3D and KITTI, we achieve the
state-of-the-art results. | [
"cs.CV",
"cs.LG"
] |
Overestimation of the maximum action-value is a well-known problem that
hinders Q-Learning performance, leading to suboptimal policies and unstable
learning. Among several Q-Learning variants proposed to address this issue,
Weighted Q-Learning (WQL) effectively reduces the bias and shows remarkable
results in stochastic environments. WQL uses a weighted sum of the estimated
action-values, where the weights correspond to the probability of each
action-value being the maximum; however, the computation of these probabilities
is only practical in the tabular settings. In this work, we provide the
methodological advances to benefit from the WQL properties in Deep
Reinforcement Learning (DRL), by using neural networks with Dropout Variational
Inference as an effective approximation of deep Gaussian processes. In
particular, we adopt the Concrete Dropout variant to obtain calibrated
estimates of epistemic uncertainty in DRL. We show that model uncertainty in
DRL can be useful not only for action selection, but also action evaluation. We
analyze how the novel Weighted Deep Q-Learning algorithm reduces the bias
w.r.t. relevant baselines and provide empirical evidence of its advantages on
several representative benchmarks. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging
marker for cerebral small vessel disease, and have been shown to be related to
increased risk of various neurological diseases, including stroke and dementia.
Automatic quantification of EPVS would greatly help to advance research into
its etiology and its potential as a risk indicator of disease. We propose a
convolutional network regression method to quantify the extent of EPVS in the
basal ganglia from 3D brain MRI. We first segment the basal ganglia and
subsequently apply a 3D convolutional regression network designed for small
object detection within this region of interest. The network takes an image as
input, and outputs a quantification score of EPVS. The network has
significantly more convolution operations than pooling ones and no final
activation, allowing it to span the space of real numbers. We validated our
approach using a dataset of 2000 brain MRI scans scored visually. Experiments
with varying sizes of training and test sets showed that a good performance can
be achieved with a training set of only 200 scans. With a training set of 1000
scans, the intraclass correlation coefficient (ICC) between our scoring method
and the expert's visual score was 0.74. Our method outperforms by a large
margin - more than 0.10 - four more conventional automated approaches based on
intensities, scale-invariant feature transform, and random forest. We show that
the network learns the structures of interest and investigate the influence of
hyper-parameters on the performance. We also evaluate the reproducibility of
our network using a set of 60 subjects scanned twice (scan-rescan
reproducibility). On this set our network achieves an ICC of 0.93, while the
intrarater agreement reaches 0.80. Furthermore, the automatic EPVS scoring
correlates similarly to age as visual scoring. | [
"cs.CV"
] |
To calculate the model accuracy on a computer vision task, e.g., object
recognition, we usually require a test set composing of test samples and their
ground truth labels. Whilst standard usage cases satisfy this requirement, many
real-world scenarios involve unlabeled test data, rendering common model
evaluation methods infeasible. We investigate this important and under-explored
problem, Automatic model Evaluation (AutoEval). Specifically, given a labeled
training set and a classifier, we aim to estimate the classification accuracy
on unlabeled test datasets. We construct a meta-dataset: a dataset comprised of
datasets generated from the original images via various transformations such as
rotation, background substitution, foreground scaling, etc. As the
classification accuracy of the model on each sample (dataset) is known from the
original dataset labels, our task can be solved via regression. Using the
feature statistics to represent the distribution of a sample dataset, we can
train regression models (e.g., a regression neural network) to predict model
performance. Using synthetic meta-dataset and real-world datasets in training
and testing, respectively, we report a reasonable and promising prediction of
the model accuracy. We also provide insights into the application scope,
limitation, and potential future direction of AutoEval. | [
"cs.CV"
] |
We study the problem of off-policy critic evaluation in several variants of
value-based off-policy actor-critic algorithms. Off-policy actor-critic
algorithms require an off-policy critic evaluation step, to estimate the value
of the new policy after every policy gradient update. Despite enormous success
of off-policy policy gradients on control tasks, existing general methods
suffer from high variance and instability, partly because the policy
improvement depends on gradient of the estimated value function. In this work,
we present a new way of off-policy policy evaluation in actor-critic, based on
the doubly robust estimators. We extend the doubly robust estimator from
off-policy policy evaluation (OPE) to actor-critic algorithms that consist of a
reward estimator performance model. We find that doubly robust estimation of
the critic can significantly improve performance in continuous control tasks.
Furthermore, in cases where the reward function is stochastic that can lead to
high variance, doubly robust critic estimation can improve performance under
corrupted, stochastic reward signals, indicating its usefulness for robust and
safe reinforcement learning. | [
"cs.LG",
"stat.ML"
] |
Tensor-network techniques have enjoyed outstanding success in physics, and
have recently attracted attention in machine learning, both as a tool for the
formulation of new learning algorithms and for enhancing the mathematical
understanding of existing methods. Inspired by these developments, and the
natural correspondence between tensor networks and probabilistic graphical
models, we provide a rigorous analysis of the expressive power of various
tensor-network factorizations of discrete multivariate probability
distributions. These factorizations include non-negative tensor-trains/MPS,
which are in correspondence with hidden Markov models, and Born machines, which
are naturally related to local quantum circuits. When used to model probability
distributions, they exhibit tractable likelihoods and admit efficient learning
algorithms. Interestingly, we prove that there exist probability distributions
for which there are unbounded separations between the resource requirements of
some of these tensor-network factorizations. Particularly surprising is the
fact that using complex instead of real tensors can lead to an arbitrarily
large reduction in the number of parameters of the network. Additionally, we
introduce locally purified states (LPS), a new factorization inspired by
techniques for the simulation of quantum systems, with provably better
expressive power than all other representations considered. The ramifications
of this result are explored through numerical experiments. Our findings imply
that LPS should be considered over hidden Markov models, and furthermore
provide guidelines for the design of local quantum circuits for probabilistic
modeling. | [
"cs.LG",
"cond-mat.str-el",
"math.OC",
"quant-ph",
"stat.ML"
] |
Automatic animation line art colorization is a challenging computer vision
problem, since the information of the line art is highly sparse and abstracted
and there exists a strict requirement for the color and style consistency
between frames. Recently, a lot of Generative Adversarial Network (GAN) based
image-to-image translation methods for single line art colorization have
emerged. They can generate perceptually appealing results conditioned on line
art images. However, these methods can not be adopted for the purpose of
animation colorization because there is a lack of consideration of the
in-between frame consistency. Existing methods simply input the previous
colored frame as a reference to color the next line art, which will mislead the
colorization due to the spatial misalignment of the previous colored frame and
the next line art especially at positions where apparent changes happen. To
address these challenges, we design a kind of correlation matching feature
transfer model (called CMFT) to align the colored reference feature in a
learnable way and integrate the model into an U-Net based generator in a
coarse-to-fine manner. This enables the generator to transfer the layer-wise
synchronized features from the deep semantic code to the content progressively.
Extension evaluation shows that CMFT model can effectively improve the
in-between consistency and the quality of colored frames especially when the
motion is intense and diverse. | [
"cs.CV",
"cs.GR",
"eess.IV"
] |
Automotive traffic scenes are complex due to the variety of possible
scenarios, objects, and weather conditions that need to be handled. In contrast
to more constrained environments, such as automated underground trains,
automotive perception systems cannot be tailored to a narrow field of specific
tasks but must handle an ever-changing environment with unforeseen events. As
currently no single sensor is able to reliably perceive all relevant activity
in the surroundings, sensor data fusion is applied to perceive as much
information as possible. Data fusion of different sensors and sensor modalities
on a low abstraction level enables the compensation of sensor weaknesses and
misdetections among the sensors before the information-rich sensor data are
compressed and thereby information is lost after a sensor-individual object
detection. This paper develops a low-level sensor fusion network for 3D object
detection, which fuses lidar, camera, and radar data. The fusion network is
trained and evaluated on the nuScenes data set. On the test set, fusion of
radar data increases the resulting AP (Average Precision) detection score by
about 5.1% in comparison to the baseline lidar network. The radar sensor fusion
proves especially beneficial in inclement conditions such as rain and night
scenes. Fusing additional camera data contributes positively only in
conjunction with the radar fusion, which shows that interdependencies of the
sensors are important for the detection result. Additionally, the paper
proposes a novel loss to handle the discontinuity of a simple yaw
representation for object detection. Our updated loss increases the detection
and orientation estimation performance for all sensor input configurations. The
code for this research has been made available on GitHub. | [
"cs.CV"
] |
In this paper, we propose an efficient and effective dense hybrid recurrent
multi-view stereo net with dynamic consistency checking, namely
$D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction. Our novel
hybrid recurrent multi-view stereo net consists of two core modules: 1) a light
DRENet (Dense Reception Expanded) module to extract dense feature maps of
original size with multi-scale context information, 2) a HU-LSTM (Hybrid
U-LSTM) to regularize 3D matching volume into predicted depth map, which
efficiently aggregates different scale information by coupling LSTM and U-Net
architecture. To further improve the accuracy and completeness of reconstructed
point clouds, we leverage a dynamic consistency checking strategy instead of
prefixed parameters and strategies widely adopted in existing methods for dense
point cloud reconstruction. In doing so, we dynamically aggregate geometric
consistency matching error among all the views. Our method ranks
\textbf{$1^{st}$} on the complex outdoor \textsl{Tanks and Temples} benchmark
over all the methods. Extensive experiments on the in-door DTU dataset show our
method exhibits competitive performance to the state-of-the-art method while
dramatically reduces memory consumption, which costs only $19.4\%$ of R-MVSNet
memory consumption. The codebase is available at
\hyperlink{https://github.com/yhw-yhw/D2HC-RMVSNet}{https://github.com/yhw-yhw/D2HC-RMVSNet}. | [
"cs.CV"
] |
In this paper we describe a new mobile architecture, MobileNetV2, that
improves the state of the art performance of mobile models on multiple tasks
and benchmarks as well as across a spectrum of different model sizes. We also
describe efficient ways of applying these mobile models to object detection in
a novel framework we call SSDLite. Additionally, we demonstrate how to build
mobile semantic segmentation models through a reduced form of DeepLabv3 which
we call Mobile DeepLabv3.
The MobileNetV2 architecture is based on an inverted residual structure where
the input and output of the residual block are thin bottleneck layers opposite
to traditional residual models which use expanded representations in the input
an MobileNetV2 uses lightweight depthwise convolutions to filter features in
the intermediate expansion layer. Additionally, we find that it is important to
remove non-linearities in the narrow layers in order to maintain
representational power. We demonstrate that this improves performance and
provide an intuition that led to this design. Finally, our approach allows
decoupling of the input/output domains from the expressiveness of the
transformation, which provides a convenient framework for further analysis. We
measure our performance on Imagenet classification, COCO object detection, VOC
image segmentation. We evaluate the trade-offs between accuracy, and number of
operations measured by multiply-adds (MAdd), as well as the number of
parameters | [
"cs.CV"
] |
While Deep Neural Networks (DNNs) achieve state-of-the-art results in many
different problem settings, they are affected by some crucial weaknesses. On
the one hand, DNNs depend on exploiting a vast amount of training data, whose
labeling process is time-consuming and expensive. On the other hand, DNNs are
often treated as black box systems, which complicates their evaluation and
validation. Both problems can be mitigated by incorporating prior knowledge
into the DNN.
