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We propose a new attention model for video question answering. The main idea
of the attention models is to locate on the most informative parts of the
visual data. The attention mechanisms are quite popular these days. However,
most existing visual attention mechanisms regard the question as a whole. They
ignore the word-level semantics where each word can have different attentions
and some words need no attention. Neither do they consider the semantic
structure of the sentences. Although the Extended Soft Attention (E-SA) model
for video question answering leverages the word-level attention, it performs
poorly on long question sentences. In this paper, we propose the heterogeneous
tree-structured memory network (HTreeMN) for video question answering. Our
proposed approach is based upon the syntax parse trees of the question
sentences. The HTreeMN treats the words differently where the \textit{visual}
words are processed with an attention module and the \textit{verbal} ones not.
It also utilizes the semantic structure of the sentences by combining the
neighbors based on the recursive structure of the parse trees. The
understandings of the words and the videos are propagated and merged from
leaves to the root. Furthermore, we build a hierarchical attention mechanism to
distill the attended features. We evaluate our approach on two datasets. The
experimental results show the superiority of our HTreeMN model over the other
attention models especially on complex questions. Our code is available on
github.
Our code is available at https://github.com/ZJULearning/TreeAttention | [
"cs.CV",
"cs.CL",
"cs.LG"
] |
In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000
fps with the extreme motion to the research community for video frame
interpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that
first handles the VFI for 4K videos with large motion. The XVFI-Net is based on
a recursive multi-scale shared structure that consists of two cascaded modules
for bidirectional optical flow learning between two input frames (BiOF-I) and
for bidirectional optical flow learning from target to input frames (BiOF-T).
The optical flows are stably approximated by a complementary flow reversal
(CFR) proposed in BiOF-T module. During inference, the BiOF-I module can start
at any scale of input while the BiOF-T module only operates at the original
input scale so that the inference can be accelerated while maintaining highly
accurate VFI performance. Extensive experimental results show that our XVFI-Net
can successfully capture the essential information of objects with extremely
large motions and complex textures while the state-of-the-art methods exhibit
poor performance. Furthermore, our XVFI-Net framework also performs comparably
on the previous lower resolution benchmark dataset, which shows a robustness of
our algorithm as well. All source codes, pre-trained models, and proposed
X4K1000FPS datasets are publicly available at
https://github.com/JihyongOh/XVFI. | [
"cs.CV"
] |
We examine gradient descent on unregularized logistic regression problems,
with homogeneous linear predictors on linearly separable datasets. We show the
predictor converges to the direction of the max-margin (hard margin SVM)
solution. The result also generalizes to other monotone decreasing loss
functions with an infimum at infinity, to multi-class problems, and to training
a weight layer in a deep network in a certain restricted setting. Furthermore,
we show this convergence is very slow, and only logarithmic in the convergence
of the loss itself. This can help explain the benefit of continuing to optimize
the logistic or cross-entropy loss even after the training error is zero and
the training loss is extremely small, and, as we show, even if the validation
loss increases. Our methodology can also aid in understanding implicit
regularization n more complex models and with other optimization methods. | [
"stat.ML",
"cs.LG"
] |
Registering accurately point clouds from a cheap low-resolution sensor is a
challenging task. Existing rigid registration methods failed to use the
physical 3D uncertainty distribution of each point from a real sensor in the
dynamic alignment process mainly because the uncertainty model for a point is
static and invariant and it is hard to describe the change of these physical
uncertainty models in the registration process. Additionally, the existing
Gaussian mixture alignment architecture cannot be efficiently implement these
dynamic changes.
This paper proposes a simple architecture combining error estimation from
sample covariances and dual dynamic global probability alignment using the
convolution of uncertainty-based Gaussian Mixture Models (GMM) from point
clouds. Firstly, we propose an efficient way to describe the change of each 3D
uncertainty model, which represents the structure of the point cloud much
better. Unlike the invariant GMM (representing a fixed point cloud) in
traditional Gaussian mixture alignment, we use two uncertainty-based GMMs that
change and interact with each other in each iteration. In order to have a wider
basin of convergence than other local algorithms, we design a more robust
energy function by convolving efficiently the two GMMs over the whole 3D space.
Tens of thousands of trials have been conducted on hundreds of models from
multiple datasets to demonstrate the proposed method's superior performance
compared with the current state-of-the-art methods. The new dataset and code is
available from https://github.com/Canpu999 | [
"cs.CV"
] |
Disparity by Block Matching stereo is usually used in applications with
limited computational power in order to get depth estimates. However, the
research on simple stereo methods has been lesser than the energy based
counterparts which promise a better quality depth map with more potential for
future improvements. Semi-global-matching (SGM) methods offer good performance
and easy implementation but suffer from the problem of very high memory
footprint because it's working on the full disparity space image. On the other
hand, Block matching stereo needs much less memory. In this paper, we introduce
a novel multi-scale-hierarchical block-matching approach using a pyramidal
variant of depth and cost functions which drastically improves the results of
standard block matching stereo techniques while preserving the low memory
footprint and further reducing the complexity of standard block matching. We
tested our new multi block matching scheme on the Middlebury stereo benchmark.
For the Middlebury benchmark we get results that are only slightly worse than
state of the art SGM implementations. | [
"cs.CV"
] |
Model-free approaches for reinforcement learning (RL) and continuous control
find policies based only on past states and rewards, without fitting a model of
the system dynamics. They are appealing as they are general purpose and easy to
implement; however, they also come with fewer theoretical guarantees than
model-based RL. In this work, we present a new model-free algorithm for
controlling linear quadratic (LQ) systems, and show that its regret scales as
$O(T^{\xi+2/3})$ for any small $\xi>0$ if time horizon satisfies $T>C^{1/\xi}$
for a constant $C$. The algorithm is based on a reduction of control of Markov
decision processes to an expert prediction problem. In practice, it corresponds
to a variant of policy iteration with forced exploration, where the policy in
each phase is greedy with respect to the average of all previous value
functions. This is the first model-free algorithm for adaptive control of LQ
systems that provably achieves sublinear regret and has a polynomial
computation cost. Empirically, our algorithm dramatically outperforms standard
policy iteration, but performs worse than a model-based approach. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
We analyze the DQN reinforcement learning algorithm as a stochastic
approximation scheme using the o.d.e. (for 'ordinary differential equation')
approach and point out certain theoretical issues. We then propose a modified
scheme called Full Gradient DQN (FG-DQN, for short) that has a sound
theoretical basis and compare it with the original scheme on sample problems.
We observe a better performance for FG-DQN. | [
"cs.LG",
"math.OC",
"math.PR"
] |
Predicting drug-target interactions (DTI) is an essential part of the drug
discovery process, which is an expensive process in terms of time and cost.
Therefore, reducing DTI cost could lead to reduced healthcare costs for a
patient. In addition, a precisely learned molecule representation in a DTI
model could contribute to developing personalized medicine, which will help
many patient cohorts. In this paper, we propose a new molecule representation
based on the self-attention mechanism, and a new DTI model using our molecule
representation. The experiments show that our DTI model outperforms the state
of the art by up to 4.9% points in terms of area under the precision-recall
curve. Moreover, a study using the DrugBank database proves that our model
effectively lists all known drugs targeting a specific cancer biomarker in the
top-30 candidate list. | [
"cs.LG",
"stat.ML"
] |
Convolutional networks optimized for accuracy on challenging, dense
prediction tasks are prohibitively slow to run on each frame in a video. The
spatial similarity of nearby video frames, however, suggests opportunity to
reuse computation. Existing work has explored basic feature reuse and feature
warping based on optical flow, but has encountered limits to the speedup
attainable with these techniques. In this paper, we present a new, two part
approach to accelerating inference on video. First, we propose a fast feature
propagation technique that utilizes the block motion vectors present in
compressed video (e.g. H.264 codecs) to cheaply propagate features from frame
to frame. Second, we develop a novel feature estimation scheme, termed feature
interpolation, that fuses features propagated from enclosing keyframes to
render accurate feature estimates, even at sparse keyframe frequencies. We
evaluate our system on the Cityscapes and CamVid datasets, comparing to both a
frame-by-frame baseline and related work. We find that we are able to
substantially accelerate segmentation on video, achieving near real-time frame
rates (20.1 frames per second) on large images (960 x 720 pixels), while
maintaining competitive accuracy. This represents an improvement of almost 6x
over the single-frame baseline and 2.5x over the fastest prior work. | [
"cs.CV"
] |
Polarization measurements done using Imaging Polarimeters such as the Robotic
Polarimeter are very sensitive to the presence of artefacts in images.
Artefacts can range from internal reflections in a telescope to satellite
trails that could contaminate an area of interest in the image. With the advent
of wide-field polarimetry surveys, it is imperative to develop methods that
automatically flag artefacts in images. In this paper, we implement a
Convolutional Neural Network to identify the most dominant artefacts in the
images. We find that our model can successfully classify sources with 98\% true
positive and 97\% true negative rates. Such models, combined with transfer
learning, will give us a running start in artefact elimination for near-future
surveys like WALOP. | [
"cs.LG",
"astro-ph.IM",
"stat.ML"
] |
We present 3D-MPA, a method for instance segmentation on 3D point clouds.
Given an input point cloud, we propose an object-centric approach where each
point votes for its object center. We sample object proposals from the
predicted object centers. Then, we learn proposal features from grouped point
features that voted for the same object center. A graph convolutional network
introduces inter-proposal relations, providing higher-level feature learning in
addition to the lower-level point features. Each proposal comprises a semantic
label, a set of associated points over which we define a foreground-background
mask, an objectness score and aggregation features. Previous works usually
perform non-maximum-suppression (NMS) over proposals to obtain the final object
detections or semantic instances. However, NMS can discard potentially correct
predictions. Instead, our approach keeps all proposals and groups them together
based on the learned aggregation features. We show that grouping proposals
improves over NMS and outperforms previous state-of-the-art methods on the
tasks of 3D object detection and semantic instance segmentation on the
ScanNetV2 benchmark and the S3DIS dataset. | [
"cs.CV"
] |
Viewpoint estimation for known categories of objects has been improved
significantly thanks to deep networks and large datasets, but generalization to
unknown categories is still very challenging. With an aim towards improving
performance on unknown categories, we introduce the problem of category-level
few-shot viewpoint estimation. We design a novel framework to successfully
train viewpoint networks for new categories with few examples (10 or less). We
formulate the problem as one of learning to estimate category-specific 3D
canonical shapes, their associated depth estimates, and semantic 2D keypoints.
We apply meta-learning to learn weights for our network that are amenable to
category-specific few-shot fine-tuning. Furthermore, we design a flexible
meta-Siamese network that maximizes information sharing during meta-learning.
Through extensive experimentation on the ObjectNet3D and Pascal3D+ benchmark
datasets, we demonstrate that our framework, which we call MetaView,
significantly outperforms fine-tuning the state-of-the-art models with few
examples, and that the specific architectural innovations of our method are
crucial to achieving good performance. | [
"cs.CV"
] |
This paper demonstrates that Non-Maximum Suppression (NMS), which is commonly
used in Object Detection (OD) tasks to filter redundant detection results, is
no longer secure. Considering that NMS has been an integral part of OD systems,
thwarting the functionality of NMS can result in unexpected or even lethal
consequences for such systems. In this paper, an adversarial example attack
which triggers malfunctioning of NMS in end-to-end OD models is proposed. The
attack, namely \texttt{Daedalus}, compresses the dimensions of detection boxes
to evade NMS. As a result, the final detection output contains extremely dense
false positives. This can be fatal for many OD applications such as autonomous
vehicles and surveillance systems. The attack can be generalised to different
end-to-end OD models, such that the attack cripples various OD applications.
Furthermore, a way to craft robust adversarial examples is developed by using
an ensemble of popular detection models as the substitutes. Considering the
pervasive nature of model reusing in real-world OD scenarios, Daedalus examples
crafted based on an \textit{ensemble of substitutes} can launch attacks without
knowing the parameters of the victim models. Experimental results demonstrate
that the attack effectively stops NMS from filtering redundant bounding boxes.
