text
stringlengths 29
3.31k
| label
sequencelengths 1
11
|
|---|---|
This paper presents a new method to synthesize an image from arbitrary views
and times given a collection of images of a dynamic scene. A key challenge for
the novel view synthesis arises from dynamic scene reconstruction where
epipolar geometry does not apply to the local motion of dynamic contents. To
address this challenge, we propose to combine the depth from single view (DSV)
and the depth from multi-view stereo (DMV), where DSV is complete, i.e., a
depth is assigned to every pixel, yet view-variant in its scale, while DMV is
view-invariant yet incomplete. Our insight is that although its scale and
quality are inconsistent with other views, the depth estimation from a single
view can be used to reason about the globally coherent geometry of dynamic
contents. We cast this problem as learning to correct the scale of DSV, and to
refine each depth with locally consistent motions between views to form a
coherent depth estimation. We integrate these tasks into a depth fusion network
in a self-supervised fashion. Given the fused depth maps, we synthesize a
photorealistic virtual view in a specific location and time with our deep
blending network that completes the scene and renders the virtual view. We
evaluate our method of depth estimation and view synthesis on diverse
real-world dynamic scenes and show the outstanding performance over existing
methods. | [
"cs.CV"
] |
Neural architecture search (NAS) with an accuracy predictor that predicts the
accuracy of candidate architectures has drawn increasing attention due to its
simplicity and effectiveness. Previous works usually employ neural
network-based predictors which require more delicate design and are easy to
overfit. Considering that most architectures are represented as sequences of
discrete symbols which are more like tabular data and preferred by non-neural
predictors, in this paper, we study an alternative approach which uses
non-neural model for accuracy prediction. Specifically, as decision tree based
models can better handle tabular data, we leverage gradient boosting decision
tree (GBDT) as the predictor for NAS. We demonstrate that the GBDT predictor
can achieve comparable (if not better) prediction accuracy than neural network
based predictors. Moreover, considering that a compact search space can ease
the search process, we propose to prune the search space gradually according to
important features derived from GBDT. In this way, NAS can be performed by
first pruning the search space and then searching a neural architecture, which
is more efficient and effective. Experiments on NASBench-101 and ImageNet
demonstrate the effectiveness of using GBDT as predictor for NAS: (1) On
NASBench-101, it is 22x, 8x, and 6x more sample efficient than random search,
regularized evolution, and Monte Carlo Tree Search (MCTS) in finding the global
optimum; (2) It achieves 24.2% top-1 error rate on ImageNet, and further
achieves 23.4% top-1 error rate on ImageNet when enhanced with search space
pruning. Code is provided at https://github.com/renqianluo/GBDT-NAS. | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] |
Attribution maps have gained popularity as tools for explaining neural
networks predictions. By assigning an importance value to each input dimension
that represents their influence towards the outcome, they give an intuitive
explanation of the decision process. However, recent work has discovered
vulnerability of these maps to imperceptible, carefully crafted changes in the
input that lead to significantly different attributions, rendering them
meaningless. By borrowing notions of traditional adversarial training - a
method to achieve robust predictions - we propose a novel framework for
attributional robustness (FAR) to mitigate this vulnerability. Central
assumption is that similar inputs should yield similar attribution maps, while
keeping the prediction of the network constant. Specifically, we define a new
generic regularization term and training objective that minimizes the maximal
dissimilarity of attribution maps in a local neighbourhood of the input. We
then show how current state-of-the-art methods can be recovered through
principled instantiations of these objectives. Moreover, we propose two new
training methods, AAT and AdvAAT, derived from the framework, that directly
optimize for robust attributions and predictions. We showcase the effectivity
of our training methods by comparing them to current state-of-the-art
attributional robustness approaches on widely used vision datasets. Experiments
show that they perform better or comparably to current methods in terms of
attributional robustness, while being applicable to any attribution method and
input data domain. We finally show that our methods mitigate undesired
dependencies of attributional robustness and some training and estimation
parameters, which seem to critically affect other methods. | [
"cs.LG"
] |
Purpose: Colorectal cancer (CRC) is the second most common cause of cancer
mortality worldwide. Colonoscopy is a widely used technique for colon screening
and polyp lesions diagnosis. Nevertheless, manual screening using colonoscopy
suffers from a substantial miss rate of polyps and is an overwhelming burden
for endoscopists. Computer-aided diagnosis (CAD) for polyp detection has the
potential to reduce human error and human burden. However, current polyp
detection methods based on object detection framework need many handcrafted
pre-processing and post-processing operations or user guidance that require
domain-specific knowledge.