One promising field, inspired by the success of convolutional neural networks
(CNNs) in computer vision tasks, is to incorporate knowledge about symmetric
geometrical transformations of the problem to solve. This promises an increased
data-efficiency and filter responses that are interpretable more easily. In
this survey, we try to give a concise overview about different approaches to
incorporate geometrical prior knowledge into DNNs. Additionally, we try to
connect those methods to the field of 3D object detection for autonomous
driving, where we expect promising results applying those methods. | [
"cs.CV",
"cs.LG"
] |
While supervised object detection and segmentation methods achieve impressive
accuracy, they generalize poorly to images whose appearance significantly
differs from the data they have been trained on. To address this when
annotating data is prohibitively expensive, we introduce a self-supervised
detection and segmentation approach that can work with single images captured
by a potentially moving camera. At the heart of our approach lies the
observation that object segmentation and background reconstruction are linked
tasks, and that, for structured scenes, background regions can be
re-synthesized from their surroundings, whereas regions depicting the moving
object cannot. We encode this intuition into a self-supervised loss function
that we exploit to train a proposal-based segmentation network. To account for
the discrete nature of the proposals, we develop a Monte Carlo-based training
strategy that allows the algorithm to explore the large space of object
proposals. We apply our method to human detection and segmentation in images
that visually depart from those of standard benchmarks and outperform existing
self-supervised methods. | [
"cs.CV"
] |
Existing domain adaptation methods aim at learning features that can be
generalized among domains. These methods commonly require to update source
classifier to adapt to the target domain and do not properly handle the trade
off between the source domain and the target domain. In this work, instead of
training a classifier to adapt to the target domain, we use a separable
component called data calibrator to help the fixed source classifier recover
discrimination power in the target domain, while preserving the source domain's
performance. When the difference between two domains is small, the source
classifier's representation is sufficient to perform well in the target domain
and outperforms GAN-based methods in digits. Otherwise, the proposed method can
leverage synthetic images generated by GANs to boost performance and achieve
state-of-the-art performance in digits datasets and driving scene semantic
segmentation. Our method empirically reveals that certain intriguing hints,
which can be mitigated by adversarial attack to domain discriminators, are one
of the sources for performance degradation under the domain shift. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
In this paper, we address the semantic segmentation task with a new context
aggregation scheme named \emph{object context}, which focuses on enhancing the
role of object information. Motivated by the fact that the category of each
pixel is inherited from the object it belongs to, we define the object context
for each pixel as the set of pixels that belong to the same category as the
given pixel in the image. We use a binary relation matrix to represent the
relationship between all pixels, where the value one indicates the two selected
pixels belong to the same category and zero otherwise.
We propose to use a dense relation matrix to serve as a surrogate for the
binary relation matrix. The dense relation matrix is capable to emphasize the
contribution of object information as the relation scores tend to be larger on
the object pixels than the other pixels. Considering that the dense relation
matrix estimation requires quadratic computation overhead and memory
consumption w.r.t. the input size, we propose an efficient interlaced sparse
self-attention scheme to model the dense relations between any two of all
pixels via the combination of two sparse relation matrices.
To capture richer context information, we further combine our interlaced
sparse self-attention scheme with the conventional multi-scale context schemes
including pyramid pooling~\citep{zhao2017pyramid} and atrous spatial pyramid
pooling~\citep{chen2018deeplab}. We empirically show the advantages of our
approach with competitive performances on five challenging benchmarks
including: Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff | [
"cs.CV"
] |
In this paper, we consider the problem to automatically reconstruct garment
and body shapes from a single near-front view RGB image. To this end, we
propose a layered garment representation on top of SMPL and novelly make the
skinning weight of garment independent of the body mesh, which significantly
improves the expression ability of our garment model. Compared with existing
methods, our method can support more garment categories and recover more
accurate geometry. To train our model, we construct two large scale datasets
with ground truth body and garment geometries as well as paired color images.
Compared with single mesh or non-parametric representation, our method can
achieve more flexible control with separate meshes, makes applications like
re-pose, garment transfer, and garment texture mapping possible. Code and some
data is available at https://github.com/jby1993/BCNet. | [
"cs.CV",
"cs.GR"
] |
Vision-language navigation (VLN) is the task of navigating an embodied agent
to carry out natural language instructions inside real 3D environments. In this
paper, we study how to address three critical challenges for this task: the
cross-modal grounding, the ill-posed feedback, and the generalization problems.
First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that
enforces cross-modal grounding both locally and globally via reinforcement
learning (RL). Particularly, a matching critic is used to provide an intrinsic
reward to encourage global matching between instructions and trajectories, and
a reasoning navigator is employed to perform cross-modal grounding in the local
visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model
significantly outperforms previous methods by 10% on SPL and achieves the new
state-of-the-art performance. To improve the generalizability of the learned
policy, we further introduce a Self-Supervised Imitation Learning (SIL) method
to explore unseen environments by imitating its own past, good decisions. We
demonstrate that SIL can approximate a better and more efficient policy, which
tremendously minimizes the success rate performance gap between seen and unseen
environments (from 30.7% to 11.7%). | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.RO"
] |
The utility of learning a dynamics/world model of the environment in
reinforcement learning has been shown in a many ways. When using neural
networks, however, these models suffer catastrophic forgetting when learned in
a lifelong or continual fashion. Current solutions to the continual learning
problem require experience to be segmented and labeled as discrete tasks,
however, in continuous experience it is generally unclear what a sufficient
segmentation of tasks would be. Here we propose a method to continually learn
these internal world models through the interleaving of internally generated
episodes of past experiences (i.e., pseudo-rehearsal). We show this method can
sequentially learn unsupervised temporal prediction, without task labels, in a
disparate set of Atari games. Empirically, this interleaving of the internally
generated rollouts with the external environment's observations leads to a
consistent reduction in temporal prediction loss compared to non-interleaved
learning and is preserved over repeated random exposures to various tasks.
Similarly, using a network distillation approach, we show that modern policy
gradient based reinforcement learning algorithms can use this internal model to
continually learn to optimize reward based on the world model's representation
of the environment. | [
"cs.LG",
"stat.ML",
"68T05 91E40"
] |
Autonomous vehicles are conceived to provide safe and secure services by
validating the safety standards as indicated by SOTIF-ISO/PAS-21448 (Safety of
the intended functionality). Keeping in this context, the perception of the
environment plays an instrumental role in conjunction with localization,
planning and control modules. As a pivotal algorithm in the perception stack,
object detection provides extensive insights into the autonomous vehicle's
surroundings. Camera and Lidar are extensively utilized for object detection
among different sensor modalities, but these exteroceptive sensors have
limitations in resolution and adverse weather conditions. In this work,
radar-based object detection is explored provides a counterpart sensor modality
to be deployed and used in adverse weather conditions. The radar gives complex
data; for this purpose, a channel boosting feature ensemble method with
transformer encoder-decoder network is proposed. The object detection task
using radar is formulated as a set prediction problem and evaluated on the
publicly available dataset in both good and good-bad weather conditions. The
proposed method's efficacy is extensively evaluated using the COCO evaluation
metric, and the best-proposed model surpasses its state-of-the-art counterpart
method by $12.55\%$ and $12.48\%$ in both good and good-bad weather conditions. | [
"cs.CV"
] |
Batch Normalization (BN) is a common technique used to speed-up and stabilize
training. On the other hand, the learnable parameters of BN are commonly used
in conditional Generative Adversarial Networks (cGANs) for representing
class-specific information using conditional Batch Normalization (cBN). In this
paper we propose to generalize both BN and cBN using a Whitening and Coloring
based batch normalization. We show that our conditional Coloring can represent
categorical conditioning information which largely helps the cGAN qualitative
results. Moreover, we show that full-feature whitening is important in a
general GAN scenario in which the training process is known to be highly
unstable. We test our approach on different datasets and using different GAN
networks and training protocols, showing a consistent improvement in all the
tested frameworks. Our CIFAR-10 conditioned results are higher than all
previous works on this dataset. | [
"stat.ML",
"cs.LG"
] |
In our work, we bridge deep neural network design with numerical differential
equations. We show that many effective networks, such as ResNet, PolyNet,
FractalNet and RevNet, can be interpreted as different numerical
discretizations of differential equations. This finding brings us a brand new
perspective on the design of effective deep architectures. We can take
advantage of the rich knowledge in numerical analysis to guide us in designing
new and potentially more effective deep networks. As an example, we propose a
linear multi-step architecture (LM-architecture) which is inspired by the
linear multi-step method solving ordinary differential equations. The
LM-architecture is an effective structure that can be used on any ResNet-like
networks. In particular, we demonstrate that LM-ResNet and LM-ResNeXt (i.e. the
networks obtained by applying the LM-architecture on ResNet and ResNeXt
respectively) can achieve noticeably higher accuracy than ResNet and ResNeXt on
both CIFAR and ImageNet with comparable numbers of trainable parameters. In
particular, on both CIFAR and ImageNet, LM-ResNet/LM-ResNeXt can significantly
compress ($>50$\%) the original networks while maintaining a similar
performance. This can be explained mathematically using the concept of modified
equation from numerical analysis. Last but not least, we also establish a
connection between stochastic control and noise injection in the training
process which helps to improve generalization of the networks. Furthermore, by
relating stochastic training strategy with stochastic dynamic system, we can
easily apply stochastic training to the networks with the LM-architecture. As
an example, we introduced stochastic depth to LM-ResNet and achieve significant
improvement over the original LM-ResNet on CIFAR10. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
The problem of computing category agnostic bounding box proposals is utilized
as a core component in many computer vision tasks and thus has lately attracted
a lot of attention. In this work we propose a new approach to tackle this
problem that is based on an active strategy for generating box proposals that
starts from a set of seed boxes, which are uniformly distributed on the image,
and then progressively moves its attention on the promising image areas where
it is more likely to discover well localized bounding box proposals. We call
our approach AttractioNet and a core component of it is a CNN-based category
agnostic object location refinement module that is capable of yielding accurate
and robust bounding box predictions regardless of the object category.
We extensively evaluate our AttractioNet approach on several image datasets
(i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on
all of them state-of-the-art results that surpass the previous work in the
field by a significant margin and also providing strong empirical evidence that
our approach is capable to generalize to unseen categories. Furthermore, we
evaluate our AttractioNet proposals in the context of the object detection task
using a VGG16-Net based detector and the achieved detection performance on COCO
manages to significantly surpass all other VGG16-Net based detectors while even
being competitive with a heavily tuned ResNet-101 based detector. Code as well
as box proposals computed for several datasets are available at::
https://github.com/gidariss/AttractioNet. | [
"cs.CV"
] |
Reward function design and exploration time are arguably the biggest
obstacles to the deployment of reinforcement learning (RL) agents in the real
world. In many real-world tasks, designing a reward function takes considerable
hand engineering and often requires additional sensors to be installed just to
measure whether the task has been executed successfully. Furthermore, many
interesting tasks consist of multiple implicit intermediate steps that must be
executed in sequence. Even when the final outcome can be measured, it does not
necessarily provide feedback on these intermediate steps. To address these
issues, we propose leveraging the abstraction power of intermediate visual
representations learned by deep models to quickly infer perceptual reward
functions from small numbers of demonstrations. We present a method that is
able to identify key intermediate steps of a task from only a handful of
demonstration sequences, and automatically identify the most discriminative
features for identifying these steps. This method makes use of the features in
a pre-trained deep model, but does not require any explicit specification of
sub-goals. The resulting reward functions can then be used by an RL agent to
learn to perform the task in real-world settings. To evaluate the learned
reward, we present qualitative results on two real-world tasks and a
quantitative evaluation against a human-designed reward function. We also show
that our method can be used to learn a real-world door opening skill using a
real robot, even when the demonstration used for reward learning is provided by
a human using their own hand. To our knowledge, these are the first results
showing that complex robotic manipulation skills can be learned directly and
without supervised labels from a video of a human performing the task.