As the evaluation results suggest, Daedalus increases the false positive rate
in detection results to $99.9\%$ and reduces the mean average precision scores
to $0$, while maintaining a low cost of distortion on the original inputs. It
is also demonstrated that the attack can be practically launched against
real-world OD systems via printed posters. | [
"cs.CV"
] |
Graph representation learning has become a ubiquitous component in many
scenarios, ranging from social network analysis to energy forecasting in smart
grids. In several applications, ensuring the fairness of the node (or graph)
representations with respect to some protected attributes is crucial for their
correct deployment. Yet, fairness in graph deep learning remains
under-explored, with few solutions available. In particular, the tendency of
similar nodes to cluster on several real-world graphs (i.e., homophily) can
dramatically worsen the fairness of these procedures. In this paper, we propose
a biased edge dropout algorithm (FairDrop) to counter-act homophily and improve
fairness in graph representation learning. FairDrop can be plugged in easily on
many existing algorithms, is efficient, adaptable, and can be combined with
other fairness-inducing solutions. After describing the general algorithm, we
demonstrate its application on two benchmark tasks, specifically, as a random
walk model for producing node embeddings, and to a graph convolutional network
for link prediction. We prove that the proposed algorithm can successfully
improve the fairness of all models up to a small or negligible drop in
accuracy, and compares favourably with existing state-of-the-art solutions. In
an ablation study, we demonstrate that our algorithm can flexibly interpolate
between biasing towards fairness and an unbiased edge dropout. Furthermore, to
better evaluate the gains, we propose a new dyadic group definition to measure
the bias of a link prediction task when paired with group-based fairness
metrics. In particular, we extend the metric used to measure the bias in the
node embeddings to take into account the graph structure. | [
"cs.LG",
"stat.ML"
] |
The performance of object detection, to a great extent, depends on the
availability of large annotated datasets. To alleviate the annotation cost, the
research community has explored a number of ways to exploit unlabeled or weakly
labeled data. However, such efforts have met with limited success so far. In
this work, we revisit the problem with a pragmatic standpoint, trying to
explore a new balance between detection performance and annotation cost by
jointly exploiting fully and weakly annotated data. Specifically, we propose a
weakly- and semi-supervised object detection framework (WSSOD), which involves
a two-stage learning procedure. An agent detector is first trained on a joint
dataset and then used to predict pseudo bounding boxes on weakly-annotated
images. The underlying assumptions in the current as well as common
semi-supervised pipelines are also carefully examined under a unified EM
formulation. On top of this framework, weakly-supervised loss (WSL), label
attention and random pseudo-label sampling (RPS) strategies are introduced to
relax these assumptions, bringing additional improvement on the efficacy of the
detection pipeline. The proposed framework demonstrates remarkable performance
on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to
those obtained in fully-supervised settings, with only one third of the
annotations. | [
"cs.CV"
] |
Integrating higher level visual and linguistic interpretations is at the
heart of human intelligence. As automatic visual category recognition in images
is approaching human performance, the high level understanding in the dynamic
spatiotemporal domain of videos and its translation into natural language is
still far from being solved. While most works on vision-to-text translations
use pre-learned or pre-established computational linguistic models, in this
paper we present an approach that uses vision alone to efficiently learn how to
translate into language the video content. We discover, in simple form, the
story played by main actors, while using only visual cues for representing
objects and their interactions. Our method learns in a hierarchical manner
higher level representations for recognizing subjects, actions and objects
involved, their relevant contextual background and their interaction to one
another over time. We have a three stage approach: first we take in
consideration features of the individual entities at the local level of
appearance, then we consider the relationship between these objects and actions
and their video background, and third, we consider their spatiotemporal
relations as inputs to classifiers at the highest level of interpretation.
Thus, our approach finds a coherent linguistic description of videos in the
form of a subject, verb and object based on their role played in the overall
visual story learned directly from training data, without using a known
language model. We test the efficiency of our approach on a large scale dataset
containing YouTube clips taken in the wild and demonstrate state-of-the-art
performance, often superior to current approaches that use more complex,
pre-learned linguistic knowledge. | [
"cs.CV",
"cs.CL"
] |
Automatically discovering image categories in unlabeled natural images is one
of the important goals of unsupervised learning. However, the task is
challenging and even human beings define visual categories based on a large
amount of prior knowledge. In this paper, we similarly utilize prior knowledge
to facilitate the discovery of image categories. We present a novel end-to-end
network to map unlabeled images to categories as a clustering network. We
propose that this network can be learned with contrastive loss which is only
based on weak binary pair-wise constraints. Such binary constraints can be
learned from datasets in other domains as transferred similarity functions,
which mimic a simple knowledge transfer. We first evaluate our experiments on
the MNIST dataset as a proof of concept, based on predicted similarities
trained on Omniglot, showing a 99\% accuracy which significantly outperforms
clustering based approaches. Then we evaluate the discovery performance on
Cifar-10, STL-10, and ImageNet, which achieves both state-of-the-art accuracy
and shows it can be scalable to various large natural images. | [
"cs.CV",
"cs.LG"
] |
Multi-view clustering attracts much attention recently, which aims to take
advantage of multi-view information to improve the performance of clustering.
However, most recent work mainly focus on self-representation based subspace
clustering, which is of high computation complexity. In this paper, we focus on
the Markov chain based spectral clustering method and propose a novel essential
tensor learning method to explore the high order correlations for multi-view
representation. We first construct a tensor based on multi-view transition
probability matrices of the Markov chain. By incorporating the idea from robust
principle component analysis, tensor singular value decomposition (t-SVD) based
tensor nuclear norm is imposed to preserve the low-rank property of the
essential tensor, which can well capture the principle information from
multiple views. We also employ the tensor rotation operator for this task to
better investigate the relationship among views as well as reduce the
computation complexity. The proposed method can be efficiently optimized by the
alternating direction method of multipliers~(ADMM). Extensive experiments on
six real world datasets corresponding to five different applications show that
our method achieves superior performance over other state-of-the-art methods. | [
"cs.CV"
] |
Accurate 3D object detection from point clouds has become a crucial component
in autonomous driving. However, the volumetric representations and the
projection methods in previous works fail to establish the relationships
between the local point sets. In this paper, we propose Sparse Voxel-Graph
Attention Network (SVGA-Net), a novel end-to-end trainable network which mainly
contains voxel-graph module and sparse-to-dense regression module to achieve
comparable 3D detection tasks from raw LIDAR data. Specifically, SVGA-Net
constructs the local complete graph within each divided 3D spherical voxel and
global KNN graph through all voxels. The local and global graphs serve as the
attention mechanism to enhance the extracted features. In addition, the novel
sparse-to-dense regression module enhances the 3D box estimation accuracy
through feature maps aggregation at different levels. Experiments on KITTI
detection benchmark demonstrate the efficiency of extending the graph
representation to 3D object detection and the proposed SVGA-Net can achieve
decent detection accuracy. | [
"cs.CV"
] |
Wind power, as an alternative to burning fossil fuels, is abundant and
inexhaustible. To fully utilize wind power, wind farms are usually located in
areas of high altitude and facing serious ice conditions, which can lead to
serious consequences. Quick detection of blade ice accretion is crucial for the
maintenance of wind farms. Unlike traditional methods of installing expensive
physical detectors on wind blades, data-driven approaches are increasingly
popular for inspecting the wind turbine failures. In this work, we propose a
wavelet enhanced autoencoder model (WaveletAE) to identify wind turbine
dysfunction by analyzing the multivariate time series monitored by the SCADA
system. WaveletAE is enhanced with wavelet detail coefficients to enforce the
autoencoder to capture information from multiple scales, and the CNN-LSTM
architecture is applied to learn channel-wise and temporal-wise relations. The
empirical study shows that the proposed model outperforms other
state-of-the-art time series anomaly detection methods for real-world blade
icing detection. | [
"cs.LG",
"stat.ML"
] |
Generative Adversarial Networks (GANs) can successfully approximate a
probability distribution and produce realistic samples. However, open questions
such as sufficient convergence conditions and mode collapse still persist. In
this paper, we build on existing work in the area by proposing a novel
framework for training the generator against an ensemble of discriminator
networks, which can be seen as a one-student/multiple-teachers setting. We
formalize this problem within the full-information adversarial bandit
framework, where we evaluate the capability of an algorithm to select mixtures
of discriminators for providing the generator with feedback during learning. To
this end, we propose a reward function which reflects the progress made by the
generator and dynamically update the mixture weights allocated to each
discriminator. We also draw connections between our algorithm and stochastic
optimization methods and then show that existing approaches using multiple
discriminators in literature can be recovered from our framework. We argue that
less expressive discriminators are smoother and have a general coarse grained
view of the modes map, which enforces the generator to cover a wide portion of
the data distribution support. On the other hand, highly expressive
discriminators ensure samples quality. Finally, experimental results show that
our approach improves samples quality and diversity over existing baselines by
effectively learning a curriculum. These results also support the claim that
weaker discriminators have higher entropy improving modes coverage. Keywords:
multiple discriminators, curriculum learning, multiple resolutions
discriminators, multi-armed bandits, generative adversarial networks, smooth
discriminators, multi-discriminator gan training, multiple experts. | [
"cs.LG",
"stat.ML"
] |
Recently, learning-based approaches for 3D model reconstruction have
attracted attention owing to its modern applications such as Extended
Reality(XR), robotics and self-driving cars. Several approaches presented good
performance on reconstructing 3D shapes by learning solely from images, i.e.,
without using 3D models in training. Challenges, however, remain in texture
generation due to the gap between 2D and 3D modals. In previous work, the grid
sampling mechanism from Spatial Transformer Networks was adopted to sample
color from an input image to formulate texture. Despite its success, the
existing framework has limitations on searching scope in sampling, resulting in
flaws in generated texture and consequentially on rendered 3D models. In this
paper, to solve that issue, we present a novel sampling algorithm by optimizing
the gradient of predicted coordinates based on the variance on the sampling
image. Taking into account the semantics of the image, we adopt Frechet
Inception Distance (FID) to form a loss function in learning, which helps
bridging the gap between rendered images and input images. As a result, we
greatly improve generated texture. Furthermore, to optimize 3D shape
reconstruction and to accelerate convergence at training, we adopt part
segmentation and template learning in our model. Without any 3D supervision in
learning, and with only a collection of single-view 2D images, the shape and
texture learned by our model outperform those from previous work. We
demonstrate the performance with experimental results on a publically available
dataset. | [
"cs.CV"
] |
In this work, we propose a novel Cycle In Cycle Generative Adversarial
Network (C$^2$GAN) for the task of keypoint-guided image generation. The
proposed C$^2$GAN is a cross-modal framework exploring a joint exploitation of
the keypoint and the image data in an interactive manner. C$^2$GAN contains two
different types of generators, i.e., keypoint-oriented generator and
image-oriented generator. Both of them are mutually connected in an end-to-end
learnable fashion and explicitly form three cycled sub-networks, i.e., one
image generation cycle and two keypoint generation cycles. Each cycle not only
aims at reconstructing the input domain, and also produces useful output
involving in the generation of another cycle. By so doing, the cycles constrain
each other implicitly, which provides complementary information from the two
different modalities and brings extra supervision across cycles, thus
facilitating more robust optimization of the whole network. Extensive
experimental results on two publicly available datasets, i.e., Radboud Faces
and Market-1501, demonstrate that our approach is effective to generate more
photo-realistic images compared with state-of-the-art models. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
For medical image segmentation, most fully convolutional networks (FCNs) need
strong supervision through a large sample of high-quality dense segmentations,
which is taxing in terms of costs, time and logistics involved. This burden of
annotation can be alleviated by exploiting weak inexpensive annotations such as
bounding boxes and anatomical landmarks. However, it is very difficult to
\textit{a priori} estimate the optimal balance between the number of
annotations needed for each supervision type that leads to maximum performance
with the least annotation cost. To optimize this cost-performance trade off, we
present a budget-based cost-minimization framework in a mixed-supervision
setting via dense segmentations, bounding boxes, and landmarks. We propose a
linear programming (LP) formulation combined with uncertainty and similarity
based ranking strategy to judiciously select samples to be annotated next for
optimal performance. In the results section, we show that our proposed method
achieves comparable performance to state-of-the-art approaches with
significantly reduced cost of annotations. | [
"cs.CV"
] |
Despite the great advances made in the field of image super-resolution (ISR)
during the last years, the performance has merely been evaluated perceptually.