Methods: In this paper, we propose a convolution in transformer (COTR)
network for end-to-end polyp detection. Motivated by the detection transformer
(DETR), COTR is constituted by a CNN for feature extraction, transformer
encoder layers interleaved with convolutional layers for feature encoding and
recalibration, transformer decoder layers for object querying, and a
feed-forward network for detection prediction. Considering the slow convergence
of DETR, COTR embeds convolution layers into transformer encoder for feature
reconstruction and convergence acceleration.
Results: Experimental results on two public polyp datasets show that COTR
achieved 91.49\% precision, 82.69% sensitivity, and 86.87% F1-score on the
ETIS-LARIB, and 91.67% precision, 93.54% sensitivity, and 92.60% F1-score on
the CVC-ColonDB.
Conclusion: This study proposed an end to end detection method based on
detection transformer for colorectal polyp detection. Experimental results on
ETIS-LARIB and CVC-ColonDB dataset demonstrated that the proposed model
achieved comparable performance against state-of-the-art methods. | [
"cs.CV"
] |
Drowsiness driving is a major cause of traffic accidents and thus numerous
previous researches have focused on driver drowsiness detection. Many drive
relevant factors have been taken into consideration for fatigue detection and
can lead to high precision, but there are still several serious constraints,
such as most existing models are environmentally susceptible. In this paper,
fatigue detection is considered as temporal action detection problem instead of
image classification. The proposed detection system can be divided into four
parts: (1) Localize the key patches of the detected driver picture which are
critical for fatigue detection and calculate the corresponding optical flow.
(2) Contrast Limited Adaptive Histogram Equalization (CLAHE) is used in our
system to reduce the impact of different light conditions. (3) Three individual
two-stream networks combined with attention mechanism are designed for each
feature to extract temporal information. (4) The outputs of the three
sub-networks will be concatenated and sent to the fully-connected network,
which judges the status of the driver. The drowsiness detection system is
trained and evaluated on the famous Nation Tsing Hua University Driver
Drowsiness Detection (NTHU-DDD) dataset and we obtain an accuracy of 94.46%,
which outperforms most existing fatigue detection models. | [
"cs.CV"
] |
We consider the problem of predicting the future path of a pedestrian using
its motion history and the motion history of the surrounding pedestrians,
called social information. Since the seminal paper on Social-LSTM,
deep-learning has become the main tool used to model the impact of social
interactions on a pedestrian's motion. The demonstration that these models can
learn social interactions relies on an ablative study of these models. The
models are compared with and without their social interactions module on two
standard metrics, the Average Displacement Error and Final Displacement Error.
Yet, these complex models were recently outperformed by a simple
constant-velocity approach. This questions if they actually allow to model
social interactions as well as the validity of the proof. In this paper, we
focus on the deep-learning models with a soft-attention mechanism for social
interaction modeling and study whether they use social information at
prediction time. We conduct two experiments across four state-of-the-art
approaches on the ETH and UCY datasets, which were also used in previous work.
First, the models are trained by replacing the social information with random
noise and compared to model trained with actual social information. Second, we
use a gating mechanism along with a $L_0$ penalty, allowing models to shut down
their inner components. The models consistently learn to prune their
soft-attention mechanism. For both experiments, neither the course of the
convergence nor the prediction performance were altered. This demonstrates that
the soft-attention mechanism and therefore the social information are ignored
by the models. | [
"cs.CV",
"cs.LG"
] |
Transfer learning has recently attracted significant research attention, as
it simultaneously learns from different source domains, which have plenty of
labeled data, and transfers the relevant knowledge to the target domain with
limited labeled data to improve the prediction performance. We propose a
Bayesian transfer learning framework where the source and target domains are
related through the joint prior density of the model parameters. The modeling
of joint prior densities enables better understanding of the "transferability"
between domains. We define a joint Wishart density for the precision matrices
of the Gaussian feature-label distributions in the source and target domains to
act like a bridge that transfers the useful information of the source domain to
help classification in the target domain by improving the target posteriors.