Supplementary material and data are available at
https://sermanet.github.io/rewards | [
"cs.CV",
"cs.RO"
] |
Data are represented as graphs in a wide range of applications, such as
Computer Vision (e.g., images) and Graphics (e.g., 3D meshes), network analysis
(e.g., social networks), and bio-informatics (e.g., molecules). In this
context, our overall goal is the definition of novel Fourier-based and graph
filters induced by rational polynomials for graph processing, which generalise
polynomial filters and the Fourier transform to non-Euclidean domains. For the
efficient evaluation of discrete spectral Fourier-based and wavelet operators,
we introduce a spectrum-free approach, which requires the solution of a small
set of sparse, symmetric, well-conditioned linear systems and is oblivious of
the evaluation of the Laplacian or kernel spectrum. Approximating arbitrary
graph filters with rational polynomials provides a more accurate and
numerically stable alternative with respect to polynomials. To achieve these
goals, we also study the link between spectral operators, wavelets, and
filtered convolution with integral operators induced by spectral kernels.
According to our tests, main advantages of the proposed approach are (i) its
generality with respect to the input data (e.g., graphs, 3D shapes),
applications (e.g., signal reconstruction and smoothing, shape correspondence),
and filters (e.g., polynomial, rational polynomial), and (ii) a spectrum-free
computation with a generally low computational cost and storage overhead. | [
"cs.LG",
"cs.GR"
] |
Point cloud-based large scale place recognition is fundamental for many
applications like Simultaneous Localization and Mapping (SLAM). Although many
models have been proposed and have achieved good performance by learning
short-range local features, long-range contextual properties have often been
neglected. Moreover, the model size has also become a bottleneck for their wide
applications. To overcome these challenges, we propose a super light-weight
network model termed SVT-Net for large scale place recognition. Specifically,
on top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based
Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer
(CSVT) are proposed to learn both short-range local features and long-range
contextual features in this model. Consisting of ASVT and CSVT, SVT-Net can
achieve state-of-the-art on benchmark datasets in terms of both accuracy and
speed with a super-light model size (0.9M). Meanwhile, two simplified versions
of SVT-Net are introduced, which also achieve state-of-the-art and further
reduce the model size to 0.8M and 0.4M respectively. | [
"cs.CV"
] |
Convolutional neural networks have recently demonstrated interesting results
for single image super-resolution. However, these networks were trained to deal
with super-resolution problem on natural images. In this paper, we adapt a deep
network, which was proposed for natural images superresolution, to single text
image super-resolution. To evaluate the network, we present our database for
single text image super-resolution. Moreover, we propose to combine Gradient
Difference Loss (GDL) with L1/L2 loss to enhance edges in super-resolution
image. Quantitative and qualitative evaluations on our dataset show that adding
the GDL improves the super-resolution results. | [
"cs.CV"
] |
Object Transfiguration replaces an object in an image with another object
from a second image. For example it can perform tasks like "putting exactly
those eyeglasses from image A on the nose of the person in image B". Usage of
exemplar images allows more precise specification of desired modifications and
improves the diversity of conditional image generation. However, previous
methods that rely on feature space operations, require paired data and/or
appearance models for training or disentangling objects from background. In
this work, we propose a model that can learn object transfiguration from two
unpaired sets of images: one set containing images that "have" that kind of
object, and the other set being the opposite, with the mild constraint that the
objects be located approximately at the same place. For example, the training
data can be one set of reference face images that have eyeglasses, and another
set of images that have not, both of which spatially aligned by face landmarks.
Despite the weak 0/1 labels, our model can learn an "eyeglasses" subspace that
contain multiple representatives of different types of glasses. Consequently,
we can perform fine-grained control of generated images, like swapping the
glasses in two images by swapping the projected components in the "eyeglasses"
subspace, to create novel images of people wearing eyeglasses.
Overall, our deterministic generative model learns disentangled attribute
subspaces from weakly labeled data by adversarial training. Experiments on
CelebA and Multi-PIE datasets validate the effectiveness of the proposed model
on real world data, in generating images with specified eyeglasses, smiling,
hair styles, and lighting conditions etc. The code is available online. | [
"cs.CV",
"cs.GR"
] |
This paper investigates generalisation in multi-agent games, where the
generality of the agent can be evaluated by playing against opponents it hasn't
seen during training. We propose two new games with concealed information and
complex, non-transitive reward structure (think rock/paper/scissors). It turns
out that most current deep reinforcement learning methods fail to efficiently
explore the strategy space, thus learning policies that generalise poorly to
unseen opponents. We then propose a novel hierarchical agent architecture,
where the hierarchy is grounded in the game-theoretic structure of the game --
the top level chooses strategic responses to opponents, while the low level
implements them into policy over primitive actions. This grounding facilitates
credit assignment across the levels of hierarchy. Our experiments show that the
proposed hierarchical agent is capable of generalisation to unseen opponents,
while conventional baselines fail to generalise whatsoever. | [
"cs.LG",
"cs.AI",
"cs.MA",
"cs.NE",
"stat.ML"
] |
ADAPT is an open-source python library providing the implementation of
several domain adaptation methods. The library is suited for scikit-learn
estimator object (object which implement fit and predict methods) and
tensorflow models. Most of the implemented methods are developed in an
estimator agnostic fashion, offering various possibilities adapted to multiple
usage. The library offers three modules corresponding to the three principal
strategies of domain adaptation: (i) feature-based containing methods
performing feature transformation; (ii) instance-based with the implementation
of reweighting techniques and (iii) parameter-based proposing methods to adapt
pre-trained models to novel observations. A full documentation is proposed
online https://adapt-python.github.io/adapt/ with gallery of examples. Besides,
the library presents an high test coverage. | [
"cs.LG"
] |
Although deep convolutional networks have reached state-of-the-art
performance in many medical image segmentation tasks, they have typically
demonstrated poor generalisation capability. To be able to generalise from one
domain (e.g. one imaging modality) to another, domain adaptation has to be
performed. While supervised methods may lead to good performance, they require
to fully annotate additional data which may not be an option in practice. In
contrast, unsupervised methods don't need additional annotations but are
usually unstable and hard to train. In this work, we propose a novel
weakly-supervised method. Instead of requiring detailed but time-consuming
annotations, scribbles on the target domain are used to perform domain
adaptation. This paper introduces a new formulation of domain adaptation based
on structured learning and co-segmentation. Our method is easy to train, thanks
to the introduction of a regularised loss. The framework is validated on
Vestibular Schwannoma segmentation (T1 to T2 scans). Our proposed method
outperforms unsupervised approaches and achieves comparable performance to a
fully-supervised approach. | [
"cs.CV"
] |
Reinforcement Learning (RL) is essentially a trial-and-error learning
procedure which may cause unsafe behavior during the
exploration-and-exploitation process. This hinders the application of RL to
real-world control problems, especially to those for safety-critical systems.
In this paper, we introduce a framework for safe RL that is based on
integration of a RL algorithm with an add-on safety supervision module, called
the Robust Action Governor (RAG), which exploits set-theoretic techniques and
online optimization to manage safety-related requirements during learning. We
illustrate this proposed safe RL framework through an application to automotive
adaptive cruise control. | [
"cs.LG",
"cs.SY",
"eess.SY"
] |
To verify and validate networks, it is essential to gain insight into their
decisions, limitations as well as possible shortcomings of training data. In
this work, we propose a post-hoc, optimization based visual explanation method,
which highlights the evidence in the input image for a specific prediction. Our
approach is based on a novel technique to defend against adversarial evidence
(i.e. faulty evidence due to artefacts) by filtering gradients during
optimization. The defense does not depend on human-tuned parameters. It enables
explanations which are both fine-grained and preserve the characteristics of
images, such as edges and colors. The explanations are interpretable, suited
for visualizing detailed evidence and can be tested as they are valid model
inputs. We qualitatively and quantitatively evaluate our approach on a
multitude of models and datasets. | [
"cs.CV",
"cs.LG"
] |
Recently, data-driven single-view reconstruction methods have shown great
progress in modeling 3D dressed humans. However, such methods suffer heavily
from depth ambiguities and occlusions inherent to single view inputs. In this
paper, we address such issues by lifting the single-view input with additional
views and investigate the best strategy to suitably exploit information from
multiple views. We propose an end-to-end approach that learns an implicit 3D
representation of dressed humans from sparse camera views. Specifically, we
introduce two key components: first an attention-based fusion layer that learns
to aggregate visual information from several viewpoints; second a mechanism
that encodes local 3D patterns under the multi-view context. In the
experiments, we show the proposed approach outperforms the state of the art on
standard data both quantitatively and qualitatively. Additionally, we apply our
method on real data acquired with a multi-camera platform and demonstrate our
approach can obtain results comparable to multi-view stereo with dramatically
less views. | [
"cs.CV"
] |
We propose a novel technique to register sparse 3D scans in the absence of
texture. While existing methods such as KinectFusion or Iterative Closest
Points (ICP) heavily rely on dense point clouds, this task is particularly
challenging under sparse conditions without RGB data. Sparse texture-less data
does not come with high-quality boundary signal, and this prohibits the use of
correspondences from corners, junctions, or boundary lines. Moreover, in the
case of sparse data, it is incorrect to assume that the same point will be
captured in two consecutive scans. We take a different approach and first
re-parameterize the point-cloud using a large number of line segments. In this
re-parameterized data, there exists a large number of line intersection (and
not correspondence) constraints that allow us to solve the registration task.
We propose the use of a two-step alternating projection algorithm by
formulating the registration as the simultaneous satisfaction of intersection
and rigidity constraints. The proposed approach outperforms other top-scoring
algorithms on both Kinect and LiDAR datasets. In Kinect, we can use 100X
downsampled sparse data and still outperform competing methods operating on
full-resolution data. | [
"cs.CV",
"cs.RO"
] |
We present a novel facial expression recognition network, called Distract
your Attention Network (DAN). Our method is based on two key observations.
Firstly, multiple classes share inherently similar underlying facial
appearance, and their differences could be subtle. Secondly, facial expressions
exhibit themselves through multiple facial regions simultaneously, and the
recognition requires a holistic approach by encoding high-order interactions
among local features. To address these issues, we propose our DAN with three
key components: Feature Clustering Network (FCN), Multi-head cross Attention
Network (MAN), and Attention Fusion Network (AFN). The FCN extracts robust
features by adopting a large-margin learning objective to maximize class
separability. In addition, the MAN instantiates a number of attention heads to
simultaneously attend to multiple facial areas and build attention maps on
these regions. Further, the AFN distracts these attentions to multiple
locations before fusing the attention maps to a comprehensive one. Extensive
experiments on three public datasets (including AffectNet, RAF-DB, and SFEW
2.0) verified that the proposed method consistently achieves state-of-the-art
facial expression recognition performance. Code will be made available at
https://github.com/yaoing/DAN. | [
"cs.CV"
] |
The existence of adversarial examples in which an imperceptible change in the
input can fool well trained neural networks was experimentally discovered by
Szegedy et al in 2013, who called them "Intriguing properties of neural
networks". Since then, this topic had become one of the hottest research areas
within machine learning, but the ease with which we can switch between any two
decisions in targeted attacks is still far from being understood, and in
particular it is not clear which parameters determine the number of input
coordinates we have to change in order to mislead the network. In this paper we
develop a simple mathematical framework which enables us to think about this
baffling phenomenon from a fresh perspective, turning it into a natural
consequence of the geometry of $\mathbb{R}^n$ with the $L_0$ (Hamming) metric,
which can be quantitatively analyzed. In particular, we explain why we should
expect to find targeted adversarial examples with Hamming distance of roughly
$m$ in arbitrarily deep neural networks which are designed to distinguish
between $m$ input classes. | [
"cs.LG",
"cs.CR",
"stat.ML"
] |
We propose to adapt segmentation networks with a constrained formulation,
which embeds domain-invariant prior knowledge about the segmentation regions.