Thus, it is still unclear whether ISR is helpful for other vision tasks. In
this paper, we present the first comprehensive study and analysis of the
usefulness of ISR for other vision applications. In particular, six ISR methods
are evaluated on four popular vision tasks, namely edge detection, semantic
image segmentation, digit recognition, and scene recognition. We show that
applying ISR to input images of other vision systems does improve their
performance when the input images are of low-resolution. We also study the
correlation between four standard perceptual evaluation criteria (namely PSNR,
SSIM, IFC, and NQM) and the usefulness of ISR to the vision tasks. Experiments
show that they correlate well with each other in general, but perceptual
criteria are still not accurate enough to be used as full proxies for the
usefulness. We hope this work will inspire the community to evaluate ISR
methods also in real vision applications, and to adopt ISR as a pre-processing
step of other vision tasks if the resolution of their input images is low. | [
"cs.CV"
] |
Medical image segmentation is referred to the segmentation of known anatomic
structures from different medical images. Normally, the medical data researches
are more complicated and an exclusive structures. This computer aided diagnosis
is used for assisting doctors in evaluating medical imagery or in recognizing
abnormal findings in a medical image. To integrate the specialized knowledge
for medical data processing is helpful to form a real useful healthcare
decision making system. This paper studies the different symmetry based
distances applied in clustering algorithms and analyzes symmetry approach for
Positron Emission Tomography (PET) scan image segmentation. Unlike CT and MRI,
the PET scan identifies the structure of blood flow to and from organs. PET
scan also helps in early diagnosis of cancer and heart, brain and gastro
intestinal ailments and to detect the progress of treatment. In this paper, the
scope diagnostic task expands for PET image in various brain functions. | [
"cs.CV"
] |
This paper introduces the real image Super-Resolution (SR) challenge that was
part of the Advances in Image Manipulation (AIM) workshop, held in conjunction
with ECCV 2020. This challenge involves three tracks to super-resolve an input
image for $\times$2, $\times$3 and $\times$4 scaling factors, respectively. The
goal is to attract more attention to realistic image degradation for the SR
task, which is much more complicated and challenging, and contributes to
real-world image super-resolution applications. 452 participants were
registered for three tracks in total, and 24 teams submitted their results.
They gauge the state-of-the-art approaches for real image SR in terms of PSNR
and SSIM. | [
"cs.CV"
] |
Fonts are one of the most basic and core design concepts. Numerous use cases
can benefit from an in depth understanding of Fonts such as Text Customization
which can change text in an image while maintaining the Font attributes like
style, color, size. Currently, Text recognition solutions can group recognized
text based on line breaks or paragraph breaks, if the Font attributes are known
multiple text blocks can be combined based on context in a meaningful manner.
In this paper, we propose two engines: Font Detection Engine, which identifies
the font style, color and size attributes of text in an image and a Font
Prediction Engine, which predicts similar fonts for a query font. Major
contributions of this paper are three-fold: First, we developed a novel CNN
architecture for identifying font style of text in images. Second, we designed
a novel algorithm for predicting similar fonts for a given query font. Third,
we have optimized and deployed the entire engine On-Device which ensures
privacy and improves latency in real time applications such as instant
messaging. We achieve a worst case On-Device inference time of 30ms and a model
size of 4.5MB for both the engines. | [
"cs.CV",
"cs.LG"
] |
Significant performance improvement has been achieved for fully-supervised
video salient object detection with the pixel-wise labeled training datasets,
which are time-consuming and expensive to obtain. To relieve the burden of data
annotation, we present the first weakly supervised video salient object
detection model based on relabeled "fixation guided scribble annotations".
Specifically, an "Appearance-motion fusion module" and bidirectional ConvLSTM
based framework are proposed to achieve effective multi-modal learning and
long-term temporal context modeling based on our new weak annotations. Further,
we design a novel foreground-background similarity loss to further explore the
labeling similarity across frames. A weak annotation boosting strategy is also
introduced to boost our model performance with a new pseudo-label generation
technique. Extensive experimental results on six benchmark video saliency
detection datasets illustrate the effectiveness of our solution. | [
"cs.CV"
] |
A wide range of applications in marine ecology extensively uses underwater
cameras. Still, to efficiently process the vast amount of data generated, we
need to develop tools that can automatically detect and recognize species
captured on film. Classifying fish species from videos and images in natural
environments can be challenging because of noise and variation in illumination
and the surrounding habitat. In this paper, we propose a two-step deep learning
approach for the detection and classification of temperate fishes without
pre-filtering. The first step is to detect each single fish in an image,
independent of species and sex. For this purpose, we employ the You Only Look
Once (YOLO) object detection technique. In the second step, we adopt a
Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE)
architecture for classifying each fish in the image without pre-filtering. We
apply transfer learning to overcome the limited training samples of temperate
fishes and to improve the accuracy of the classification. This is done by
training the object detection model with ImageNet and the fish classifier via a
public dataset (Fish4Knowledge), whereupon both the object detection and
classifier are updated with temperate fishes of interest. The weights obtained
from pre-training are applied to post-training as a priori. Our solution
achieves the state-of-the-art accuracy of 99.27\% on the pre-training. The
percentage values for accuracy on the post-training are good; 83.68\% and
87.74\% with and without image augmentation, respectively, indicating that the
solution is viable with a more extensive dataset. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Compressed sensing techniques enable efficient acquisition and recovery of
sparse, high-dimensional data signals via low-dimensional projections. In this
work, we propose Uncertainty Autoencoders, a learning framework for
unsupervised representation learning inspired by compressed sensing. We treat
the low-dimensional projections as noisy latent representations of an
autoencoder and directly learn both the acquisition (i.e., encoding) and
amortized recovery (i.e., decoding) procedures. Our learning objective
optimizes for a tractable variational lower bound to the mutual information
between the datapoints and the latent representations. We show how our
framework provides a unified treatment to several lines of research in
dimensionality reduction, compressed sensing, and generative modeling.
Empirically, we demonstrate a 32% improvement on average over competing
approaches for the task of statistical compressed sensing of high-dimensional
datasets. | [
"stat.ML",
"cs.LG",
"cs.NE"
] |
The pair-matching problem appears in many applications where one wants to
discover good matches between pairs of entities or individuals. Formally, the
set of individuals is represented by the nodes of a graph where the edges,
unobserved at first, represent the good matches. The algorithm queries pairs of
nodes and observes the presence/absence of edges. Its goal is to discover as
many edges as possible with a fixed budget of queries. Pair-matching is a
particular instance of multi-armed bandit problem in which the arms are pairs
of individuals and the rewards are edges linking these pairs. This bandit
problem is non-standard though, as each arm can only be played once.
Given this last constraint, sublinear regret can be expected only if the
graph presents some underlying structure. This paper shows that sublinear
regret is achievable in the case where the graph is generated according to a
Stochastic Block Model (SBM) with two communities. Optimal regret bounds are
computed for this pair-matching problem. They exhibit a phase transition
related to the Kesten-Stigum threshold for community detection in SBM. The
pair-matching problem is considered in the case where each node is constrained
to be sampled less than a given amount of times. We show how optimal regret
rates depend on this constraint. The paper is concluded by a conjecture
regarding the optimal regret when the number of communities is larger than 2.
Contrary to the two communities case, we argue that a statistical-computational
gap would appear in this problem. | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH",
"62h30, 68T05, 05C80"
] |
Existing action detection algorithms usually generate action proposals
through an extensive search over the video at multiple temporal scales, which
brings about huge computational overhead and deviates from the human perception
procedure. We argue that the process of detecting actions should be naturally
one of observation and refinement: observe the current window and refine the
span of attended window to cover true action regions. In this paper, we propose
an active action proposal model that learns to find actions through
continuously adjusting the temporal bounds in a self-adaptive way. The whole
process can be deemed as an agent, which is firstly placed at a position in the
video at random, adopts a sequence of transformations on the current attended
region to discover actions according to a learned policy. We utilize
reinforcement learning, especially the Deep Q-learning algorithm to learn the
agent's decision policy. In addition, we use temporal pooling operation to
extract more effective feature representation for the long temporal window, and
design a regression network to adjust the position offsets between predicted
results and the ground truth. Experiment results on THUMOS 2014 validate the
effectiveness of the proposed approach, which can achieve competitive
performance with current action detection algorithms via much fewer proposals. | [
"cs.CV"
] |
Three-dimensional medical image segmentation is one of the most important
problems in medical image analysis and plays a key role in downstream diagnosis
and treatment. Recent years, deep neural networks have made groundbreaking
success in medical image segmentation problem. However, due to the high
variance in instrumental parameters, experimental protocols, and subject
appearances, the generalization of deep learning models is often hindered by
the inconsistency in medical images generated by different machines and
hospitals. In this work, we present StyleSegor, an efficient and easy-to-use
strategy to alleviate this inconsistency issue. Specifically, neural style
transfer algorithm is applied to unlabeled data in order to minimize the
differences in image properties including brightness, contrast, texture, etc.
between the labeled and unlabeled data. We also apply probabilistic adjustment
on the network output and integrate multiple predictions through ensemble
learning. On a publicly available whole heart segmentation benchmarking dataset
from MICCAI HVSMR 2016 challenge, we have demonstrated an elevated dice
accuracy surpassing current state-of-the-art method and notably, an improvement
of the total score by 29.91\%. StyleSegor is thus corroborated to be an
accurate tool for 3D whole heart segmentation especially on highly inconsistent
data, and is available at https://github.com/horsepurve/StyleSegor. | [
"cs.CV"
] |
We propose a robust variational autoencoder with $\beta$ divergence for
tabular data (RTVAE) with mixed categorical and continuous features.
Variational autoencoders (VAE) and their variations are popular frameworks for
anomaly detection problems. The primary assumption is that we can learn
representations for normal patterns via VAEs and any deviation from that can
indicate anomalies. However, the training data itself can contain outliers. The
source of outliers in training data include the data collection process itself
(random noise) or a malicious attacker (data poisoning) who may target to
degrade the performance of the machine learning model. In either case, these
outliers can disproportionately affect the training process of VAEs and may
lead to wrong conclusions about what the normal behavior is. In this work, we
derive a novel form of a variational autoencoder for tabular data sets with
categorical and continuous features that is robust to outliers in training
data. Our results on the anomaly detection application for network traffic
datasets demonstrate the effectiveness of our approach. | [
"cs.LG",
"eess.IV",
"eess.SP",
"stat.ML"
] |
This paper proposes a novel MAP inference framework for Markov Random Field
(MRF) in parallel computing environments. The inference framework, dubbed Swarm
Fusion, is a natural generalization of the Fusion Move method. Every thread (in
a case of multi-threading environments) maintains and updates a solution. At
each iteration, a thread can generate arbitrary number of solution proposals
and take arbitrary number of concurrent solutions from the other threads to
perform multi-way fusion in updating its solution. The framework is general,
making popular existing inference techniques such as alpha-expansion, fusion
move, parallel alpha-expansion, and hierarchical fusion, its special cases. We
have evaluated the effectiveness of our approach against competing methods on
three problems of varying difficulties, in particular, the stereo, the optical
flow, and the layered depthmap estimation problems. | [
"cs.CV"
] |
The usage of convolutional neural networks (CNNs) for unsupervised image
segmentation was investigated in this study. In the proposed approach, label
prediction and network parameter learning are alternately iterated to meet the
following criteria: (a) pixels of similar features should be assigned the same
label, (b) spatially continuous pixels should be assigned the same label, and
(c) the number of unique labels should be large. Although these criteria are
incompatible, the proposed approach minimizes the combination of similarity
loss and spatial continuity loss to find a plausible solution of label
assignment that balances the aforementioned criteria well. The contributions of
this study are four-fold. First, we propose a novel end-to-end network of
unsupervised image segmentation that consists of normalization and an argmax
function for differentiable clustering. Second, we introduce a spatial
continuity loss function that mitigates the limitations of fixed segment
boundaries possessed by previous work. Third, we present an extension of the
proposed method for segmentation with scribbles as user input, which showed
better accuracy than existing methods while maintaining efficiency. Finally, we
introduce another extension of the proposed method: unseen image segmentation
by using networks pre-trained with a few reference images without re-training
the networks. The effectiveness of the proposed approach was examined on
several benchmark datasets of image segmentation. | [
"cs.CV"
] |
Point cloud based retrieval for place recognition is an emerging problem in
vision field. The main challenge is how to find an efficient way to encode the
local features into a discriminative global descriptor. In this paper, we
propose a Point Contextual Attention Network (PCAN), which can predict the
significance of each local point feature based on point context. Our network
makes it possible to pay more attention to the task-relevent features when
aggregating local features. Experiments on various benchmark datasets show that
the proposed network can provide outperformance than current state-of-the-art
approaches. | [
"cs.CV",
"cs.RO"
] |
We introduce a novel learning-based, visibility-aware, surface reconstruction
method for large-scale, defect-laden point clouds. Our approach can cope with
the scale and variety of point cloud defects encountered in real-life
Multi-View Stereo (MVS) acquisitions. Our method relies on a 3D Delaunay
tetrahedralization whose cells are classified as inside or outside the surface
by a graph neural network and an energy model solvable with a graph cut. Our
model, making use of both local geometric attributes and line-of-sight
visibility information, is able to learn a visibility model from a small amount
of synthetic training data and generalizes to real-life acquisitions. Combining
the efficiency of deep learning methods and the scalability of energy based
models, our approach outperforms both learning and non learning-based
reconstruction algorithms on two publicly available reconstruction benchmarks. | [
"cs.CV",
"cs.CG"
] |
We have developed a deep learning network for classification of different
flowers. For this, we have used Visual Geometry Group's 102 category flower
dataset having 8189 images of 102 different flowers from University of Oxford.