Using several theorems in multivariate statistics, the posteriors and posterior
predictive densities are derived in closed forms with hypergeometric functions
of matrix argument, leading to our novel closed-form and fast Optimal Bayesian
Transfer Learning (OBTL) classifier. Experimental results on both synthetic and
real-world benchmark data confirm the superb performance of the OBTL compared
to the other state-of-the-art transfer learning and domain adaptation methods. | [
"stat.ML",
"cs.CV",
"cs.LG"
] |
Estimating the predictive uncertainty of a Bayesian learning model is
critical in various decision-making problems, e.g., reinforcement learning,
detecting adversarial attack, self-driving car. As the model posterior is
almost always intractable, most efforts were made on finding an accurate
approximation the true posterior. Even though a decent estimation of the model
posterior is obtained, another approximation is required to compute the
predictive distribution over the desired output. A common accurate solution is
to use Monte Carlo (MC) integration. However, it needs to maintain a large
number of samples, evaluate the model repeatedly and average multiple model
outputs. In many real-world cases, this is computationally prohibitive. In this
work, assuming that the exact posterior or a decent approximation is obtained,
we propose a generic framework to approximate the output probability
distribution induced by model posterior with a parameterized model and in an
amortized fashion. The aim is to approximate the true uncertainty of a specific
Bayesian model, meanwhile alleviating the heavy workload of MC integration at
testing time. The proposed method is universally applicable to Bayesian
classification models that allow for posterior sampling. Theoretically, we show
that the idea of amortization incurs no additional costs on approximation
performance. Empirical results validate the strong practical performance of our
approach. | [
"cs.LG",
"stat.ML"
] |
Point-cloud is an efficient way to represent 3D world. Analysis of
point-cloud deals with understanding the underlying 3D geometric structure. But
due to the lack of smooth topology, and hence the lack of neighborhood
structure, standard correlation can not be directly applied on point-cloud. One
of the popular approaches to do point correlation is to partition the
point-cloud into voxels and extract features using standard 3D correlation. But
this approach suffers from sparsity of point-cloud and hence results in
multiple empty voxels. One possible solution to deal with this problem is to
learn a MLP to map a point or its local neighborhood to a high dimensional
feature space. All these methods suffer from a large number of parameters
requirement and are susceptible to random rotations. A popular way to make the
model "invariant" to rotations is to use data augmentation techniques with
small rotations but the potential drawback includes \item more training samples
\item susceptible to large rotations. In this work, we develop a rotation
invariant point-cloud segmentation and classification scheme based on the
omni-directional camera model (dubbed as {\bf POIRot$^1$}). Our proposed model
is rotationally invariant and can preserve geometric shape of a 3D point-cloud.
Because of the inherent rotation invariant property, our proposed framework
requires fewer number of parameters (please see \cite{Iandola2017SqueezeNetAA}
and the references therein for motivation of lean models). Several experiments
have been performed to show that our proposed method can beat the
state-of-the-art algorithms in classification and part segmentation
applications. | [
"cs.CV"
] |
We study multi-task reinforcement learning (RL) in tabular episodic Markov
decision processes (MDPs). We formulate a heterogeneous multi-player RL
problem, in which a group of players concurrently face similar but not
necessarily identical MDPs, with a goal of improving their collective
performance through inter-player information sharing. We design and analyze an
algorithm based on the idea of model transfer, and provide gap-dependent and
gap-independent upper and lower bounds that characterize the intrinsic
complexity of the problem. | [
"cs.LG"
] |
The transformer networks are particularly good at modeling long-range
dependencies within a long sequence. In this paper, we conduct research on
applying the transformer networks for salient object detection (SOD). We adopt
the dense transformer backbone for fully supervised RGB image based SOD, RGB-D
image pair based SOD, and weakly supervised SOD within a unified framework
based on the observation that the transformer backbone can provide accurate
structure modeling, which makes it powerful in learning from weak labels with
less structure information. Further, we find that the vision transformer
architectures do not offer direct spatial supervision, instead encoding
position as a feature. Therefore, we investigate the contributions of two
strategies to provide stronger spatial supervision through the transformer
layers within our unified framework, namely deep supervision and
difficulty-aware learning. We find that deep supervision can get gradients back
into the higher level features, thus leads to uniform activation within the
same semantic object. Difficulty-aware learning on the other hand is capable of
identifying the hard pixels for effective hard negative mining. We also
visualize features of conventional backbone and transformer backbone before and
after fine-tuning them for SOD, and find that transformer backbone encodes more
accurate object structure information and more distinct semantic information