Such knowledge may take the form of simple anatomical information, e.g.,
structure size or shape, estimated from source samples or known a priori. Our
method imposes domain-invariant inequality constraints on the network outputs
of unlabeled target samples. It implicitly matches prediction statistics
between target and source domains with permitted uncertainty of prior
knowledge. We address our constrained problem with a differentiable penalty,
fully suited for standard stochastic gradient descent approaches, removing the
need for computationally expensive Lagrangian optimization with dual
projections. Unlike current two-step adversarial training, our formulation is
based on a single loss in a single network, which simplifies adaptation by
avoiding extra adversarial steps, while improving convergence and quality of
training.
The comparison of our approach with state-of-the-art adversarial methods
reveals substantially better performance on the challenging task of adapting
spine segmentation across different MRI modalities. Our results also show a
robustness to imprecision of size priors, approaching the accuracy of a fully
supervised model trained directly in a target domain.Our method can be readily
used for various constraints and segmentation problems. | [
"cs.CV"
] |
Three-dimensional (3D) biomedical image sets are often acquired with in-plane
pixel spacings that are far less than the out-of-plane spacings between images.
The resultant anisotropy, which can be detrimental in many applications, can be
decreased using image interpolation. Optical flow and/or other
registration-based interpolators have proven useful in such interpolation roles
in the past. When acquired images are comprised of signals that describe the
flow velocity of fluids, additional information is available to guide the
interpolation process. In this paper, we present an optical-flow based
framework for image interpolation that also minimizes resultant divergence in
the interpolated data. | [
"cs.CV"
] |
The adoption of electronic health records (EHR) has become universal during
the past decade, which has afforded in-depth data-based research. By learning
from the large amount of healthcare data, various data-driven models have been
built to predict future events for different medical tasks, such as auto
diagnosis and heart-attack prediction. Although EHR is abundant, the population
that satisfies specific criteria for learning population-specific tasks is
scarce, making it challenging to train data-hungry deep learning models. This
study presents the Claim Pre-Training (Claim-PT) framework, a generic
pre-training model that first trains on the entire pediatric claims dataset,
followed by a discriminative fine-tuning on each population-specific task. The
semantic meaning of medical events can be captured in the pre-training stage,
and the effective knowledge transfer is completed through the task-aware
fine-tuning stage. The fine-tuning process requires minimal parameter
modification without changing the model architecture, which mitigates the data
scarcity issue and helps train the deep learning model adequately on small
patient cohorts. We conducted experiments on a real-world claims dataset with
more than one million patient records. Experimental results on two downstream
tasks demonstrated the effectiveness of our method: our general task-agnostic
pre-training framework outperformed tailored task-specific models, achieving
more than 10\% higher in model performance as compared to baselines. In
addition, our framework showed a great generalizability potential to transfer
learned knowledge from one institution to another, paving the way for future
healthcare model pre-training across institutions. | [
"cs.LG",
"cs.AI"
] |
Identifying independently moving objects is an essential task for dynamic
scene understanding. However, traditional cameras used in dynamic scenes may
suffer from motion blur or exposure artifacts due to their sampling principle.
By contrast, event-based cameras are novel bio-inspired sensors that offer
advantages to overcome such limitations. They report pixel-wise intensity
changes asynchronously, which enables them to acquire visual information at
exactly the same rate as the scene dynamics. We have developed a method to
identify independently moving objects acquired with an event-based camera,
i.e., to solve the event-based motion segmentation problem. This paper
describes how to formulate the problem as a weakly-constrained multi-model
fitting one via energy minimization, and how to jointly solve its two
subproblems -- event-cluster assignment (labeling) and motion model fitting --
in an iterative manner, by exploiting the spatio-temporal structure of input
events in the form of a space-time graph. Experiments on available datasets
demonstrate the versatility of the method in scenes with different motion
patterns and number of moving objects. The evaluation shows that the method
performs on par or better than the state of the art without having to
predetermine the number of expected moving objects. | [
"cs.CV"
] |
Deep learning compiler frameworks are gaining ground as a more portable
back-end for deep learning applications on increasingly diverse hardware.
However, they face the daunting challenge of matching performance offered by
hand-tuned target-specific libraries. While auto-tuning frameworks with
statistical cost models can provide dynamic and efficient code optimization,
they suffer from large space exploration and cost model training overheads.
This paper proposes MetaTune, a meta-learning based cost model that more
quickly and accurately predicts the performance of optimized codes with
pre-trained model parameters. MetaTune encodes convolution kernel codes as
structurally similar graphs to facilitate meta-learning, meta-trains a GNN
model with a very small input data set, and then predicts optimization
parameters for unseen convolution operations with varying sizes and structures
during compilation. The resulting framework with MetaTune provides 8 to 13%
better inference time on average for four CNN models with comparable or lower
optimization time while outperforming transfer learning by 10% in
cross-platform cases. | [
"cs.LG",
"cs.AI"
] |
This paper introduces a new loss function induced by the Fourier-based
Metric. This metric is equivalent to the Wasserstein distance but is computed
very efficiently using the Fast Fourier Transform algorithm. We prove that the
Fourier loss function is twice differentiable, and we provide the explicit
formula for both its gradient and its Hessian matrix. More importantly, we show
that minimising the Fourier loss function is equivalent to maximising the
likelihood of the data under a Gaussian noise in the space of frequencies. We
apply our loss function to a multi-class classification task using MNIST,
Fashion-MNIST, and CIFAR10 datasets. The computational results show that, while
its accuracy is competitive with other state-of-the-art loss functions, the
Fourier loss function is significantly more robust to noisy data. | [
"stat.ML",
"cs.LG",
"math.OC",
"68T07"
] |
Compared to the general semantic segmentation problem, portrait segmentation
has higher precision requirement on boundary area. However, this problem has
not been well studied in previous works. In this paper, we propose a
boundary-sensitive deep neural network (BSN) for portrait segmentation. BSN
introduces three novel techniques. First, an individual boundary-sensitive
kernel is proposed by dilating the contour line and assigning the boundary
pixels with multi-class labels. Second, a global boundary-sensitive kernel is
employed as a position sensitive prior to further constrain the overall shape
of the segmentation map. Third, we train a boundary-sensitive attribute
classifier jointly with the segmentation network to reinforce the network with
semantic boundary shape information. We have evaluated BSN on the current
largest public portrait segmentation dataset, i.e, the PFCN dataset, as well as
the portrait images collected from other three popular image segmentation
datasets: COCO, COCO-Stuff, and PASCAL VOC. Our method achieves the superior
quantitative and qualitative performance over state-of-the-arts on all the
datasets, especially on the boundary area. | [
"cs.CV",
"eess.IV"
] |
Deep reinforcement learning (RL) algorithms have recently achieved remarkable
successes in various sequential decision making tasks, leveraging advances in
methods for training large deep networks. However, these methods usually
require large amounts of training data, which is often a big problem for
real-world applications. One natural question to ask is whether learning good
representations for states and using larger networks helps in learning better
policies. In this paper, we try to study if increasing input dimensionality
helps improve performance and sample efficiency of model-free deep RL
algorithms. To do so, we propose an online feature extractor network (OFENet)
that uses neural nets to produce good representations to be used as inputs to
deep RL algorithms. Even though the high dimensionality of input is usually
supposed to make learning of RL agents more difficult, we show that the RL
agents in fact learn more efficiently with the high-dimensional representation
than with the lower-dimensional state observations. We believe that stronger
feature propagation together with larger networks (and thus larger search
space) allows RL agents to learn more complex functions of states and thus
improves the sample efficiency. Through numerical experiments, we show that the
proposed method outperforms several other state-of-the-art algorithms in terms
of both sample efficiency and performance. Codes for the proposed method are
available at http://www.merl.com/research/license/OFENet . | [
"cs.LG",
"cs.RO",
"stat.ML"
] |
We present an information-based uncertainty quantification method for general
Markov Random Fields. Markov Random Fields (MRF) are structured, probabilistic
graphical models over undirected graphs, and provide a fundamental unifying
modeling tool for statistical mechanics, probabilistic machine learning, and
artificial intelligence. Typically MRFs are complex and high-dimensional with
nodes and edges (connections) built in a modular fashion from simpler,
low-dimensional probabilistic models and their local connections; in turn, this
modularity allows to incorporate available data to MRFs and efficiently
simulate them by leveraging their graph-theoretic structure. Learning graphical
models from data and/or constructing them from physical modeling and
constraints necessarily involves uncertainties inherited from data, modeling
choices, or numerical approximations. These uncertainties in the MRF can be
manifested either in the graph structure or the probability distribution
functions, and necessarily will propagate in predictions for quantities of
interest. Here we quantify such uncertainties using tight, information based
bounds on the predictions of quantities of interest; these bounds take
advantage of the graphical structure of MRFs and are capable of handling the
inherent high-dimensionality of such graphical models. We demonstrate our
methods in MRFs for medical diagnostics and statistical mechanics models. In
the latter, we develop uncertainty quantification bounds for finite size
effects and phase diagrams, which constitute two of the typical predictions
goals of statistical mechanics modeling. | [
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT",
"math.PR",
"62H22, 82B20, 94A17"
] |
The in-depth analysis of time series has gained a lot of research interest in
recent years, with the identification of periodic patterns being one important
aspect. Many of the methods for identifying periodic patterns require time
series' season length as input parameter. There exist only a few algorithms for
automatic season length approximation. Many of these rely on simplifications
such as data discretization and user defined parameters. This paper presents an
algorithm for season length detection that is designed to be sufficiently
reliable to be used in practical applications and does not require any input
other than the time series to be analyzed. The algorithm estimates a time
series' season length by interpolating, filtering and detrending the data. This
is followed by analyzing the distances between zeros in the directly
corresponding autocorrelation function. Our algorithm was tested against a
comparable algorithm and outperformed it by passing 122 out of 165 tests, while
the existing algorithm passed 83 tests. The robustness of our method can be
jointly attributed to both the algorithmic approach and also to design
decisions taken at the implementational level. | [
"cs.LG",
"stat.ML"
] |
Saliency prediction for Standard Dynamic Range (SDR) videos has been well
explored in the last decade. However, limited studies are available on High
Dynamic Range (HDR) Visual Attention Models (VAMs). Considering that the
characteristic of HDR content in terms of dynamic range and color gamut is
quite different than those of SDR content, it is essential to identify the
importance of different saliency attributes of HDR videos for designing a VAM
and understand how to combine these features. To this end we propose a
learning-based visual saliency fusion method for HDR content (LVBS-HDR) to
combine various visual saliency features. In our approach various conspicuity
maps are extracted from HDR data, and then for fusing conspicuity maps, a
Random Forests algorithm is used to train a model based on the collected data
from an eye-tracking experiment. Performance evaluations demonstrate the
superiority of the proposed fusion method against other existing fusion
methods. | [
"cs.CV"
] |
3D object classification has attracted appealing attentions in academic
researches and industrial applications. However, most existing methods need to
access the training data of past 3D object classes when facing the common
real-world scenario: new classes of 3D objects arrive in a sequence. Moreover,
the performance of advanced approaches degrades dramatically for past learned
classes (i.e., catastrophic forgetting), due to the irregular and redundant
geometric structures of 3D point cloud data. To address these challenges, we
propose a new Incremental 3D Object Learning (i.e., I3DOL) model, which is the
first exploration to learn new classes of 3D object continually. Specifically,
an adaptive-geometric centroid module is designed to construct discriminative
local geometric structures, which can better characterize the irregular point
cloud representation for 3D object. Afterwards, to prevent the catastrophic
forgetting brought by redundant geometric information, a geometric-aware
attention mechanism is developed to quantify the contributions of local
geometric structures, and explore unique 3D geometric characteristics with high
contributions for classes incremental learning. Meanwhile, a score fairness
compensation strategy is proposed to further alleviate the catastrophic
forgetting caused by unbalanced data between past and new classes of 3D object,
by compensating biased prediction for new classes in the validation phase.