The method is basically divided into two parts; Image segmentation and
classification. We have compared the performance of two different Convolutional
Neural Network architectures GoogLeNet and AlexNet for classification purpose.
By keeping the hyper parameters same for both architectures, we have found that
the top 1 and top 5 accuracies of GoogLeNet are 47.15% and 69.17% respectively
whereas the top 1 and top 5 accuracies of AlexNet are 43.39% and 68.68%
respectively. These results are extremely good when compared to random
classification accuracy of 0.98%. This method for classification of flowers can
be implemented in real time applications and can be used to help botanists for
their research as well as camping enthusiasts. | [
"cs.CV"
] |
Automating report generation for medical imaging promises to reduce workload
and assist diagnosis in clinical practice. Recent work has shown that deep
learning models can successfully caption natural images. However, learning from
medical data is challenging due to the diversity and uncertainty inherent in
the reports written by different radiologists with discrepant expertise and
experience. To tackle these challenges, we propose variational topic inference
for automatic report generation. Specifically, we introduce a set of topics as
latent variables to guide sentence generation by aligning image and language
modalities in a latent space. The topics are inferred in a conditional
variational inference framework, with each topic governing the generation of a
sentence in the report. Further, we adopt a visual attention module that
enables the model to attend to different locations in the image and generate
more informative descriptions. We conduct extensive experiments on two
benchmarks, namely Indiana U. Chest X-rays and MIMIC-CXR. The results
demonstrate that our proposed variational topic inference method can generate
novel reports rather than mere copies of reports used in training, while still
achieving comparable performance to state-of-the-art methods in terms of
standard language generation criteria. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Image quality that is consistent with human opinion is assessed by a
perceptual image quality assessment (IQA) that defines/utilizes a computational
model. A good model should take effectiveness and efficiency into
consideration, but most of the previously proposed IQA models do not
simultaneously consider these factors. Therefore, this study attempts to
develop an effective and efficient IQA metric. Contrast is an inherent visual
attribute that indicates image quality, and visual saliency (VS) is a quality
that attracts the attention of human beings. The proposed model utilized these
two features to characterize the image local quality. After obtaining the local
contrast quality map and the global VS quality map, we added the weighted
standard deviation of the previous two quality maps together to yield the final
quality score. The experimental results for three benchmark databases (LIVE,
TID2008, and CSIQ) demonstrated that our model performs the best in terms of a
correlation with the human judgment of visual quality. Furthermore, compared
with competing IQA models, this proposed model is more efficient. | [
"cs.CV"
] |
Explainable components in XAI algorithms often come from a familiar set of
models, such as linear models or decision trees. We formulate an approach where
the type of explanation produced is guided by a specification. Specifications
are elicited from the user, possibly using interaction with the user and
contributions from other areas. Areas where a specification could be obtained
include forensic, medical, and scientific applications. Providing a menu of
possible types of specifications in an area is an exploratory knowledge
representation and reasoning task for the algorithm designer, aiming at
understanding the possibilities and limitations of efficiently computable modes
of explanations. Two examples are discussed: explanations for Bayesian networks
using the theory of argumentation, and explanations for graph neural networks.
The latter case illustrates the possibility of having a representation
formalism available to the user for specifying the type of explanation
requested, for example, a chemical query language for classifying molecules.
The approach is motivated by a theory of explanation in the philosophy of
science, and it is related to current questions in the philosophy of science on
the role of machine learning. | [
"cs.LG",
"cs.AI",
"cs.HC",
"stat.ML"
] |
In this paper, we focus on the challenging multicategory instance
segmentation problem in remote sensing images (RSIs), which aims at predicting
the categories of all instances and localizing them with pixel-level masks.
Although many landmark frameworks have demonstrated promising performance in
instance segmentation, the complexity in the background and scale variability
instances still remain challenging for instance segmentation of RSIs. To
address the above problems, we propose an end-to-end multi-category instance
segmentation model, namely Semantic Attention and Scale Complementary Network,
which mainly consists of a Semantic Attention (SEA) module and a Scale
Complementary Mask Branch (SCMB). The SEA module contains a simple fully
convolutional semantic segmentation branch with extra supervision to strengthen
the activation of interest instances on the feature map and reduce the
background noise's interference. To handle the under-segmentation of geospatial
instances with large varying scales, we design the SCMB that extends the
original single mask branch to trident mask branches and introduces
complementary mask supervision at different scales to sufficiently leverage the
multi-scale information. We conduct comprehensive experiments to evaluate the
effectiveness of our proposed method on the iSAID dataset and the NWPU Instance
Segmentation dataset and achieve promising performance. | [
"cs.CV"
] |
Image aesthetic quality assessment has been a relatively hot topic during the
last decade. Most recently, comments type assessment (aesthetic captions) has
been proposed to describe the general aesthetic impression of an image using
text. In this paper, we propose Aesthetic Attributes Assessment of Images,
which means the aesthetic attributes captioning. This is a new formula of image
aesthetic assessment, which predicts aesthetic attributes captions together
with the aesthetic score of each attribute. We introduce a new dataset named
\emph{DPC-Captions} which contains comments of up to 5 aesthetic attributes of
one image through knowledge transfer from a full-annotated small-scale dataset.
Then, we propose Aesthetic Multi-Attribute Network (AMAN), which is trained on
a mixture of fully-annotated small-scale PCCD dataset and weakly-annotated
large-scale DPC-Captions dataset. Our AMAN makes full use of transfer learning
and attention model in a single framework. The experimental results on our
DPC-Captions and PCCD dataset reveal that our method can predict captions of 5
aesthetic attributes together with numerical score assessment of each
attribute. We use the evaluation criteria used in image captions to prove that
our specially designed AMAN model outperforms traditional CNN-LSTM model and
modern SCA-CNN model of image captions. | [
"cs.CV"
] |
Generating novel graph structures that optimize given objectives while
obeying some given underlying rules is fundamental for chemistry, biology and
social science research. This is especially important in the task of molecular
graph generation, whose goal is to discover novel molecules with desired
properties such as drug-likeness and synthetic accessibility, while obeying
physical laws such as chemical valency. However, designing models to find
molecules that optimize desired properties while incorporating highly complex
and non-differentiable rules remains to be a challenging task. Here we propose
Graph Convolutional Policy Network (GCPN), a general graph convolutional
network based model for goal-directed graph generation through reinforcement
learning. The model is trained to optimize domain-specific rewards and
adversarial loss through policy gradient, and acts in an environment that
incorporates domain-specific rules. Experimental results show that GCPN can
achieve 61% improvement on chemical property optimization over state-of-the-art
baselines while resembling known molecules, and achieve 184% improvement on the
constrained property optimization task. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
An Objective Structured Practical Examination (OSPE) is an effective and
robust, but resource-intensive, means of evaluating anatomical knowledge. Since
most OSPEs employ short answer or fill-in-the-blank style questions, the format
requires many people familiar with the content to mark the exams. However, the
increasing prevalence of online delivery for anatomy and physiology courses
could result in students losing the OSPE practice that they would receive in
face-to-face learning sessions. The purpose of this study was to test the
accuracy of Decision Trees (DTs) in marking OSPE questions as a potential first
step to creating an intelligent, online OSPE tutoring system. The study used
the results of the winter 2020 semester final OSPE from McMaster University's
anatomy and physiology course in the Faculty of Health Sciences (HTHSCI
2FF3/2LL3/1D06) as the data set. Ninety percent of the data set was used in a
10-fold validation algorithm to train a DT for each of the 54 questions. Each
DT was comprised of unique words that appeared in correct, student-written
answers. The remaining 10% of the data set was marked by the generated DTs.
When the answers marked by the DT were compared to the answers marked by staff
and faculty, the DT achieved an average accuracy of 94.49% across all 54
questions. This suggests that machine learning algorithms such as DTs are a
highly effective option for OSPE grading and are suitable for the development
of an intelligent, online OSPE tutoring system. | [
"cs.LG"
] |
Remarkable performance from Transformer networks in Natural Language
Processing promote the development of these models in dealing with computer
vision tasks such as image recognition and segmentation. In this paper, we
introduce a novel framework, called Multi-level Multi-scale Point Transformer
(MLMSPT) that works directly on the irregular point clouds for representation
learning. Specifically, a point pyramid transformer is investigated to model
features with diverse resolutions or scales we defined, followed by a
multi-level transformer module to aggregate contextual information from
different levels of each scale and enhance their interactions. While a
multi-scale transformer module is designed to capture the dependencies among
representations across different scales. Extensive evaluation on public
benchmark datasets demonstrate the effectiveness and the competitive
performance of our methods on 3D shape classification, part segmentation and
semantic segmentation tasks. | [
"cs.CV"
] |
In real world applications like healthcare, it is usually difficult to build
a machine learning prediction model that works universally well across
different institutions. At the same time, the available model is often
proprietary, i.e., neither the model parameter nor the data set used for model
training is accessible. In consequence, leveraging the knowledge hidden in the
available model (aka. the hypothesis) and adapting it to a local data set
becomes extremely challenging. Motivated by this situation, in this paper we
aim to address such a specific case within the hypothesis transfer learning
framework, in which 1) the source hypothesis is a black-box model and 2) the
source domain data is unavailable. In particular, we introduce a novel
algorithm called dynamic knowledge distillation for hypothesis transfer
learning (dkdHTL). In this method, we use knowledge distillation with
instance-wise weighting mechanism to adaptively transfer the "dark" knowledge
from the source hypothesis to the target domain.The weighting coefficients of
the distillation loss and the standard loss are determined by the consistency
between the predicted probability of the source hypothesis and the target
ground-truth label.Empirical results on both transfer learning benchmark
datasets and a healthcare dataset demonstrate the effectiveness of our method. | [
"cs.LG",
"stat.ML"
] |
This paper presents a unified framework to tackle estimation problems in
Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The use
of SVMs in estimation problems has been traditionally limited to its mere use
as a black-box model. Noting such limitations in the literature, we take
advantage of several properties of Mercer's kernels and functional analysis to
develop a family of SVM methods for estimation in DSP. Three types of signal
model equations are analyzed. First, when a specific time-signal structure is
assumed to model the underlying system that generated the data, the linear
signal model (so called Primal Signal Model formulation) is first stated and
analyzed. Then, non-linear versions of the signal structure can be readily
developed by following two different approaches. On the one hand, the signal
model equation is written in reproducing kernel Hilbert spaces (RKHS) using the
well-known RKHS Signal Model formulation, and Mercer's kernels are readily used
in SVM non-linear algorithms. On the other hand, in the alternative and not so
common Dual Signal Model formulation, a signal expansion is made by using an
auxiliary signal model equation given by a non-linear regression of each time
instant in the observed time series. These building blocks can be used to
generate different novel SVM-based methods for problems of signal estimation,
and we deal with several of the most important ones in DSP. We illustrate the
usefulness of this methodology by defining SVM algorithms for linear and
non-linear system identification, spectral analysis, nonuniform interpolation,
sparse deconvolution, and array processing. The performance of the developed
SVM methods is compared to standard approaches in all these settings. The
experimental results illustrate the generality, simplicity, and capabilities of
the proposed SVM framework for DSP. | [
"stat.ML",
"stat.AP"
] |
We explore encoding brain symmetry into a neural network for a brain tumor
segmentation task. A healthy human brain is symmetric at a high level of
abstraction, and the high-level asymmetric parts are more likely to be tumor
regions. Paying more attention to asymmetries has the potential to boost the
performance in brain tumor segmentation. We propose a method to encode brain
symmetry into existing neural networks and apply the method to a
state-of-the-art neural network for medical imaging segmentation. We evaluate
our symmetry-encoded network on the dataset from a brain tumor segmentation
challenge and verify that the new model extracts information in the training
images more efficiently than the original model. | [
"cs.CV"
] |
We propose a novel bootstrap procedure for dependent data based on Generative
Adversarial networks (GANs). We show that the dynamics of common stationary
time series processes can be learned by GANs and demonstrate that GANs trained
on a single sample path can be used to generate additional samples from the
process. We find that temporal convolutional neural networks provide a suitable
design for the generator and discriminator, and that convincing samples can be
generated on the basis of a vector of iid normal noise. We demonstrate the
finite sample properties of GAN sampling and the suggested bootstrap using
simulations where we compare the performance to circular block bootstrapping in
the case of resampling an AR(1) time series processes. We find that resampling
using the GAN can outperform circular block bootstrapping in terms of empirical
coverage. | [
"cs.LG",
"econ.EM",
"stat.ME",
"stat.ML"
] |
Camera pose estimation in large-scale environments is still an open question
and, despite recent promising results, it may still fail in some situations.