within the lower and higher level features respectively. We also apply our
model to camouflaged object detection (COD) and achieve similar observations as
the above three SOD tasks. Extensive experimental results on various SOD and
COD tasks illustrate that transformer networks can transform SOD and COD,
leading to new benchmarks for each related task. The source code and
experimental results are available via our project page:
https://github.com/fupiao1998/TrasformerSOD. | [
"cs.CV"
] |
As the availability and importance of temporal interaction data--such as
email communication--increases, it becomes increasingly important to understand
the underlying structure that underpins these interactions. Often these
interactions form a multigraph, where we might have multiple interactions
between two entities. Such multigraphs tend to be sparse yet structured, and
their distribution often evolves over time. Existing statistical models with
interpretable parameters can capture some, but not all, of these properties. We
propose a dynamic nonparametric model for interaction multigraphs that combines
the sparsity of edge-exchangeable multigraphs with dynamic clustering patterns
that tend to reinforce recent behavioral patterns. We show that our method
yields improved held-out likelihood over stationary variants, and impressive
predictive performance against a range of state-of-the-art dynamic graph
models. | [
"cs.LG",
"stat.ME",
"stat.ML"
] |
Access to large, diverse RGB-D datasets is critical for training RGB-D scene
understanding algorithms. However, existing datasets still cover only a limited
number of views or a restricted scale of spaces. In this paper, we introduce
Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views
from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided
with surface reconstructions, camera poses, and 2D and 3D semantic
segmentations. The precise global alignment and comprehensive, diverse
panoramic set of views over entire buildings enable a variety of supervised and
self-supervised computer vision tasks, including keypoint matching, view
overlap prediction, normal prediction from color, semantic segmentation, and
region classification. | [
"cs.CV"
] |
In this work, we have developed a robust lane detection and departure warning
technique. Our system is based on single camera sensor. For lane detection a
modified Inverse Perspective Mapping using only a few extrinsic camera
parameters and illuminant Invariant techniques is used. Lane markings are
represented using a combination of 2nd and 4th order steerable filters, robust
to shadowing. Effect of shadowing and extra sun light are removed using Lab
color space, and illuminant invariant representation. Lanes are assumed to be
cubic curves and fitted using robust RANSAC. This method can reliably detect
lanes of the road and its boundary. This method has been experimented in Indian
road conditions under different challenging situations and the result obtained
were very good. For lane departure angle an optical flow based method were
used. | [
"cs.CV",
"68T45"
] |
In this paper we present a novel radar-camera sensor fusion framework for
accurate object detection and distance estimation in autonomous driving
scenarios. The proposed architecture uses a middle-fusion approach to fuse the
radar point clouds and RGB images. Our radar object proposal network uses radar
point clouds to generate 3D proposals from a set of 3D prior boxes. These
proposals are mapped to the image and fed into a Radar Proposal Refinement
(RPR) network for objectness score prediction and box refinement. The RPR
network utilizes both radar information and image feature maps to generate
accurate object proposals and distance estimations. The radar-based proposals
are combined with image-based proposals generated by a modified Region Proposal
Network (RPN). The RPN has a distance regression layer for estimating distance
for every generated proposal. The radar-based and image-based proposals are
merged and used in the next stage for object classification. Experiments on the
challenging nuScenes dataset show our method outperforms other existing
radar-camera fusion methods in the 2D object detection task while at the same
time accurately estimates objects' distances. | [
"cs.CV"
] |
Deep learning performs remarkably well on many time series analysis tasks
recently. The superior performance of deep neural networks relies heavily on a
large number of training data to avoid overfitting. However, the labeled data
of many real-world time series applications may be limited such as
classification in medical time series and anomaly detection in AIOps. As an
effective way to enhance the size and quality of the training data, data
augmentation is crucial to the successful application of deep learning models
on time series data. In this paper, we systematically review different data
augmentation methods for time series. We propose a taxonomy for the reviewed
methods, and then provide a structured review for these methods by highlighting
their strengths and limitations. We also empirically compare different data
augmentation methods for different tasks including time series classification,
anomaly detection, and forecasting. Finally, we discuss and highlight five
future directions to provide useful research guidance. | [
"cs.LG",
"eess.SP",
"stat.ML"
] |
Deep learning technique has yielded significant improvements in point cloud
completion with the aim of completing missing object shapes from partial
inputs. However, most existing methods fail to recover realistic structures due