Experiments on 3D representative datasets validate the superiority of our I3DOL
framework. | [
"cs.CV"
] |
We present a framework for training GANs with explicit control over generated
images. We are able to control the generated image by settings exact attributes
such as age, pose, expression, etc. Most approaches for editing GAN-generated
images achieve partial control by leveraging the latent space disentanglement
properties, obtained implicitly after standard GAN training. Such methods are
able to change the relative intensity of certain attributes, but not explicitly
set their values. Recently proposed methods, designed for explicit control over
human faces, harness morphable 3D face models to allow fine-grained control
capabilities in GANs. Unlike these methods, our control is not constrained to
morphable 3D face model parameters and is extendable beyond the domain of human
faces. Using contrastive learning, we obtain GANs with an explicitly
disentangled latent space. This disentanglement is utilized to train
control-encoders mapping human-interpretable inputs to suitable latent vectors,
thus allowing explicit control. In the domain of human faces we demonstrate
control over identity, age, pose, expression, hair color and illumination. We
also demonstrate control capabilities of our framework in the domains of
painted portraits and dog image generation. We demonstrate that our approach
achieves state-of-the-art performance both qualitatively and quantitatively. | [
"cs.CV"
] |
Haze degrades content and obscures information of images, which can
negatively impact vision-based decision-making in real-time systems. In this
paper, we propose an efficient fully convolutional neural network (CNN) image
dehazing method designed to run on edge graphical processing units (GPUs). We
utilize three variants of our architecture to explore the dependency of dehazed
image quality on parameter count and model design. The first two variants
presented, a small and big version, make use of a single efficient
encoder-decoder convolutional feature extractor. The final variant utilizes a
pair of encoder-decoders for atmospheric light and transmission map estimation.
Each variant ends with an image refinement pyramid pooling network to form the
final dehazed image. For the big variant of the single-encoder network, we
demonstrate state-of-the-art performance on the NYU Depth dataset. For the
small variant, we maintain competitive performance on the super-resolution
O/I-HAZE datasets without the need for image cropping. Finally, we examine some
challenges presented by the Dense-Haze dataset when leveraging CNN
architectures for dehazing of dense haze imagery and examine the impact of loss
function selection on image quality. Benchmarks are included to show the
feasibility of introducing this approach into real-time systems. | [
"cs.CV",
"65D19",
"I.4.4"
] |
Joint Energy-based Model (JEM) of Grathwohl et al. shows that a standard
softmax classifier can be reinterpreted as an energy-based model (EBM) for the
joint distribution p(x,y); the resulting model can be optimized to improve
calibration, robustness, and out-of-distribution detection, while generating
samples rivaling the quality of recent GAN-based approaches. However, the
softmax classifier that JEM exploits is inherently discriminative and its
latent feature space is not well formulated as probabilistic distributions,
which may hinder its potential for image generation and incur training
instability. We hypothesize that generative classifiers, such as Linear
Discriminant Analysis (LDA), might be more suitable for image generation since
generative classifiers model the data generation process explicitly. This paper
therefore investigates an LDA classifier for image classification and
generation. In particular, the Max-Mahalanobis Classifier (MMC), a special case
of LDA, fits our goal very well. We show that our Generative MMC (GMMC) can be
trained discriminatively, generatively, or jointly for image classification and
generation. Extensive experiments on multiple datasets show that GMMC achieves
state-of-the-art discriminative and generative performances, while
outperforming JEM in calibration, adversarial robustness, and
out-of-distribution detection by a significant margin. Our source code is
available at https://github.com/sndnyang/GMMC. | [
"cs.CV",
"cs.LG"
] |
Three-dimensional object recognition has recently achieved great progress
thanks to the development of effective point cloud-based learning frameworks,
such as PointNet and its extensions. However, existing methods rely heavily on
fully connected layers, which introduce a significant amount of parameters,
making the network harder to train and prone to overfitting problems. In this
paper, we propose a simple yet effective framework for point set feature
learning by leveraging a nonlinear activation layer encoded by Radial Basis
Function (RBF) kernels. Unlike PointNet variants, that fail to recognize local
point patterns, our approach explicitly models the spatial distribution of
point clouds by aggregating features from sparsely distributed RBF kernels. A
typical RBF kernel, e.g. Gaussian function, naturally penalizes long-distance
response and is only activated by neighboring points. Such localized response
generates highly discriminative features given different point distributions.
In addition, our framework allows the joint optimization of kernel distribution
and its receptive field, automatically evolving kernel configurations in an
end-to-end manner. We demonstrate that the proposed network with a single RBF
layer can outperform the state-of-the-art Pointnet++ in terms of classification
accuracy for 3D object recognition tasks. Moreover, the introduction of
nonlinear mappings significantly reduces the number of network parameters and
computational cost, enabling significantly faster training and a deployable
point cloud recognition solution on portable devices with limited resources. | [
"cs.CV"
] |
Super-resolution using deep neural networks typically relies on highly
curated training sets that are often unavailable in clinical deployment
scenarios. Using loss functions that assume Gaussian-distributed residuals
makes the learning sensitive to corruptions in clinical training sets. We
propose novel loss functions that are robust to corruptions in training sets by
modeling heavy-tailed non-Gaussian distributions on the residuals. We propose a
loss based on an autoencoder-based manifold-distance between the super-resolved
and high-resolution images, to reproduce realistic textural content in
super-resolved images. We propose to learn to super-resolve images to match
human perceptions of structure, luminance, and contrast. Results on a large
clinical dataset shows the advantages of each of our contributions, where our
framework improves over the state of the art. | [
"cs.CV"
] |
Hyperspectral imaging, providing abundant spatial and spectral information
simultaneously, has attracted a lot of interest in recent years. Unfortunately,
due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to
various degradations, such noises (random noise, HSI denoising), blurs
(Gaussian and uniform blur, HSI deblurring), and down-sampled (both spectral
and spatial downsample, HSI super-resolution). Previous HSI restoration methods
are designed for one specific task only. Besides, most of them start from the
1-D vector or 2-D matrix models and cannot fully exploit the structurally
spectral-spatial correlation in 3-D HSI. To overcome these limitations, in this
work, we propose a unified low-rank tensor recovery model for comprehensive HSI
restoration tasks, in which non-local similarity between spectral-spatial cubic
and spectral correlation are simultaneously captured by 3-order tensors.
Further, to improve the capability and flexibility, we formulate it as a
weighted low-rank tensor recovery (WLRTR) model by treating the singular values
differently, and study its analytical solution. We also consider the exclusive
stripe noise in HSI as the gross error by extending WLRTR to robust principal
component analysis (WLRTR-RPCA). Extensive experiments demonstrate the proposed
WLRTR models consistently outperform state-of-the-arts in typical low level
vision HSI tasks, including denoising, destriping, deblurring and
super-resolution. | [
"cs.CV"
] |
We present a deep reinforcement learning-based artificial intelligence agent
that could provide optimized development plans given a basic description of the
reservoir and rock/fluid properties with minimal computational cost. This
artificial intelligence agent, comprising of a convolutional neural network,
provides a mapping from a given state of the reservoir model, constraints, and
economic condition to the optimal decision (drill/do not drill and well
location) to be taken in the next stage of the defined sequential field
development planning process. The state of the reservoir model is defined using
parameters that appear in the governing equations of the two-phase flow. A
feedback loop training process referred to as deep reinforcement learning is
used to train an artificial intelligence agent with such a capability. The
training entails millions of flow simulations with varying reservoir model
descriptions (structural, rock and fluid properties), operational constraints,
and economic conditions. The parameters that define the reservoir model,
operational constraints, and economic conditions are randomly sampled from a
defined range of applicability. Several algorithmic treatments are introduced
to enhance the training of the artificial intelligence agent. After appropriate
training, the artificial intelligence agent provides an optimized field
development plan instantly for new scenarios within the defined range of
applicability. This approach has advantages over traditional optimization
algorithms (e.g., particle swarm optimization, genetic algorithm) that are
generally used to find a solution for a specific field development scenario and
typically not generalizable to different scenarios. | [
"cs.LG",
"math.OC"
] |
We study algebraic neural networks (AlgNNs) with commutative algebras which
unify diverse architectures such as Euclidean convolutional neural networks,
graph neural networks, and group neural networks under the umbrella of
algebraic signal processing. An AlgNN is a stacked layered information
processing structure where each layer is conformed by an algebra, a vector
space and a homomorphism between the algebra and the space of endomorphisms of
the vector space. Signals are modeled as elements of the vector space and are
processed by convolutional filters that are defined as the images of the
elements of the algebra under the action of the homomorphism. We analyze
stability of algebraic filters and AlgNNs to deformations of the homomorphism
and derive conditions on filters that lead to Lipschitz stable operators. We
conclude that stable algebraic filters have frequency responses -- defined as
eigenvalue domain representations -- whose derivative is inversely proportional
to the frequency -- defined as eigenvalue magnitudes. It follows that for a
given level of discriminability, AlgNNs are more stable than algebraic filters,
thereby explaining their better empirical performance. This same phenomenon has
been proven for Euclidean convolutional neural networks and graph neural
networks. Our analysis shows that this is a deep algebraic property shared by a
number of architectures. | [
"cs.LG",
"stat.ML"
] |
Most of the existing deep learning based end-to-end video coding (DLEC)
architectures are designed specifically for RGB color format, yet the video
coding standards, including H.264/AVC, H.265/HEVC and H.266/VVC developed over
past few decades, have been designed primarily for YUV 4:2:0 format, where the
chrominance (U and V) components are subsampled to achieve superior compression
performances considering the human visual system. While a broad number of
papers on DLEC compare these two distinct coding schemes in RGB domain, it is
ideal to have a common evaluation framework in YUV 4:2:0 domain for a more fair
comparison. This paper introduces a new DLEC architecture for video coding to
effectively support YUV 4:2:0 and compares its performance against the HEVC
standard under a common evaluation framework. The experimental results on YUV
4:2:0 video sequences show that the proposed architecture can outperform HEVC
in intra-frame coding, however inter-frame coding is not as efficient on
contrary to the RGB coding results reported in recent papers. | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] |
Spectral images captured by satellites and radio-telescopes are analyzed to
obtain information about geological compositions distributions, distant asters
as well as undersea terrain. Spectral images usually contain tens to hundreds
of continuous narrow spectral bands and are widely used in various fields. But
the vast majority of those image signals are beyond the visible range, which
calls for special visualization technique. The visualizations of spectral
images shall convey as much information as possible from the original signal
and facilitate image interpretation. However, most of the existing visualizatio
methods display spectral images in false colors, which contradict with human's
experience and expectation. In this paper, we present a novel visualization
generative adversarial network (GAN) to display spectral images in natural
colors. To achieve our goal, we propose a loss function which consists of an
adversarial loss and a structure loss. The adversarial loss pushes our solution
to the natural image distribution using a discriminator network that is trained
to differentiate between false-color images and natural-color images. We also
use a cycle loss as the structure constraint to guarantee structure
consistency. Experimental results show that our method is able to generate
structure-preserved and natural-looking visualizations. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
In conveyor belt sushi restaurants, billing is a burdened job because one has
to manually count the number of dishes and identify the color of them to
calculate the price. In a busy situation, there can be a mistake that customers
are overcharged or under-charged. To deal with this problem, we developed a
method that automatically identifies the color of dishes and calculate the
total price using real images. Our method consists of ellipse fitting and
convol-utional neural network. It achieves ellipse detection precision 85% and
recall 96% and classification accuracy 92%. | [
"cs.CV"
] |
Time-lapse fluorescent microscopy (TLFM) combined with predictive
mathematical modelling is a powerful tool to study the inherently dynamic
processes of life on the single-cell level. Such experiments are costly,
complex and labour intensive. A complimentary approach and a step towards in
silico experimentation, is to synthesise the imagery itself. Here, we propose
Multi-StyleGAN as a descriptive approach to simulate time-lapse fluorescence
microscopy imagery of living cells, based on a past experiment. This novel
generative adversarial network synthesises a multi-domain sequence of
consecutive timesteps. We showcase Multi-StyleGAN on imagery of multiple live
yeast cells in microstructured environments and train on a dataset recorded in
our laboratory. The simulation captures underlying biophysical factors and time
dependencies, such as cell morphology, growth, physical interactions, as well
as the intensity of a fluorescent reporter protein. An immediate application is
to generate additional training and validation data for feature extraction
algorithms or to aid and expedite development of advanced experimental
techniques such as online monitoring or control of cells.