The research so far has focused on improving subcomponents of estimation
pipelines, to achieve more accurate poses. However, there is no guarantee for
the result to be correct, even though the correctness of pose estimation is
critically important in several visual localization applications,such as in
autonomous navigation. In this paper we bring to attention a novel research
question, pose confidence estimation,where we aim at quantifying how reliable
the visually estimated pose is. We develop a novel confidence measure to fulfil
this task and show that it can be flexibly applied to different datasets,indoor
or outdoor, and for various visual localization pipelines.We also show that the
proposed techniques can be used to accomplish a secondary goal: improving the
accuracy of existing pose estimation pipelines. Finally, the proposed approach
is computationally light-weight and adds only a negligible increase to the
computational effort of pose estimation. | [
"cs.CV"
] |
In this paper we deal with the offline handwriting text recognition (HTR)
problem with reduced training datasets. Recent HTR solutions based on
artificial neural networks exhibit remarkable solutions in referenced
databases. These deep learning neural networks are composed of both
convolutional (CNN) and long short-term memory recurrent units (LSTM). In
addition, connectionist temporal classification (CTC) is the key to avoid
segmentation at character level, greatly facilitating the labeling task. One of
the main drawbacks of the CNNLSTM-CTC (CLC) solutions is that they need a
considerable part of the text to be transcribed for every type of calligraphy,
typically in the order of a few thousands of lines. Furthermore, in some
scenarios the text to transcribe is not that long, e.g. in the Washington
database. The CLC typically overfits for this reduced number of training
samples. Our proposal is based on the transfer learning (TL) from the
parameters learned with a bigger database. We first investigate, for a reduced
and fixed number of training samples, 350 lines, how the learning from a large
database, the IAM, can be transferred to the learning of the CLC of a reduced
database, Washington. We focus on which layers of the network could be not
re-trained. We conclude that the best solution is to re-train the whole CLC
parameters initialized to the values obtained after the training of the CLC
from the larger database. We also investigate results when the training size is
further reduced. The differences in the CER are more remarkable when training
with just 350 lines, a CER of 3.3% is achieved with TL while we have a CER of
18.2% when training from scratch. As a byproduct, the learning times are quite
reduced. Similar good results are obtained from the Parzival database when
trained with this reduced number of lines and this new approach. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
We address the problem of weakly-supervised semantic segmentation (WSSS)
using bounding box annotations. Although object bounding boxes are good
indicators to segment corresponding objects, they do not specify object
boundaries, making it hard to train convolutional neural networks (CNNs) for
semantic segmentation. We find that background regions are perceptually
consistent in part within an image, and this can be leveraged to discriminate
foreground and background regions inside object bounding boxes. To implement
this idea, we propose a novel pooling method, dubbed background-aware pooling
(BAP), that focuses more on aggregating foreground features inside the bounding
boxes using attention maps. This allows to extract high-quality pseudo
segmentation labels to train CNNs for semantic segmentation, but the labels
still contain noise especially at object boundaries. To address this problem,
we also introduce a noise-aware loss (NAL) that makes the networks less
susceptible to incorrect labels. Experimental results demonstrate that learning
with our pseudo labels already outperforms state-of-the-art weakly- and
semi-supervised methods on the PASCAL VOC 2012 dataset, and the NAL further
boosts the performance. | [
"cs.CV"
] |
Data augmentation (DA) techniques aim to increase data variability, and thus
train deep networks with better generalisation. The pioneering AutoAugment
automated the search for optimal DA policies with reinforcement learning.
However, AutoAugment is extremely computationally expensive, limiting its wide
applicability. Followup works such as Population Based Augmentation (PBA) and
Fast AutoAugment improved efficiency, but their optimization speed remains a
bottleneck. In this paper, we propose Differentiable Automatic Data
Augmentation (DADA) which dramatically reduces the cost. DADA relaxes the
discrete DA policy selection to a differentiable optimization problem via
Gumbel-Softmax. In addition, we introduce an unbiased gradient estimator,
RELAX, leading to an efficient and effective one-pass optimization strategy to
learn an efficient and accurate DA policy. We conduct extensive experiments on
CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. Furthermore, we demonstrate
the value of Auto DA in pre-training for downstream detection problems. Results
show our DADA is at least one order of magnitude faster than the
state-of-the-art while achieving very comparable accuracy. The code is
available at https://github.com/VDIGPKU/DADA. | [
"cs.CV",
"cs.LG"
] |
Convolutional neural network has recently achieved great success for image
restoration (IR) and also offered hierarchical features. However, most deep CNN
based IR models do not make full use of the hierarchical features from the
original low-quality images, thereby achieving relatively-low performance. In
this paper, we propose a novel residual dense network (RDN) to address this
problem in IR. We fully exploit the hierarchical features from all the
convolutional layers. Specifically, we propose residual dense block (RDB) to
extract abundant local features via densely connected convolutional layers. RDB
further allows direct connections from the state of preceding RDB to all the
layers of current RDB, leading to a contiguous memory mechanism. To adaptively
learn more effective features from preceding and current local features and
stabilize the training of wider network, we proposed local feature fusion in
RDB. After fully obtaining dense local features, we use global feature fusion
to jointly and adaptively learn global hierarchical features in a holistic way.
We demonstrate the effectiveness of RDN with several representative IR
applications, single image super-resolution, Gaussian image denoising, image
compression artifact reduction, and image deblurring. Experiments on benchmark
and real-world datasets show that our RDN achieves favorable performance
against state-of-the-art methods for each IR task quantitatively and visually. | [
"cs.CV"
] |
We present the first approach for 3D point-cloud to image translation based
on conditional Generative Adversarial Networks (cGAN). The model handles
multi-modal information sources from different domains, i.e. raw point-sets and
images. The generator is capable of processing three conditions, whereas the
point-cloud is encoded as raw point-set and camera projection. An image
background patch is used as constraint to bias environmental texturing. A
global approximation function within the generator is directly applied on the
point-cloud (Point-Net). Hence, the representative learning model incorporates
global 3D characteristics directly at the latent feature space. Conditions are
used to bias the background and the viewpoint of the generated image. This
opens up new ways in augmenting or texturing 3D data to aim the generation of
fully individual images. We successfully evaluated our method on the Kitti and
SunRGBD dataset with an outstanding object detection inception score. | [
"cs.CV"
] |
The categorical distribution is a natural representation of uncertainty in
multi-class segmentations. In the two-class case the categorical distribution
reduces to the Bernoulli distribution, for which grayscale morphology provides
a range of useful operations. In the general case, applying morphological
operations on uncertain multi-class segmentations is not straightforward as an
image of categorical distributions is not a complete lattice. Although
morphology on color images has received wide attention, this is not so for
color-coded or categorical images and even less so for images of categorical
distributions. In this work, we establish a set of requirements for morphology
on categorical distributions by combining classic morphology with a
probabilistic view. We then define operators respecting these requirements,
introduce protected operations on categorical distributions and illustrate the
utility of these operators on two example tasks: modeling annotator bias in
brain tumor segmentations and segmenting vesicle instances from the predictions
of a multi-class U-Net. | [
"cs.CV"
] |
A salient approach to interpretable machine learning is to restrict modeling
to simple models. In the Bayesian framework, this can be pursued by restricting
the model structure and prior to favor interpretable models. Fundamentally,
however, interpretability is about users' preferences, not the data generation
mechanism; it is more natural to formulate interpretability as a utility
function. In this work, we propose an interpretability utility, which
explicates the trade-off between explanation fidelity and interpretability in
the Bayesian framework. The method consists of two steps. First, a reference
model, possibly a black-box Bayesian predictive model which does not compromise
accuracy, is fitted to the training data. Second, a proxy model from an
interpretable model family that best mimics the predictive behaviour of the
reference model is found by optimizing the interpretability utility function.
The approach is model agnostic -- neither the interpretable model nor the
reference model are restricted to a certain class of models -- and the
optimization problem can be solved using standard tools. Through experiments on
real-word data sets, using decision trees as interpretable models and Bayesian
additive regression models as reference models, we show that for the same level
of interpretability, our approach generates more accurate models than the
alternative of restricting the prior. We also propose a systematic way to
measure stability of interpretabile models constructed by different
interpretability approaches and show that our proposed approach generates more
stable models. | [
"cs.LG",
"cs.AI",
"cs.HC",
"stat.ML"
] |
Individual mobility prediction is an essential task for transportation demand
management and traffic system operation. There exist a large body of works on
modeling location sequence and predicting the next location of users; however,
little attention is paid to the prediction of the next trip, which is governed
by the strong spatiotemporal dependencies between diverse attributes, including
trip start time $t$, origin $o$, and destination $d$. To fill this gap, in this
paper we propose a novel point process-based model -- Attentive Marked temporal
point processes (AMTPP) -- to model human mobility and predict the whole trip
$(t,o,d)$ in a joint manner. To encode the influence of history trips, AMTPP
employs the self-attention mechanism with a carefully designed positional
embedding to capture the daily/weekly periodicity and regularity in individual
travel behavior. Given the unique peaked nature of inter-event time in human
behavior, we use an asymmetric log-Laplace mixture distribution to precisely
model the distribution of trip start time $t$. Furthermore, an
origin-destination (OD) matrix learning block is developed to model the
relationship between every origin and destination pair. Experimental results on
two large metro trip datasets demonstrate the superior performance of AMTPP. | [
"cs.LG"
] |
In this paper, an automatic seeded region growing algorithm is proposed for
cellular image segmentation. First, the regions of interest (ROIs) extracted
from the preprocessed image. Second, the initial seeds are automatically
selected based on ROIs extracted from the image. Third, the most reprehensive
seeds are selected using a machine learning algorithm. Finally, the cellular
image is segmented into regions where each region corresponds to a seed. The
aim of the proposed is to automatically extract the Region of Interests (ROI)
from the cellular images in terms of overcoming the explosion, under
segmentation and over segmentation problems. Experimental results show that the
proposed algorithm can improve the segmented image and the segmented results
are less noisy as compared to some existing algorithms. | [
"cs.CV"
] |
Alzheimer's Dementia (AD) is an incurable, debilitating, and progressive
neurodegenerative condition that affects cognitive function. Early diagnosis is
important as therapeutics can delay progression and give those diagnosed vital
time. Developing models that analyse spontaneous speech could eventually
provide an efficient diagnostic modality for earlier diagnosis of AD. The
Alzheimer's Dementia Recognition through Spontaneous Speech task offers
acoustically pre-processed and balanced datasets for the classification and
prediction of AD and associated phenotypes through the modelling of spontaneous
speech. We exclusively analyse the supplied textual transcripts of the
spontaneous speech dataset, building and comparing performance across numerous
models for the classification of AD vs controls and the prediction of Mental
Mini State Exam scores. We rigorously train and evaluate Support Vector
Machines (SVMs), Gradient Boosting Decision Trees (GBDT), and Conditional
Random Fields (CRFs) alongside deep learning Transformer based models. We find
our top performing models to be a simple Term Frequency-Inverse Document
Frequency (TF-IDF) vectoriser as input into a SVM model and a pre-trained
Transformer based model `DistilBERT' when used as an embedding layer into
simple linear models. We demonstrate test set scores of 0.81-0.82 across
classification metrics and a RMSE of 4.58. | [
"cs.LG",
"cs.CL",
"cs.SD",
"eess.AS",
"stat.ML"
] |
Time series forecasting is essential for agents to make decisions in many
domains. Existing models rely on classical statistical methods to predict
future values based on previously observed numerical information. Yet,
practitioners often rely on visualizations such as charts and plots to reason
about their predictions. Inspired by the end-users, we re-imagine the topic by
creating a framework to produce visual forecasts, similar to the way humans
intuitively do. In this work, we take a novel approach by leveraging advances
in deep learning to extend the field of time series forecasting to a visual
setting. We do this by transforming the numerical analysis problem into the
computer vision domain. Using visualizations of time series data as input, we
train a convolutional autoencoder to produce corresponding visual forecasts. We
examine various synthetic and real datasets with diverse degrees of complexity.
Our experiments show that visual forecasting is effective for cyclic data but
somewhat less for irregular data such as stock price. Importantly, we find the
proposed visual forecasting method to outperform numerical baselines. We
attribute the success of the visual forecasting approach to the fact that we
convert the continuous numerical regression problem into a discrete domain with
quantization of the continuous target signal into pixel space. | [
"cs.CV",
"cs.LG",
"econ.EM"
] |
In recent years, Generative Adversarial Networks (GANs) have drawn a lot of
attentions for learning the underlying distribution of data in various
applications. Despite their wide applicability, training GANs is notoriously
difficult. This difficulty is due to the min-max nature of the resulting
optimization problem and the lack of proper tools of solving general
(non-convex, non-concave) min-max optimization problems. In this paper, we try
to alleviate this problem by proposing a new generative network that relies on
the use of random discriminators instead of adversarial design. This design
helps us to avoid the min-max formulation and leads to an optimization problem
that is stable and could be solved efficiently. The performance of the proposed
method is evaluated using handwritten digits (MNIST) and Fashion products
(Fashion-MNIST) data sets. While the resulting images are not as sharp as
adversarial training, the use of random discriminator leads to a much faster
algorithm as compared to the adversarial counterpart. This observation, at the
minimum, illustrates the potential of the random discriminator approach for
warm-start in training GANs. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
This paper addresses the problem of generating dense point clouds from given
sparse point clouds to model the underlying geometric structures of
objects/scenes. To tackle this challenging issue, we propose a novel end-to-end
learning-based framework. Specifically, by taking advantage of the linear
approximation theorem, we first formulate the problem explicitly, which boils
down to determining the interpolation weights and high-order approximation
errors. Then, we design a lightweight neural network to adaptively learn
unified and sorted interpolation weights as well as the high-order refinements,
by analyzing the local geometry of the input point cloud. The proposed method
can be interpreted by the explicit formulation, and thus is more
memory-efficient than existing ones. In sharp contrast to the existing methods
that work only for a pre-defined and fixed upsampling factor, the proposed
framework only requires a single neural network with one-time training to
handle various upsampling factors, which is highly desired in real-world
applications. In addition, we propose a simple yet effective training strategy
to drive such a flexible ability. In addition, our method can handle
non-uniformly distributed and noisy data well. Extensive experiments on both
synthetic and real-world data demonstrate the superiority of the proposed
method over state-of-the-art methods both quantitatively and qualitatively. | [
"cs.CV"
] |
Preparing and scanning histopathology slides consists of several steps, each
with a multitude of parameters. The parameters can vary between pathology labs
and within the same lab over time, resulting in significant variability of the
tissue appearance that hampers the generalization of automatic image analysis
methods. Typically, this is addressed with ad-hoc approaches such as staining
normalization that aim to reduce the appearance variability. In this paper, we
propose a systematic solution based on domain-adversarial neural networks. We
hypothesize that removing the domain information from the model representation
leads to better generalization. We tested our hypothesis for the problem of
mitosis detection in breast cancer histopathology images and made a comparative
analysis with two other approaches. We show that combining color augmentation
with domain-adversarial training is a better alternative than standard
approaches to improve the generalization of deep learning methods. | [
"cs.CV"
] |
We present a fully convolutional neural network (ConvNet), named RatLesNetv2,
for segmenting lesions in rodent magnetic resonance (MR) brain images.
RatLesNetv2 architecture resembles an autoencoder and it incorporates residual
blocks that facilitate its optimization. RatLesNetv2 is trained end to end on
three-dimensional images and it requires no preprocessing. We evaluated
RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat
brain MRI scans of 671 rats at nine different lesion stages that were used to
study focal cerebral ischemia for drug development. In addition, we compared
its performance with three other ConvNets specifically designed for medical
image segmentation. RatLesNetv2 obtained similar to higher Dice coefficient
values than the other ConvNets and it produced much more realistic and compact
segmentations with notably fewer holes and lower Hausdorff distance. The Dice
scores of RatLesNetv2 segmentations also exceeded inter-rater agreement of
manual segmentations. In conclusion, RatLesNetv2 could be used for automated
lesion segmentation, reducing human workload and improving reproducibility.
RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2. | [
"cs.CV",
"eess.IV"
] |
In this paper, we use the interaction inside adversarial perturbations to
explain and boost the adversarial transferability. We discover and prove the
negative correlation between the adversarial transferability and the
interaction inside adversarial perturbations. The negative correlation is
further verified through different DNNs with various inputs. Moreover, this
negative correlation can be regarded as a unified perspective to understand
current transferability-boosting methods. To this end, we prove that some
classic methods of enhancing the transferability essentially decease
interactions inside adversarial perturbations. Based on this, we propose to
directly penalize interactions during the attacking process, which
significantly improves the adversarial transferability. | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
Traditional generative adversarial networks (GAN) and many of its variants
are trained by minimizing the KL or JS-divergence loss that measures how close
the generated data distribution is from the true data distribution. A recent
advance called the WGAN based on Wasserstein distance can improve on the KL and
JS-divergence based GANs, and alleviate the gradient vanishing, instability,
and mode collapse issues that are common in the GAN training. In this work, we
aim at improving on the WGAN by first generalizing its discriminator loss to a
margin-based one, which leads to a better discriminator, and in turn a better
generator, and then carrying out a progressive training paradigm involving
multiple GANs to contribute to the maximum margin ranking loss so that the GAN
at later stages will improve upon early stages. We call this method Gang of
GANs (GoGAN). We have shown theoretically that the proposed GoGAN can reduce
the gap between the true data distribution and the generated data distribution
by at least half in an optimally trained WGAN. We have also proposed a new way
of measuring GAN quality which is based on image completion tasks. We have
evaluated our method on four visual datasets: CelebA, LSUN Bedroom, CIFAR-10,
and 50K-SSFF, and have seen both visual and quantitative improvement over
baseline WGAN. | [
"cs.CV",
"cs.LG"
] |
The fact that image datasets are often imbalanced poses an intense challenge
for deep learning techniques. In this paper, we propose a method to restore the
balance in imbalanced images, by coalescing two concurrent methods, generative
adversarial networks (GANs) and capsule network. In our model, generative and
discriminative networks play a novel competitive game, in which the generator
generates samples towards specific classes from multivariate probabilities
distribution. The discriminator of our model is designed in a way that while
recognizing the real and fake samples, it is also requires to assign classes to
the inputs. Since GAN approaches require fully observed data during training,
when the training samples are imbalanced, the approaches might generate similar
samples which leading to data overfitting. This problem is addressed by
providing all the available information from both the class components jointly
in the adversarial training. It improves learning from imbalanced data by
incorporating the majority distribution structure in the generation of new
minority samples. Furthermore, the generator is trained with feature matching
loss function to improve the training convergence. In addition, prevents
generation of outliers and does not affect majority class space. The
evaluations show the effectiveness of our proposed methodology; in particular,
the coalescing of capsule-GAN is effective at recognizing highly overlapping
classes with much fewer parameters compared with the convolutional-GAN. | [
"cs.LG",
"stat.ML"
] |
Low-light image enhancement aims to improve an image's visibility while
keeping its visual naturalness. Different from existing methods, which tend to
accomplish the enhancement task directly, we investigate the intrinsic
degradation and relight the low-light image while refining the details and
color in two steps. Inspired by the color image formulation (diffuse
illumination color plus environment illumination color), we first estimate the
degradation from low-light inputs to simulate the distortion of environment
illumination color, and then refine the content to recover the loss of diffuse
illumination color. To this end, we propose a novel Degradation-to-Refinement
Generation Network (DRGN). Its distinctive features can be summarized as 1) A
novel two-step generation network for degradation learning and content
refinement. It is not only superior to one-step methods, but also is capable of
synthesizing sufficient paired samples to benefit the model training; 2) A
multi-resolution fusion network to represent the target information
(degradation or contents) in a multi-scale cooperative manner, which is more
effective to address the complex unmixing problems. Extensive experiments on
both the enhancement task and the joint detection task have verified the
effectiveness and efficiency of our proposed method, surpassing the SOTA by
0.95dB in PSNR on LOL1000 dataset and 3.18\% in mAP on ExDark dataset. Our code
is available at \url{https://github.com/kuijiang0802/DRGN} | [
"cs.CV"
] |
Considering how congestion will propagate in the near future, understanding
traffic congestion propagation has become crucial in GPS navigation systems for
providing users with a more accurate estimated time of arrival (ETA). However,
providing the exact ETA during congestion is a challenge owing to the complex
propagation process between roads and high uncertainty regarding the future
behavior of the process. Recent studies have focused on finding frequent
congestion propagation patterns and determining the propagation probabilities.
By contrast, this study proposes a novel time delay estimation method for
traffic congestion propagation between roads using lag-specific transfer
entropy (TE). Nonlinear normalization with a sliding window is used to
effectively reveal the causal relationship between the source and target time
series in calculating the TE. Moreover, Markov bootstrap techniques were
adopted to quantify the uncertainty in the time delay estimator. To the best of
our knowledge, the time delay estimation method presented in this article is
the first to determine the time delay between roads for any congestion
propagation pattern. The proposed method was validated using simulated data as
well as real user trajectory data obtained from a major GPS navigation system
applied in South Korea. | [
"stat.ML",
"cs.LG",
"cs.SY",
"eess.SY"
] |
Diffusion maps are an emerging data-driven technique for non-linear
dimensionality reduction, which are especially useful for the analysis of
coherent structures and nonlinear embeddings of dynamical systems. However, the
computational complexity of the diffusion maps algorithm scales with the number
of observations. Thus, long time-series data presents a significant challenge
for fast and efficient embedding. We propose integrating the Nystr\"om method
with diffusion maps in order to ease the computational demand. We achieve a
speedup of roughly two to four times when approximating the dominant diffusion
map components. | [
"stat.ML",
"cs.LG"
] |
We present the design and implementation of a custom discrete optimization
technique for building rule lists over a categorical feature space. Our
algorithm produces rule lists with optimal training performance, according to
the regularized empirical risk, with a certificate of optimality. By leveraging
algorithmic bounds, efficient data structures, and computational reuse, we
achieve several orders of magnitude speedup in time and a massive reduction of
memory consumption. We demonstrate that our approach produces optimal rule
lists on practical problems in seconds. Our results indicate that it is
possible to construct optimal sparse rule lists that are approximately as
accurate as the COMPAS proprietary risk prediction tool on data from Broward
County, Florida, but that are completely interpretable. This framework is a
novel alternative to CART and other decision tree methods for interpretable
modeling. | [
"stat.ML",
"cs.LG"
] |
In real world settings, numerous constraints are present which are hard to
specify mathematically. However, for the real world deployment of reinforcement
learning (RL), it is critical that RL agents are aware of these constraints, so
that they can act safely. In this work, we consider the problem of learning
constraints from demonstrations of a constraint-abiding agent's behavior. We
experimentally validate our approach and show that our framework can
successfully learn the most likely constraints that the agent respects. We
further show that these learned constraints are \textit{transferable} to new
agents that may have different morphologies and/or reward functions. Previous
works in this regard have either mainly been restricted to tabular (discrete)
settings, specific types of constraints or assume the environment's transition
dynamics. In contrast, our framework is able to learn arbitrary
\textit{Markovian} constraints in high-dimensions in a completely model-free
setting. The code can be found it:
\url{https://github.com/shehryar-malik/icrl}. | [
"cs.LG",
"cs.RO",
"cs.SY",
"eess.SY"
] |
In this paper, we propose a novel approach for underwater image color
correction based on a Tikhonov type optimization model in the CIELAB color
space. It presents a new variational interpretation of the complementary
adaptation theory in psychophysics, which establishes the connection between
colorimetric notions and color constancy of the human visual system (HVS).
Understood as a long-term adaptive process, our method effectively removes the
underwater color cast and yields a balanced color distribution. For
visualization purposes, we enhance the image contrast by properly rescaling
both lightness and chroma without trespassing the CIELAB gamut. The magnitude
of the enhancement is hue-selective and image-based, thus our method is robust
for different underwater imaging environments. To improve the uniformity of
CIELAB, we include an approximate hue-linearization as the pre-processing and
an inverse transform of the Helmholtz-Kohlrausch effect as the post-processing.
We analyze and validate the proposed model by various numerical experiments.