Code and dataset is available at
https://git.rwth-aachen.de/bcs/projects/tp/multi-stylegan. | [
"cs.CV",
"cs.LG",
"eess.IV",
"q-bio.QM",
"stat.ML"
] |
It is laborious to manually label point cloud data for training high-quality
3D object detectors. This work proposes a weakly supervised approach for 3D
object detection, only requiring a small set of weakly annotated scenes,
associated with a few precisely labeled object instances. This is achieved by a
two-stage architecture design. Stage-1 learns to generate cylindrical object
proposals under weak supervision, i.e., only the horizontal centers of objects
are click-annotated on bird's view scenes. Stage-2 learns to refine the
cylindrical proposals to get cuboids and confidence scores, using a few
well-labeled object instances. Using only 500 weakly annotated scenes and 534
precisely labeled vehicle instances, our method achieves 85-95% the performance
of current top-leading, fully supervised detectors (which require 3, 712
exhaustively and precisely annotated scenes with 15, 654 instances). More
importantly, with our elaborately designed network architecture, our trained
model can be applied as a 3D object annotator, allowing both automatic and
active working modes. The annotations generated by our model can be used to
train 3D object detectors with over 94% of their original performance (under
manually labeled data). Our experiments also show our model's potential in
boosting performance given more training data. Above designs make our approach
highly practical and introduce new opportunities for learning 3D object
detection with reduced annotation burden. | [
"cs.CV"
] |
We propose a new approach to image segmentation, which exploits the
advantages of both conditional random fields (CRFs) and decision trees. In the
literature, the potential functions of CRFs are mostly defined as a linear
combination of some pre-defined parametric models, and then methods like
structured support vector machines (SSVMs) are applied to learn those linear
coefficients. We instead formulate the unary and pairwise potentials as
nonparametric forests---ensembles of decision trees, and learn the ensemble
parameters and the trees in a unified optimization problem within the
large-margin framework. In this fashion, we easily achieve nonlinear learning
of potential functions on both unary and pairwise terms in CRFs. Moreover, we
learn class-wise decision trees for each object that appears in the image. Due
to the rich structure and flexibility of decision trees, our approach is
powerful in modelling complex data likelihoods and label relationships. The
resulting optimization problem is very challenging because it can have
exponentially many variables and constraints. We show that this challenging
optimization can be efficiently solved by combining a modified column
generation and cutting-planes techniques. Experimental results on both binary
(Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC
2012) segmentation datasets demonstrate the power of the learned nonlinear
nonparametric potentials. | [
"cs.CV"
] |
Methods for unsupervised anomaly detection suffer from the fact that the data
is unlabeled, making it difficult to assess the optimality of detection
algorithms. Ensemble learning has shown exceptional results in classification
and clustering problems, but has not seen as much research in the context of
outlier detection. Existing methods focus on combining output scores of
individual detectors, but this leads to outputs that are not easily
interpretable. In this paper, we introduce a theoretical foundation for
combining individual detectors with Bayesian classifier combination. Not only
are posterior distributions easily interpreted as the probability distribution
of anomalies, but bias, variance, and individual error rates of detectors are
all easily obtained. Performance on real-world datasets shows high accuracy
across varied types of time series data. | [
"stat.ML",
"cs.LG"
] |
The automatic diagnosis of various retinal diseases from fundus images is
important to support clinical decision-making. However, developing such
automatic solutions is challenging due to the requirement of a large amount of
human-annotated data. Recently, unsupervised/self-supervised feature learning
techniques receive a lot of attention, as they do not need massive annotations.
Most of the current self-supervised methods are analyzed with single imaging
modality and there is no method currently utilize multi-modal images for better
results. Considering that the diagnostics of various vitreoretinal diseases can
greatly benefit from another imaging modality, e.g., FFA, this paper presents a
novel self-supervised feature learning method by effectively exploiting
multi-modal data for retinal disease diagnosis. To achieve this, we first
synthesize the corresponding FFA modality and then formulate a patient
feature-based softmax embedding objective. Our objective learns both
modality-invariant features and patient-similarity features. Through this
mechanism, the neural network captures the semantically shared information
across different modalities and the apparent visual similarity between
patients. We evaluate our method on two public benchmark datasets for retinal
disease diagnosis. The experimental results demonstrate that our method clearly
outperforms other self-supervised feature learning methods and is comparable to
the supervised baseline. | [
"cs.CV"
] |
Unmanned aerial vehicles (UAVs) equipped with multiple complementary sensors
have tremendous potential for fast autonomous or remote-controlled semantic
scene analysis, e.g., for disaster examination. In this work, we propose a UAV
system for real-time semantic inference and fusion of multiple sensor
modalities. Semantic segmentation of LiDAR scans and RGB images, as well as
object detection on RGB and thermal images, run online onboard the UAV computer
using lightweight CNN architectures and embedded inference accelerators. We
follow a late fusion approach where semantic information from multiple
modalities augments 3D point clouds and image segmentation masks while also
generating an allocentric semantic map. Our system provides augmented semantic
images and point clouds with $\approx\,$9$\,$Hz. We evaluate the integrated
system in real-world experiments in an urban environment. | [
"cs.CV",
"cs.RO"
] |
Price movement forecasting aims at predicting the future trends of financial
assets based on the current market conditions and other relevant information.
Recently, machine learning(ML) methods have become increasingly popular and
achieved promising results for price movement forecasting in both academia and
industry. Most existing ML solutions formulate the forecasting problem as a
classification(to predict the direction) or a regression(to predict the return)
problem in the entire set of training data. However, due to the extremely low
signal-to-noise ratio and stochastic nature of financial data, good trading
opportunities are extremely scarce. As a result, without careful selection of
potentially profitable samples, such ML methods are prone to capture the
patterns of noises instead of real signals. To address the above issues, we
propose a novel framework-LARA(Locality-Aware Attention and Adaptive Refined
Labeling), which contains the following three components: 1)Locality-aware
attention automatically extracts the potentially profitable samples by
attending to their label information in order to construct a more accurate
classifier on these selected samples. 2)Adaptive refined labeling further
iteratively refines the labels, alleviating the noise of samples. 3)Equipped
with metric learning techniques, Locality-aware attention enjoys task-specific
distance metrics and distributes attention on potentially profitable samples in
a more effective way. To validate our method, we conduct comprehensive
experiments on three real-world financial markets: ETFs, the China's A-share
stock market, and the cryptocurrency market. LARA achieves superior performance
compared with the time-series analysis methods and a set of machine learning
based competitors on the Qlib platform. Extensive ablation studies and
experiments demonstrate that LARA indeed captures more reliable trading
opportunities. | [
"cs.LG",
"cs.AI",
"cs.CE",
"q-fin.ST"
] |
This paper provides a simple solution for reliably solving image
classification tasks tied to spatial locations of salient objects in the scene.
Unlike conventional image classification approaches that are designed to be
invariant to translations of objects in the scene, we focus on tasks where the
output classes vary with respect to where an object of interest is situated
within an image. To handle this variant of the image classification task, we
propose augmenting the standard cross-entropy (classification) loss with a
domain dependent Forced Spatial Attention (FSA) loss, which in essence compels
the network to attend to specific regions in the image associated with the
desired output class. To demonstrate the utility of this loss function, we
consider the task of driver foot activity classification - where each activity
is strongly correlated with where the driver's foot is in the scene. Training
with our proposed loss function results in significantly improved accuracies,
better generalization, and robustness against noise, while obviating the need
for very large datasets. | [
"cs.CV"
] |
We propose a novel approach towards adversarial attacks on neural networks
(NN), focusing on tampering the data used for training instead of generating
attacks on trained models. Our network-agnostic method creates a backdoor
during training which can be exploited at test time to force a neural network
to exhibit abnormal behaviour. We demonstrate on two widely used datasets
(CIFAR-10 and SVHN) that a universal modification of just one pixel per image
for all the images of a class in the training set is enough to corrupt the
training procedure of several state-of-the-art deep neural networks causing the
networks to misclassify any images to which the modification is applied. Our
aim is to bring to the attention of the machine learning community, the
possibility that even learning-based methods that are personally trained on
public datasets can be subject to attacks by a skillful adversary. | [
"cs.LG",
"stat.ML"
] |
Fingerspelling in sign language has been the means of communicating technical
terms and proper nouns when they do not have dedicated sign language gestures.
Automatic recognition of fingerspelling can help resolve communication barriers
when interacting with deaf people. The main challenges prevalent in
fingerspelling recognition are the ambiguity in the gestures and strong
articulation of the hands. The automatic recognition model should address high
inter-class visual similarity and high intra-class variation in the gestures.
Most of the existing research in fingerspelling recognition has focused on the
dataset collected in a controlled environment. The recent collection of a
large-scale annotated fingerspelling dataset in the wild, from social media and
online platforms, captures the challenges in a real-world scenario. In this
work, we propose a fine-grained visual attention mechanism using the
Transformer model for the sequence-to-sequence prediction task in the wild
dataset. The fine-grained attention is achieved by utilizing the change in
motion of the video frames (optical flow) in sequential context-based attention
along with a Transformer encoder model. The unsegmented continuous video
dataset is jointly trained by balancing the Connectionist Temporal
Classification (CTC) loss and the maximum-entropy loss. The proposed approach
can capture better fine-grained attention in a single iteration. Experiment
evaluations show that it outperforms the state-of-the-art approaches. | [
"cs.CV"
] |
Deep Neural Networks (DNNs) have an enormous potential to learn from complex
biomedical data. In particular, DNNs have been used to seamlessly fuse
heterogeneous information from neuroanatomy, genetics, biomarkers, and
neuropsychological tests for highly accurate Alzheimer's disease diagnosis. On
the other hand, their black-box nature is still a barrier for the adoption of
such a system in the clinic, where interpretability is absolutely essential. We
propose Shapley Value Explanation of Heterogeneous Neural Networks (SVEHNN) for
explaining the Alzheimer's diagnosis made by a DNN from the 3D point cloud of
the neuroanatomy and tabular biomarkers. Our explanations are based on the
Shapley value, which is the unique method that satisfies all fundamental axioms
for local explanations previously established in the literature. Thus, SVEHNN
has many desirable characteristics that previous work on interpretability for
medical decision making is lacking. To avoid the exponential time complexity of
the Shapley value, we propose to transform a given DNN into a Lightweight
Probabilistic Deep Network without re-training, thus achieving a complexity
only quadratic in the number of features. In our experiments on synthetic and
real data, we show that we can closely approximate the exact Shapley value with
a dramatically reduced runtime and can reveal the hidden knowledge the network
has learned from the data. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Model-based meta-reinforcement learning (RL) methods have recently shown to
be a promising approach to improving the sample efficiency of RL in multi-task
settings. However, the theoretical understanding of those methods is yet to be
established, and there is currently no theoretical guarantee of their
performance in a real-world environment. In this paper, we analyze the
performance guarantee of model-based meta-RL methods by extending the theorems
proposed by Janner et al. (2019). On the basis of our theoretical results, we
propose Meta-Model-Based Meta-Policy Optimization (M3PO), a model-based meta-RL
method with a performance guarantee. We demonstrate that M3PO outperforms
existing meta-RL methods in continuous-control benchmarks. | [
"cs.LG",
"stat.ML"
] |
Graph embedding algorithms are used to efficiently represent (encode) a graph
in a low-dimensional continuous vector space that preserves the most important
properties of the graph. One aspect that is often overlooked is whether the
graph is directed or not. Most studies ignore the directionality, so as to
learn high-quality representations optimized for node classification. On the
other hand, studies that capture directionality are usually effective on link
prediction but do not perform well on other tasks. This preliminary study
presents a novel text-enriched, direction-aware algorithm called DIAGRAM ,
based on a carefully designed multi-objective model to learn embeddings that
preserve the direction of edges, textual features and graph context of nodes.