Based on image quality metrics designed for underwater conditions, we compare
with some state-of-art approaches to show that the proposed method has
consistently superior performances. | [
"cs.CV",
"math.OC"
] |
Despite the success of generative adversarial networks (GANs) for image
generation, the trade-off between visual quality and image diversity remains a
significant issue. This paper achieves both aims simultaneously by improving
the stability of training GANs. The key idea of the proposed approach is to
implicitly regularize the discriminator using representative features. Focusing
on the fact that standard GAN minimizes reverse Kullback-Leibler (KL)
divergence, we transfer the representative feature, which is extracted from the
data distribution using a pre-trained autoencoder (AE), to the discriminator of
standard GANs. Because the AE learns to minimize forward KL divergence, our GAN
training with representative features is influenced by both reverse and forward
KL divergence. Consequently, the proposed approach is verified to improve
visual quality and diversity of state of the art GANs using extensive
evaluations. | [
"cs.CV"
] |
There is an emerging trend in the reinforcement learning for healthcare
literature. In order to prepare longitudinal, irregularly sampled, clinical
datasets for reinforcement learning algorithms, many researchers will resample
the time series data to short, regular intervals and use
last-observation-carried-forward (LOCF) imputation to fill in these gaps.
Typically, they will not maintain any explicit information about which values
were imputed. In this work, we (1) call attention to this practice and discuss
its potential implications; (2) propose an alternative representation of the
patient state that addresses some of these issues; and (3) demonstrate in a
novel but representative clinical dataset that our alternative representation
yields consistently better results for achieving optimal control, as measured
by off-policy policy evaluation, compared to representations that do not
incorporate missingness information. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Autonomous driving has attracted tremendous attention especially in the past
few years. The key techniques for a self-driving car include solving tasks like
3D map construction, self-localization, parsing the driving road and
understanding objects, which enable vehicles to reason and act. However, large
scale data set for training and system evaluation is still a bottleneck for
developing robust perception models. In this paper, we present the ApolloScape
dataset [1] and its applications for autonomous driving. Compared with existing
public datasets from real scenes, e.g. KITTI [2] or Cityscapes [3], ApolloScape
contains much large and richer labelling including holistic semantic dense
point cloud for each site, stereo, per-pixel semantic labelling, lanemark
labelling, instance segmentation, 3D car instance, high accurate location for
every frame in various driving videos from multiple sites, cities and daytimes.
For each task, it contains at lease 15x larger amount of images than SOTA
datasets. To label such a complete dataset, we develop various tools and
algorithms specified for each task to accelerate the labelling process, such as
3D-2D segment labeling tools, active labelling in videos etc. Depend on
ApolloScape, we are able to develop algorithms jointly consider the learning
and inference of multiple tasks. In this paper, we provide a sensor fusion
scheme integrating camera videos, consumer-grade motion sensors (GPS/IMU), and
a 3D semantic map in order to achieve robust self-localization and semantic
segmentation for autonomous driving. We show that practically, sensor fusion
and joint learning of multiple tasks are beneficial to achieve a more robust
and accurate system. We expect our dataset and proposed relevant algorithms can
support and motivate researchers for further development of multi-sensor fusion
and multi-task learning in the field of computer vision. | [
"cs.CV"
] |
Over the years, a number of biometric template protection schemes, primarily
based on the notion of "cancelable biometrics" (CB) have been proposed. An
ideal cancelable biometric algorithm possesses four criteria, i.e.,
irreversibility, revocability, unlinkability, and performance preservation.
Cancelable biometrics employed an irreversible but distance preserving
transform to convert the original biometric templates to the protected
templates. Matching in the transformed domain can be accomplished due to the
property of distance preservation. However, the distance preservation property
invites security issues, which are often neglected. In this paper, we analyzed
the property of distance preservation in cancelable biometrics, and
subsequently, a pre-image attack is launched to break the security of
cancelable biometrics under the Kerckhoffs's assumption, where the cancelable
biometrics algorithm and parameters are known to the attackers. Furthermore, we
proposed a framework based on mutual information to measure the information
leakage incurred by the distance preserving transform, and demonstrated that
information leakage is theoretically inevitable. The results examined on face,
iris, and fingerprint revealed that the risks origin from the matching score
computed from the distance/similarity of two cancelable templates jeopardize
the security of cancelable biometrics schemes greatly. At the end, we discussed
the security and accuracy trade-off and made recommendations against pre-image
attacks in order to design a secure biometric system. | [
"cs.CV"
] |
Convolutional networks have been the paradigm of choice in many computer
vision applications. The convolution operation however has a significant
weakness in that it only operates on a local neighborhood, thus missing global
information. Self-attention, on the other hand, has emerged as a recent advance
to capture long range interactions, but has mostly been applied to sequence
modeling and generative modeling tasks. In this paper, we consider the use of
self-attention for discriminative visual tasks as an alternative to
convolutions. We introduce a novel two-dimensional relative self-attention
mechanism that proves competitive in replacing convolutions as a stand-alone
computational primitive for image classification. We find in control
experiments that the best results are obtained when combining both convolutions
and self-attention. We therefore propose to augment convolutional operators
with this self-attention mechanism by concatenating convolutional feature maps
with a set of feature maps produced via self-attention. Extensive experiments
show that Attention Augmentation leads to consistent improvements in image
classification on ImageNet and object detection on COCO across many different
models and scales, including ResNets and a state-of-the art mobile constrained
network, while keeping the number of parameters similar. In particular, our
method achieves a $1.3\%$ top-1 accuracy improvement on ImageNet classification
over a ResNet50 baseline and outperforms other attention mechanisms for images
such as Squeeze-and-Excitation. It also achieves an improvement of 1.4 mAP in
COCO Object Detection on top of a RetinaNet baseline. | [
"cs.CV"
] |
Annotating histopathological images is a time-consuming andlabor-intensive
process, which requires broad-certificated pathologistscarefully examining
large-scale whole-slide images from cells to tissues.Recent frontiers of
transfer learning techniques have been widely investi-gated for image
understanding tasks with limited annotations. However,when applied for the
analytics of histology images, few of them can effec-tively avoid the
performance degradation caused by the domain discrep-ancy between the source
training dataset and the target dataset, suchas different tissues, staining
appearances, and imaging devices. To thisend, we present a novel method for the
unsupervised domain adaptationin histopathological image analysis, based on a
backbone for embeddinginput images into a feature space, and a graph neural
layer for propa-gating the supervision signals of images with labels. The graph
model isset up by connecting every image with its close neighbors in the
embed-ded feature space. Then graph neural network is employed to synthesizenew
feature representation from every image. During the training stage,target
samples with confident inferences are dynamically allocated withpseudo labels.
The cross-entropy loss function is used to constrain thepredictions of source
samples with manually marked labels and targetsamples with pseudo labels.
Furthermore, the maximum mean diversityis adopted to facilitate the extraction
of domain-invariant feature repre-sentations, and contrastive learning is
exploited to enhance the categorydiscrimination of learned features. In
experiments of the unsupervised do-main adaptation for histopathological image
classification, our methodachieves state-of-the-art performance on four public
datasets | [
"cs.CV"
] |
Satellite imagery has long been an attractive data source that provides a
wealth of information on human-inhabited areas. While super resolution
satellite images are rapidly becoming available, little study has focused on
how to extract meaningful information about human habitation patterns and
economic scales from such data. We present READ, a new approach for obtaining
essential spatial representation for any given district from high-resolution
satellite imagery based on deep neural networks. Our method combines transfer
learning and embedded statistics to efficiently learn critical spatial
characteristics of arbitrary size areas and represent them into a fixed-length
vector with minimal information loss. Even with a small set of labels, READ can
distinguish subtle differences between rural and urban areas and infer the
degree of urbanization. An extensive evaluation demonstrates the model
outperforms the state-of-the-art in predicting economic scales, such as
population density for South Korea (R^2=0.9617), and shows a high potential use
for developing countries where district-level economic scales are not known. | [
"cs.CV",
"cs.CY"
] |
Deep convolutional neural networks (CNNs) have shown outstanding performance
in the task of semantically segmenting images. However, applying the same
methods on 3D data still poses challenges due to the heavy memory requirements
and the lack of structured data. Here, we propose LatticeNet, a novel approach
for 3D semantic segmentation, which takes as input raw point clouds. A PointNet
describes the local geometry which we embed into a sparse permutohedral
lattice. The lattice allows for fast convolutions while keeping a low memory
footprint. Further, we introduce DeformSlice, a novel learned data-dependent
interpolation for projecting lattice features back onto the point cloud. We
present results of 3D segmentation on various datasets where our method
achieves state-of-the-art performance. | [
"cs.CV",
"cs.LG",
"eess.IV",
"stat.ML"
] |
The semantic segmentation of parts of objects in the wild is a challenging
task in which multiple instances of objects and multiple parts within those
objects must be detected in the scene. This problem remains nowadays very
marginally explored, despite its fundamental importance towards detailed object
understanding. In this work, we propose a novel framework combining higher
object-level context conditioning and part-level spatial relationships to
address the task. To tackle object-level ambiguity, a class-conditioning module
is introduced to retain class-level semantics when learning parts-level
semantics. In this way, mid-level features carry also this information prior to
the decoding stage. To tackle part-level ambiguity and localization we propose
a novel adjacency graph-based module that aims at matching the relative spatial
relationships between ground truth and predicted parts. The experimental
evaluation on the Pascal-Part dataset shows that we achieve state-of-the-art
results on this task. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
With the fast growing demand on new services and applications as well as the
increasing awareness of data protection, traditional centralized traffic
classification approaches are facing unprecedented challenges. This paper
introduces a novel framework, Federated Generative Adversarial Networks and
Automatic Classification (FGAN-AC), which integrates decentralized data
synthesizing with traffic classification. FGAN-AC is able to synthesize and
classify multiple types of service data traffic from decentralized local
datasets without requiring a large volume of manually labeled dataset or
causing any data leakage. Two types of data synthesizing approaches have been
proposed and compared: computation-efficient FGAN
(FGAN-\uppercase\expandafter{\romannumeral1}) and communication-efficient FGAN
(FGAN-\uppercase\expandafter{\romannumeral2}). The former only implements a
single CNN model for processing each local dataset and the later only requires
coordination of intermediate model training parameters. An automatic data
classification and model updating framework has been proposed to automatically
identify unknown traffic from the synthesized data samples and create new
pseudo-labels for model training. Numerical results show that our proposed
framework has the ability to synthesize highly mixed service data traffic and
can significantly improve the traffic classification performance compared to
existing solutions. | [
"cs.LG",
"cs.AI"
] |
Biphasic facial age translation aims at predicting the appearance of the
input face at any age. Facial age translation has received considerable
research attention in the last decade due to its practical value in cross-age
face recognition and various entertainment applications. However, most existing
methods model age changes between holistic images, regardless of the human face
structure and the age-changing patterns of individual facial components.
Consequently, the lack of semantic supervision will cause infidelity of
generated faces in detail. To this end, we propose a unified framework for
biphasic facial age translation with noisy-semantic guided generative
adversarial networks. Structurally, we project the class-aware noisy semantic
layouts to soft latent maps for the following injection operation on the
individual facial parts. In particular, we introduce two sub-networks,
ProjectionNet and ConstraintNet. ProjectionNet introduces the low-level
structural semantic information with noise map and produces soft latent maps.
ConstraintNet disentangles the high-level spatial features to constrain the
soft latent maps, which endows more age-related context into the soft latent
maps. Specifically, attention mechanism is employed in ConstraintNet for
feature disentanglement. Meanwhile, in order to mine the strongest mapping
ability of the network, we embed two types of learning strategies in the
training procedure, supervised self-driven generation and unsupervised
condition-driven cycle-consistent generation. As a result, extensive
experiments conducted on MORPH and CACD datasets demonstrate the prominent
ability of our proposed method which achieves state-of-the-art performance. | [
"cs.CV",
"cs.AI"
] |
This paper presents a PINN training framework that employs (1) pre-training
steps that accelerates and improve the robustness of the training of
physics-informed neural network with auxiliary data stored in point clouds, (2)
a net-to-net knowledge transfer algorithm that improves the weight
initialization of the neural network and (3) a multi-objective optimization
algorithm that may improve the performance of a physical-informed neural
network with competing constraints. We consider the training and transfer and
multi-task learning of physics-informed neural network (PINN) as
multi-objective problems where the physics constraints such as the governing
equation, boundary conditions, thermodynamic inequality, symmetry, and
invariant properties, as well as point cloud used for pre-training can
sometimes lead to conflicts and necessitating the seek of the Pareto optimal
solution. In these situations, weighted norms commonly used to handle multiple
constraints may lead to poor performance, while other multi-objective
algorithms may scale poorly with increasing dimensionality. To overcome this
technical barrier, we adopt the concept of vectorized objective function and
modify a gradient descent approach to handle the issue of conflicting
gradients. Numerical experiments are compared the benchmark boundary value
problems solved via PINN. The performance of the proposed paradigm is compared
against the classical equal-weighted norm approach. Our numerical experiments
indicate that the brittleness and lack of robustness demonstrated in some PINN
implementations can be overcome with the proposed strategy. | [
"cs.LG"
] |
Surveillance scenarios are prone to several problems since they usually
involve low-resolution footage, and there is no control of how far the subjects
may be from the camera in the first place. This situation is suitable for the
application of upsampling (super-resolution) algorithms since they may be able
to recover the discriminant properties of the subjects involved. While general
super-resolution approaches were proposed to enhance image quality for
human-level perception, biometrics super-resolution methods seek the best
"computer perception" version of the image since their focus is on improving
automatic recognition performance. Convolutional neural networks and deep
learning algorithms, in general, have been applied to computer vision tasks and
are now state-of-the-art for several sub-domains, including image
classification, restoration, and super-resolution. However, no work has
evaluated the effects that the latest proposed super-resolution methods may
have upon the accuracy and face verification performance in low-resolution
"in-the-wild" data. This project aimed at evaluating and adapting different
deep neural network architectures for the task of face super-resolution driven
by face recognition performance in real-world low-resolution images. The
experimental results in a real-world surveillance and attendance datasets
showed that general super-resolution architectures might enhance face
verification performance of deep neural networks trained on high-resolution
faces. Also, since neural networks are function approximators and can be
trained based on specific objective functions, the use of a customized loss
function optimized for feature extraction showed promising results for
recovering discriminant features in low-resolution face images. | [
"cs.CV",
"cs.AI",
"I.4.0; I.4.9"
] |
A driver's gaze is critical for determining their attention, state,
situational awareness, and readiness to take over control from partially
automated vehicles. Estimating the gaze direction is the most obvious way to
gauge a driver's state under ideal conditions when limited to using
non-intrusive imaging sensors. Unfortunately, the vehicular environment
introduces a variety of challenges that are usually unaccounted for - harsh
illumination, nighttime conditions, and reflective eyeglasses. Relying on head
pose alone under such conditions can prove to be unreliable and erroneous. In
this study, we offer solutions to address these problems encountered in the
real world. To solve issues with lighting, we demonstrate that using an
infrared camera with suitable equalization and normalization suffices. To
handle eyeglasses and their corresponding artifacts, we adopt image-to-image
translation using generative adversarial networks to pre-process images prior
to gaze estimation. Our proposed Gaze Preserving CycleGAN (GPCycleGAN) is
trained to preserve the driver's gaze while removing potential eyeglasses from
face images. GPCycleGAN is based on the well-known CycleGAN approach - with the
addition of a gaze classifier and a gaze consistency loss for additional
supervision. Our approach exhibits improved performance, interpretability,
robustness and superior qualitative results on challenging real-world datasets. | [
"cs.CV",
"cs.LG"
] |
Bayesian deep neural networks (DNNs) can provide a mathematically grounded
framework to quantify uncertainty in predictions from image captioning models.
We propose a Bayesian variant of policy-gradient based reinforcement learning
training technique for image captioning models to directly optimize
non-differentiable image captioning quality metrics such as CIDEr-D. We extend
the well-known Self-Critical Sequence Training (SCST) approach for image
captioning models by incorporating Bayesian inference, and refer to it as
B-SCST. The "baseline" for the policy-gradients in B-SCST is generated by
averaging predictive quality metrics (CIDEr-D) of the captions drawn from the
distribution obtained using a Bayesian DNN model. We infer this predictive
distribution using Monte Carlo (MC) dropout approximate variational inference.
We show that B-SCST improves CIDEr-D scores on Flickr30k, MS COCO and VizWiz
image captioning datasets, compared to the SCST approach. We also provide a
study of uncertainty quantification for the predicted captions, and demonstrate
that it correlates well with the CIDEr-D scores. To our knowledge, this is the
first such analysis, and it can improve the interpretability of image
captioning model outputs, which is critical for practical applications. | [
"cs.LG",
"stat.ML"
] |
Rapid advances of hardware-based technologies during the past decades have
opened up new possibilities for Life scientists to gather multimodal data in
various application domains (e.g., Omics, Bioimaging, Medical Imaging, and
[Brain/Body]-Machine Interfaces), thus generating novel opportunities for
development of dedicated data intensive machine learning techniques. Overall,
recent research in Deep learning (DL), Reinforcement learning (RL), and their
combination (Deep RL) promise to revolutionize Artificial Intelligence. The
growth in computational power accompanied by faster and increased data storage
and declining computing costs have already allowed scientists in various fields
to apply these techniques on datasets that were previously intractable for
their size and complexity. This review article provides a comprehensive survey
on the application of DL, RL, and Deep RL techniques in mining Biological data.
In addition, we compare performances of DL techniques when applied to different
datasets across various application domains. Finally, we outline open issues in
this challenging research area and discuss future development perspectives. | [
"cs.LG",
"stat.ML",
"A.1, I.2, I.5, J.3"
] |
Stochastic gradient descent (SGD) undergoes complicated multiplicative noise
for the mean-square loss. We use this property of the SGD noise to derive a
stochastic differential equation (SDE) with simpler additive noise by
performing a non-uniform transformation of the time variable. In the SDE, the
gradient of the loss is replaced by that of the logarithmized loss.
Consequently, we show that, near a local or global minimum, the stationary
distribution $P_\mathrm{ss}(\theta)$ of the network parameters $\theta$ follows
a power-law with respect to the loss function $L(\theta)$, i.e.
$P_\mathrm{ss}(\theta)\propto L(\theta)^{-\phi}$ with the exponent $\phi$
specified by the mini-batch size, the learning rate, and the Hessian at the
minimum. We obtain the escape rate formula from a local minimum, which is
determined not by the loss barrier height $\Delta L=L(\theta^s)-L(\theta^*)$
between a minimum $\theta^*$ and a saddle $\theta^s$ but by the logarithmized
loss barrier height $\Delta\log L=\log[L(\theta^s)/L(\theta^*)]$. Our
escape-rate formula explains an empirical fact that SGD prefers flat minima
with low effective dimensions. | [
"cs.LG",
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"stat.ML"
] |
Time series with missing data are signals encountered in important settings
for machine learning. Some of the most successful prior approaches for modeling
such time series are based on recurrent neural networks that transform the
input and previous state to account for the missing observations, and then
treat the transformed signal in a standard manner.
In this paper, we introduce a single unifying framework, Recursive Input and
State Estimation (RISE), for this general approach and reformulate existing
models as specific instances of this framework. We then explore additional
novel variations within the RISE framework to improve the performance of any
instance. We exploit representation learning techniques to learn latent
representations of the signals used by RISE instances. We discuss and develop
various encoding techniques to learn latent signal representations. We
benchmark instances of the framework with various encoding functions on three
data imputation datasets, observing that RISE instances always benefit from
encoders that learn representations for numerical values from the digits into
which they can be decomposed. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
In recent years, we have seen tremendous progress in the field of object
detection. Most of the recent improvements have been achieved by targeting
deeper feedforward networks. However, many hard object categories such as
bottle, remote, etc. require representation of fine details and not just
coarse, semantic representations. But most of these fine details are lost in
the early convolutional layers. What we need is a way to incorporate finer
details from lower layers into the detection architecture. Skip connections
have been proposed to combine high-level and low-level features, but we argue
that selecting the right features from low-level requires top-down contextual
information. Inspired by the human visual pathway, in this paper we propose
top-down modulations as a way to incorporate fine details into the detection
framework. Our approach supplements the standard bottom-up, feedforward ConvNet
with a top-down modulation (TDM) network, connected using lateral connections.
These connections are responsible for the modulation of lower layer filters,
and the top-down network handles the selection and integration of contextual
information and low-level features. The proposed TDM architecture provides a
significant boost on the COCO testdev benchmark, achieving 28.6 AP for VGG16,
35.2 AP for ResNet101, and 37.3 for InceptionResNetv2 network, without any
bells and whistles (e.g., multi-scale, iterative box refinement, etc.). | [
"cs.CV",
"cs.LG"
] |
Temporal graph signals are multivariate time series with individual
components associated with nodes of a fixed graph structure. Data of this kind
arises in many domains including activity of social network users, sensor
network readings over time, and time course gene expression within the
interaction network of a model organism. Traditional matrix decomposition
methods applied to such data fall short of exploiting structural regularities
encoded in the underlying graph and also in the temporal patterns of the
signal. How can we take into account such structure to obtain a succinct and
interpretable representation of temporal graph signals?
We propose a general, dictionary-based framework for temporal graph signal
decomposition (TGSD). The key idea is to learn a low-rank, joint encoding of
the data via a combination of graph and time dictionaries. We propose a highly
scalable decomposition algorithm for both complete and incomplete data, and
demonstrate its advantage for matrix decomposition, imputation of missing
values, temporal interpolation, clustering, period estimation, and rank
estimation in synthetic and real-world data ranging from traffic patterns to
social media activity. Our framework achieves 28% reduction in RMSE compared to
baselines for temporal interpolation when as many as 75% of the observations
are missing. It scales best among baselines taking under 20 seconds on 3.5
million data points and produces the most parsimonious models. To the best of
our knowledge, TGSD is the first framework to jointly model graph signals by
temporal and graph dictionaries. | [
"cs.LG"
] |
Analyzing big geophysical observational data collected by multiple advanced
sensors on various satellite platforms promotes our understanding of the
geophysical system. For instance, convolutional neural networks (CNN) have
achieved great success in estimating tropical cyclone (TC) intensity based on
satellite data with fixed temporal frequency (e.g., 3 h). However, to achieve
more timely (under 30 min) and accurate TC intensity estimates, a deep learning
model is demanded to handle temporally-heterogeneous satellite observations.
Specifically, infrared (IR1) and water vapor (WV) images are available under
every 15 minutes, while passive microwave rain rate (PMW) is available for
about every 3 hours. Meanwhile, the visible (VIS) channel is severely affected
by noise and sunlight intensity, making it difficult to be utilized. Therefore,
we propose a novel framework that combines generative adversarial network (GAN)
with CNN. The model utilizes all data, including VIS and PMW information,
during the training phase and eventually uses only the high-frequent IR1 and WV
data for providing intensity estimates during the predicting phase.
Experimental results demonstrate that the hybrid GAN-CNN framework achieves
comparable precision to the state-of-the-art models, while possessing the
capability of increasing the maximum estimation frequency from 3 hours to less
than 15 minutes. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Most current computer vision datasets are composed of disconnected sets, such
as images from different classes. We prove that distributions of this type of
data cannot be represented with a continuous generative network without error.
They can be represented in two ways: With an ensemble of networks or with a
single network with truncated latent space. We show that ensembles are more
desirable than truncated distributions in practice. We construct a regularized
optimization problem that establishes the relationship between a single
continuous GAN, an ensemble of GANs, conditional GANs, and Gaussian Mixture
GANs. This regularization can be computed efficiently, and we show empirically
that our framework has a performance sweet spot which can be found with
hyperparameter tuning. This ensemble framework allows better performance than a
single continuous GAN or cGAN while maintaining fewer total parameters. | [
"cs.LG",
"stat.ML"
] |
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. Artifacts such as motion blur, bubbles,
specular reflections, floating objects and pixel saturation impede the visual
interpretation and the automated analysis of endoscopy videos. Given the
widespread use of endoscopy in different clinical applications, we contend that
the robust and reliable identification of such artifacts and the automated
restoration of corrupted video frames is a fundamental medical imaging problem.
Existing state-of-the-art methods only deal with the detection and restoration
of selected artifacts. However, typically endoscopy videos contain numerous
artifacts which motivates to establish a comprehensive solution.
We propose a fully automatic framework that can: 1) detect and classify six
different primary artifacts, 2) provide a quality score for each frame and 3)
restore mildly corrupted frames. To detect different artifacts our framework
exploits fast multi-scale, single stage convolutional neural network detector.
We introduce a quality metric to assess frame quality and predict image
restoration success. Generative adversarial networks with carefully chosen
regularization are finally used to restore corrupted frames.
Our detector yields the highest mean average precision (mAP at 5% threshold)
of 49.0 and the lowest computational time of 88 ms allowing for accurate
real-time processing. Our restoration models for blind deblurring, saturation
correction and inpainting demonstrate significant improvements over previous
methods. On a set of 10 test videos we show that our approach preserves an
average of 68.7% which is 25% more frames than that retained from the raw
videos. | [
"cs.CV",
"cs.AI"
] |
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