As a result, our algorithm does not have to trade one property for another and
jointly learns high-quality representations for multiple network analysis
tasks. We empirically show that DIAGRAM significantly outperforms six
state-of-the-art baselines, both direction-aware and oblivious ones,on link
prediction and network reconstruction experiments using two popular datasets.
It also achieves a comparable performance on node classification experiments
against these baselines using the same datasets. | [
"cs.LG",
"cs.SI",
"stat.ML"
] |
It has been experimentally observed that the efficiency of distributed
training with stochastic gradient (SGD) depends decisively on the batch size
and -- in asynchronous implementations -- on the gradient staleness.
Especially, it has been observed that the speedup saturates beyond a certain
batch size and/or when the delays grow too large. We identify a data-dependent
parameter that explains the speedup saturation in both these settings. Our
comprehensive theoretical analysis, for strongly convex, convex and non-convex
settings, unifies and generalized prior work directions that often focused on
only one of these two aspects. In particular, our approach allows us to derive
improved speedup results under frequently considered sparsity assumptions. Our
insights give rise to theoretically based guidelines on how the learning rates
can be adjusted in practice. We show that our results are tight and illustrate
key findings in numerical experiments. | [
"cs.LG",
"cs.DC",
"stat.ML"
] |
To further improve the performance and the self-learning ability of GCNs, in
this paper, we propose an efficient self-supervised learning strategy of GCNs,
named randomly removed links with a fixed step at one region (RRLFSOR). In
addition, we also propose another self-supervised learning strategy of GCNs,
named randomly removing links with a fixed step at some blocks (RRLFSSB), to
solve the problem that adjacent nodes have no selected step. Experiments on
transductive link prediction tasks show that our strategies outperform the
baseline models consistently by up to 21.34% in terms of accuracy on three
benchmark datasets. | [
"cs.LG"
] |
RGBT tracking has attracted increasing attention since RGB and thermal
infrared data have strong complementary advantages, which could make trackers
all-day and all-weather work. However, how to effectively represent RGBT data
for visual tracking remains unstudied well. Existing works usually focus on
extracting modality-shared or modality-specific information, but the potentials
of these two cues are not well explored and exploited in RGBT tracking. In this
paper, we propose a novel multi-adapter network to jointly perform
modality-shared, modality-specific and instance-aware target representation
learning for RGBT tracking. To this end, we design three kinds of adapters
within an end-to-end deep learning framework. In specific, we use the modified
VGG-M as the generality adapter to extract the modality-shared target
representations.To extract the modality-specific features while reducing the
computational complexity, we design a modality adapter, which adds a small
block to the generality adapter in each layer and each modality in a parallel
manner. Such a design could learn multilevel modality-specific representations
with a modest number of parameters as the vast majority of parameters are
shared with the generality adapter. We also design instance adapter to capture
the appearance properties and temporal variations of a certain target.
Moreover, to enhance the shared and specific features, we employ the loss of
multiple kernel maximum mean discrepancy to measure the distribution divergence
of different modal features and integrate it into each layer for more robust
representation learning. Extensive experiments on two RGBT tracking benchmark
datasets demonstrate the outstanding performance of the proposed tracker
against the state-of-the-art methods. | [
"cs.CV"
] |
Deep convolutional networks have achieved the state-of-the-art for semantic
image segmentation tasks. However, training these networks requires access to
densely labeled images, which are known to be very expensive to obtain. On the
other hand, the web provides an almost unlimited source of images annotated at
the image level. How can one utilize this much larger weakly annotated set for
tasks that require dense labeling? Prior work often relied on localization
cues, such as saliency maps, objectness priors, bounding boxes etc., to address
this challenging problem. In this paper, we propose a model that generates
auxiliary labels for each image, while simultaneously forcing the output of the
CNN to satisfy the mean-field constraints imposed by a conditional random
field. We show that one can enforce the CRF constraints by forcing the
distribution at each pixel to be close to the distribution of its neighbors.
This is in stark contrast with methods that compute a recursive expansion of
the mean-field distribution using a recurrent architecture and train the
resultant distribution. Instead, the proposed model adds an extra loss term to
the output of the CNN, and hence, is faster than recursive implementations. We
achieve the state-of-the-art for weakly supervised semantic image segmentation
on VOC 2012 dataset, assuming no manually labeled pixel level information is
available. Furthermore, the incorporation of conditional random fields in CNN
incurs little extra time during training. | [
"cs.CV"
] |
Humans are able to describe image contents with coarse to fine details as
they wish. However, most image captioning models are intention-agnostic which
can not generate diverse descriptions according to different user intentions
initiatively. In this work, we propose the Abstract Scene Graph (ASG) structure
to represent user intention in fine-grained level and control what and how
detailed the generated description should be. The ASG is a directed graph
consisting of three types of \textbf{abstract nodes} (object, attribute,
relationship) grounded in the image without any concrete semantic labels. Thus
it is easy to obtain either manually or automatically. From the ASG, we propose
a novel ASG2Caption model, which is able to recognise user intentions and
semantics in the graph, and therefore generate desired captions according to
the graph structure. Our model achieves better controllability conditioning on
ASGs than carefully designed baselines on both VisualGenome and MSCOCO
datasets. It also significantly improves the caption diversity via
automatically sampling diverse ASGs as control signals. | [
"cs.CV",
"cs.AI"
] |
We present a locality preserving loss (LPL) that improves the alignment
between vector space embeddings while separating uncorrelated representations.
Given two pretrained embedding manifolds, LPL optimizes a model to project an
embedding and maintain its local neighborhood while aligning one manifold to
another. This reduces the overall size of the dataset required to align the two
in tasks such as cross-lingual word alignment. We show that the LPL-based
alignment between input vector spaces acts as a regularizer, leading to better
and consistent accuracy than the baseline, especially when the size of the
training set is small. We demonstrate the effectiveness of LPL optimized
alignment on semantic text similarity (STS), natural language inference (SNLI),
multi-genre language inference (MNLI) and cross-lingual word alignment(CLA)
showing consistent improvements, finding up to 16% improvement over our
baseline in lower resource settings. | [
"cs.LG",
"cs.CL",
"stat.ML",
"I.2.7"
] |
Graph convolutional neural networks (GCNNs) have received much attention
recently, owing to their capability in handling graph-structured data. Among
the existing GCNNs, many methods can be viewed as instances of a neural message
passing motif; features of nodes are passed around their neighbors, aggregated
and transformed to produce better nodes' representations. Nevertheless, these
methods seldom use node transition probabilities, a measure that has been found
useful in exploring graphs. Furthermore, when the transition probabilities are
used, their transition direction is often improperly considered in the feature
aggregation step, resulting in an inefficient weighting scheme. In addition,
although a great number of GCNN models with increasing level of complexity have
been introduced, the GCNNs often suffer from over-fitting when being trained on
small graphs. Another issue of the GCNNs is over-smoothing, which tends to make
nodes' representations indistinguishable. This work presents a new method to
improve the message passing process based on node transition probabilities by
properly considering the transition direction, leading to a better weighting
scheme in nodes' features aggregation compared to the existing counterpart.
Moreover, we propose a novel regularization method termed DropNode to address
the over-fitting and over-smoothing issues simultaneously. DropNode randomly
discards part of a graph, thus it creates multiple deformed versions of the
graph, leading to data augmentation regularization effect. Additionally,
DropNode lessens the connectivity of the graph, mitigating the effect of
over-smoothing in deep GCNNs. Extensive experiments on eight benchmark datasets
for node and graph classification tasks demonstrate the effectiveness of the
proposed methods in comparison with the state of the art. | [
"cs.LG",
"stat.ML"
] |
Training 3D object detectors for autonomous driving has been limited to small
datasets due to the effort required to generate annotations. Reducing both task
complexity and the amount of task switching done by annotators is key to
reducing the effort and time required to generate 3D bounding box annotations.
This paper introduces a novel ground truth generation method that combines
human supervision with pretrained neural networks to generate per-instance 3D
point cloud segmentation, 3D bounding boxes, and class annotations. The
annotators provide object anchor clicks which behave as a seed to generate
instance segmentation results in 3D. The points belonging to each instance are
then used to regress object centroids, bounding box dimensions, and object
orientation. Our proposed annotation scheme requires 30x lower human annotation
time. We use the KITTI 3D object detection dataset to evaluate the efficiency
and the quality of our annotation scheme. We also test the the proposed scheme
on previously unseen data from the Autonomoose self-driving vehicle to
demonstrate generalization capabilities of the network. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Recurrent Mixture Density Networks (RMDNs) are consisted of two main parts: a
Recurrent Neural Network (RNN) and a Gaussian Mixture Model (GMM), in which a
kind of RNN (almost LSTM) is used to find the parameters of a GMM in every time
step. While available RMDNs have been faced with different difficulties. The
most important of them is high$-$dimensional problems. Since estimating the
covariance matrix for the high$-$dimensional problems is more difficult, due to
existing correlation between dimensions and satisfying the positive definition
condition. Consequently, the available methods have usually used RMDN with a
diagonal covariance matrix for high$-$dimensional problems by supposing
independence among dimensions. Hence, in this paper with inspiring a common
approach in the literature of GMM, we consider a tied configuration for each
precision matrix (inverse of the covariance matrix) in RMDN as $(\(\Sigma _k^{
- 1} = U{D_k}U\))$ to enrich GMM rather than considering a diagonal form for
it. But due to simplicity, we assume $\(U\)$ be an Identity matrix and
$\(D_k\)$ is a specific diagonal matrix for $\(k^{th}\)$ component. Until now,
we only have a diagonal matrix and it does not differ with available diagonal
RMDNs. Besides, Flow$-$based neural networks are a new group of generative
models that are able to transform a distribution to a simpler distribution and
vice versa, through a sequence of invertible functions. Therefore, we applied a
diagonal GMM on transformed observations. At every time step, the next
observation, $\({y_{t + 1}}\)$, has been passed through a flow$-$based neural
network to obtain a much simpler distribution. Experimental results for a
reinforcement learning problem verify the superiority of the proposed method to
the base$-$line method in terms of Negative Log$-$Likelihood (NLL) for RMDN and
the cumulative reward for a controller with fewer population size. | [
"cs.LG",
"stat.ML"
] |
Multi-modal medical image segmentation plays an essential role in clinical
diagnosis. It remains challenging as the input modalities are often not
well-aligned spatially. Existing learning-based methods mainly consider sharing
trainable layers across modalities and minimizing visual feature discrepancies.
While the problem is often formulated as joint supervised feature learning,
multiple-scale features and class-specific representation have not yet been
explored. In this paper, we propose an affinity-guided fully convolutional
network for multimodal image segmentation. To learn effective representations,
we design class-specific affinity matrices to encode the knowledge of
hierarchical feature reasoning, together with the shared convolutional layers
to ensure the cross-modality generalization. Our affinity matrix does not
depend on spatial alignments of the visual features and thus allows us to train
with unpaired, multimodal inputs. We extensively evaluated our method on two
public multimodal benchmark datasets and outperform state-of-the-art methods. | [
"cs.CV",
"cs.AI"
] |
Most graph neural network models learn embeddings of nodes in static
attributed graphs for predictive analysis. Recent attempts have been made to
learn temporal proximity of the nodes. We find that real dynamic attributed
graphs exhibit complex co-evolution of node attributes and graph structure.
Learning node embeddings for forecasting change of node attributes and birth
and death of links over time remains an open problem. In this work, we present
a novel framework called CoEvoGNN for modeling dynamic attributed graph
sequence. It preserves the impact of earlier graphs on the current graph by
embedding generation through the sequence. It has a temporal self-attention
mechanism to model long-range dependencies in the evolution. Moreover, CoEvoGNN
optimizes model parameters jointly on two dynamic tasks, attribute inference
and link prediction over time. So the model can capture the co-evolutionary
patterns of attribute change and link formation. This framework can adapt to
any graph neural algorithms so we implemented and investigated three methods
based on it: CoEvoGCN, CoEvoGAT, and CoEvoSAGE. Experiments demonstrate the
framework (and its methods) outperform strong baselines on predicting an entire
unseen graph snapshot of personal attributes and interpersonal links in dynamic
social graphs and financial graphs. | [
"cs.LG",
"stat.ML"
] |
Unsupervised learning with generative adversarial networks (GANs) has proven
hugely successful. Regular GANs hypothesize the discriminator as a classifier
with the sigmoid cross entropy loss function. However, we found that this loss
function may lead to the vanishing gradients problem during the learning
process. To overcome such a problem, we propose in this paper the Least Squares
Generative Adversarial Networks (LSGANs) which adopt the least squares loss
function for the discriminator. We show that minimizing the objective function
of LSGAN yields minimizing the Pearson $\chi^2$ divergence. There are two
benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher
quality images than regular GANs. Second, LSGANs perform more stable during the
learning process. We evaluate LSGANs on five scene datasets and the
experimental results show that the images generated by LSGANs are of better
quality than the ones generated by regular GANs. We also conduct two comparison
experiments between LSGANs and regular GANs to illustrate the stability of
LSGANs. | [
"cs.CV"
] |
This paper presents Roof-GAN, a novel generative adversarial network that
generates structured geometry of residential roof structures as a set of roof
primitives and their relationships. Given the number of primitives, the
generator produces a structured roof model as a graph, which consists of 1)
primitive geometry as raster images at each node, encoding facet segmentation
and angles; 2) inter-primitive colinear/coplanar relationships at each edge;
and 3) primitive geometry in a vector format at each node, generated by a novel
differentiable vectorizer while enforcing the relationships. The discriminator
is trained to assess the primitive raster geometry, the primitive
relationships, and the primitive vector geometry in a fully end-to-end
architecture. Qualitative and quantitative evaluations demonstrate the
effectiveness of our approach in generating diverse and realistic roof models
over the competing methods with a novel metric proposed in this paper for the
task of structured geometry generation. Code and data are available at
https://github.com/yi-ming-qian/roofgan . | [
"cs.CV"
] |
We propose a novel end-to-end solution for video instance segmentation (VIS)
based on transformers. Recently, the per-clip pipeline shows superior
performance over per-frame methods leveraging richer information from multiple
frames. However, previous per-clip models require heavy computation and memory
usage to achieve frame-to-frame communications, limiting practicality. In this
work, we propose Inter-frame Communication Transformers (IFC), which
significantly reduces the overhead for information-passing between frames by
efficiently encoding the context within the input clip. Specifically, we
propose to utilize concise memory tokens as a mean of conveying information as
well as summarizing each frame scene. The features of each frame are enriched
and correlated with other frames through exchange of information between the
precisely encoded memory tokens. We validate our method on the latest benchmark
sets and achieved the state-of-the-art performance (AP 44.6 on YouTube-VIS 2019
val set using the offline inference) while having a considerably fast runtime
(89.4 FPS). Our method can also be applied to near-online inference for
processing a video in real-time with only a small delay. The code will be made
available. | [
"cs.CV"
] |
In this manuscript we consider the problem of jointly estimating multiple
graphical models in high dimensions. We assume that the data are collected from
n subjects, each of which consists of T possibly dependent observations. The
graphical models of subjects vary, but are assumed to change smoothly
corresponding to a measure of closeness between subjects. We propose a kernel
based method for jointly estimating all graphical models. Theoretically, under
a double asymptotic framework, where both (T,n) and the dimension d can
increase, we provide the explicit rate of convergence in parameter estimation.
It characterizes the strength one can borrow across different individuals and
impact of data dependence on parameter estimation. Empirically, experiments on
both synthetic and real resting state functional magnetic resonance imaging
(rs-fMRI) data illustrate the effectiveness of the proposed method. | [
"stat.ML"
] |
Modeling instance-level context and object-object relationships is extremely
challenging. It requires reasoning about bounding boxes of different classes,
locations \etc. Above all, instance-level spatial reasoning inherently requires
modeling conditional distributions on previous detections. Unfortunately, our
current object detection systems do not have any {\bf memory} to remember what
to condition on! The state-of-the-art object detectors still detect all object
in parallel followed by non-maximal suppression (NMS). While memory has been
used for tasks such as captioning, they mostly use image-level memory cells
without capturing the spatial layout. On the other hand, modeling object-object
relationships requires {\bf spatial} reasoning -- not only do we need a memory
to store the spatial layout, but also a effective reasoning module to extract
spatial patterns. This paper presents a conceptually simple yet powerful
solution -- Spatial Memory Network (SMN), to model the instance-level context
efficiently and effectively. Our spatial memory essentially assembles object
instances back into a pseudo "image" representation that is easy to be fed into
another ConvNet for object-object context reasoning. This leads to a new
sequential reasoning architecture where image and memory are processed in
parallel to obtain detections which update the memory again. We show our SMN
direction is promising as it provides 2.2\% improvement over baseline Faster
RCNN on the COCO dataset so far. | [
"cs.CV"
] |
State-of-the-art neural network architectures continue to scale in size and
deliver impressive generalization results, although this comes at the expense
of limited interpretability. In particular, a key challenge is to determine
when to stop training the model, as this has a significant impact on
generalization. Convolutional neural networks (ConvNets) comprise
high-dimensional feature spaces formed by the aggregation of multiple channels,
where analyzing intermediate data representations and the model's evolution can
be challenging owing to the curse of dimensionality. We present channel-wise
DeepNNK (CW-DeepNNK), a novel channel-wise generalization estimate based on
non-negative kernel regression (NNK) graphs with which we perform local
polytope interpolation on low-dimensional channels. This method leads to
instance-based interpretability of both the learned data representations and
the relationship between channels. Motivated by our observations, we use
CW-DeepNNK to propose a novel early stopping criterion that (i) does not
require a validation set, (ii) is based on a task performance metric, and (iii)
allows stopping to be reached at different points for each channel. Our
experiments demonstrate that our proposed method has advantages as compared to
the standard criterion based on validation set performance. | [
"cs.LG",
"stat.ML"
] |
Complex blur such as the mixup of space-variant and space-invariant blur,
which is hard to model mathematically, widely exists in real images. In this
paper, we propose a novel image deblurring method that does not need to
estimate blur kernels. We utilize a pair of images that can be easily acquired
in low-light situations: (1) a blurred image taken with low shutter speed and
low ISO noise; and (2) a noisy image captured with high shutter speed and high
ISO noise. Slicing the blurred image into patches, we extend the Gaussian
mixture model (GMM) to model the underlying intensity distribution of each
patch using the corresponding patches in the noisy image. We compute patch
correspondences by analyzing the optical flow between the two images. The
Expectation Maximization (EM) algorithm is utilized to estimate the parameters
of GMM. To preserve sharp features, we add an additional bilateral term to the
objective function in the M-step. We eventually add a detail layer to the
deblurred image for refinement. Extensive experiments on both synthetic and
real-world data demonstrate that our method outperforms state-of-the-art
techniques, in terms of robustness, visual quality, and quantitative metrics. | [
"cs.CV"
] |
Approximate inference in deep Bayesian networks exhibits a dilemma of how to
yield high fidelity posterior approximations while maintaining computational
efficiency and scalability. We tackle this challenge by introducing a novel
variational structured approximation inspired by the Bayesian interpretation of
Dropout regularization. Concretely, we focus on the inflexibility of the
factorized structure in Dropout posterior and then propose an improved method
called Variational Structured Dropout (VSD). VSD employs an orthogonal
transformation to learn a structured representation on the variational noise
and consequently induces statistical dependencies in the approximate posterior.
Theoretically, VSD successfully addresses the pathologies of previous
Variational Dropout methods and thus offers a standard Bayesian justification.
We further show that VSD induces an adaptive regularization term with several
desirable properties which contribute to better generalization. Finally, we
conduct extensive experiments on standard benchmarks to demonstrate the
effectiveness of VSD over state-of-the-art variational methods on predictive
accuracy, uncertainty estimation, and out-of-distribution detection. | [
"cs.LG",
"stat.ML"
] |
Time-series forecasting has been an important research domain for so many
years. Its applications include ECG predictions, sales forecasting, weather
conditions, even COVID-19 spread predictions. These applications have motivated
many researchers to figure out an optimal forecasting approach, but the
modeling approach also changes as the application domain changes. This work has
focused on reviewing different forecasting approaches for telemetry data
predictions collected at data centers. Forecasting of telemetry data is a
critical feature of network and data center management products. However, there
are multiple options of forecasting approaches that range from a simple linear
statistical model to high capacity deep learning architectures. In this paper,
we attempted to summarize and evaluate the performance of well known time
series forecasting techniques. We hope that this evaluation provides a
comprehensive summary to innovate in forecasting approaches for telemetry data. | [
"cs.LG",
"cs.AI",
"cs.NI"
] |
Deep convolutional neural networks are powerful tools for learning visual
representations from images. However, designing efficient deep architectures to
analyse volumetric medical images remains challenging. This work investigates
efficient and flexible elements of modern convolutional networks such as
dilated convolution and residual connection. With these essential building
blocks, we propose a high-resolution, compact convolutional network for
volumetric image segmentation. To illustrate its efficiency of learning 3D
representation from large-scale image data, the proposed network is validated
with the challenging task of parcellating 155 neuroanatomical structures from
brain MR images. Our experiments show that the proposed network architecture
compares favourably with state-of-the-art volumetric segmentation networks
while being an order of magnitude more compact. We consider the brain
parcellation task as a pretext task for volumetric image segmentation; our
trained network potentially provides a good starting point for transfer
learning. Additionally, we show the feasibility of voxel-level uncertainty
estimation using a sampling approximation through dropout. | [
"cs.CV"
] |
Visual object tracking was generally tackled by reasoning independently on
fast processing algorithms, accurate online adaptation methods, and fusion of
trackers. In this paper, we unify such goals by proposing a novel tracking
methodology that takes advantage of other visual trackers, offline and online.
A compact student model is trained via the marriage of knowledge distillation
and reinforcement learning. The first allows to transfer and compress tracking
knowledge of other trackers. The second enables the learning of evaluation
measures which are then exploited online. After learning, the student can be
ultimately used to build (i) a very fast single-shot tracker, (ii) a tracker
with a simple and effective online adaptation mechanism, (iii) a tracker that
performs fusion of other trackers. Extensive validation shows that the proposed
algorithms compete with real-time state-of-the-art trackers. | [
"cs.CV"
] |
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