to over-smoothing of fine-grained details. In this paper, we develop a
voxel-based network for point cloud completion by leveraging edge generation
(VE-PCN). We first embed point clouds into regular voxel grids, and then
generate complete objects with the help of the hallucinated shape edges. This
decoupled architecture together with a multi-scale grid feature learning is
able to generate more realistic on-surface details. We evaluate our model on
the publicly available completion datasets and show that it outperforms
existing state-of-the-art approaches quantitatively and qualitatively. Our
source code is available at https://github.com/xiaogangw/VE-PCN. | [
"cs.CV",
"cs.AI"
] |
Image segmentation of touching objects plays a key role in providing accurate
classification for computer vision technologies. A new line profile based
imaging segmentation algorithm has been developed to provide a robust and
accurate segmentation of a group of touching corns. The performance of the line
profile based algorithm has been compared to a watershed based imaging
segmentation algorithm. Both algorithms are tested on three different patterns
of images, which are isolated corns, single-lines, and random distributed
formations. The experimental results show that the algorithm can segment a
large number of touching corn kernels efficiently and accurately. | [
"cs.CV"
] |
State-of-the-art models for unpaired image-to-image translation with
Generative Adversarial Networks (GANs) can learn the mapping from the source
domain to the target domain using a cycle-consistency loss. The intuition
behind these models is that if we translate from one domain to the other and
back again we should arrive at where we started. However, existing methods
always adopt a symmetric network architecture to learn both forward and
backward cycles. Because of the task complexity and cycle input difference
between the source and target image domains, the inequality in bidirectional
forward-backward cycle translations is significant and the amount of
information between two domains is different. In this paper, we analyze the
limitation of the existing symmetric GAN models in asymmetric translation
tasks, and propose an AsymmetricGAN model with both translation and
reconstruction generators of unequal sizes and different parameter-sharing
strategy to adapt to the asymmetric need in both unsupervised and supervised
image-to-image translation tasks. Moreover, the training stage of existing
methods has the common problem of model collapse that degrades the quality of
the generated images, thus we explore different optimization losses for better
training of AsymmetricGAN, and thus make image-to-image translation with higher
consistency and better stability. Extensive experiments on both supervised and
unsupervised generative tasks with several publicly available datasets
demonstrate that the proposed AsymmetricGAN achieves superior model capacity
and better generation performance compared with existing GAN models. To the
best of our knowledge, we are the first to investigate the asymmetric GAN
framework on both unsupervised and supervised image-to-image translation tasks.
The source code, data and trained models are available at
https://github.com/Ha0Tang/AsymmetricGAN. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
In many domains data is currently represented as graphs and therefore, the
graph representation of this data becomes increasingly important in machine
learning. Network data is, implicitly or explicitly, always represented using a
graph shift operator (GSO) with the most common choices being the adjacency,
Laplacian matrices and their normalisations. In this paper, a novel
parametrised GSO (PGSO) is proposed, where specific parameter values result in
the most commonly used GSOs and message-passing operators in graph neural
network (GNN) frameworks. The PGSO is suggested as a replacement of the
standard GSOs that are used in state-of-the-art GNN architectures and the
optimisation of the PGSO parameters is seamlessly included in the model
training. It is proved that the PGSO has real eigenvalues and a set of real
eigenvectors independent of the parameter values and spectral bounds on the
PGSO are derived. PGSO parameters are shown to adapt to the sparsity of the
graph structure in a study on stochastic blockmodel networks, where they are
found to automatically replicate the GSO regularisation found in the
literature. On several real-world datasets the accuracy of state-of-the-art GNN
architectures is improved by the inclusion of the PGSO in both node- and
graph-classification tasks. | [
"cs.LG",
"stat.ML"
] |
Massive Open Online Courses (MOOCs) have become popular platforms for online
learning. While MOOCs enable students to study at their own pace, this
flexibility makes it easy for students to drop out of class. In this paper, our
goal is to predict if a learner is going to drop out within the next week,
given clickstream data for the current week. To this end, we present a
multi-layer representation learning solution based on branch and bound (BB)
algorithm, which learns from low-level clickstreams in an unsupervised manner,
produces interpretable results, and avoids manual feature engineering. In
experiments on Coursera data, we show that our model learns a representation
that allows a simple model to perform similarly well to more complex,
task-specific models, and how the BB algorithm enables interpretable results.
In our analysis of the observed limitations, we discuss promising future
directions. | [
"cs.LG",
"cs.AI"
] |
Auto-encoding generative adversarial networks (GANs) combine the standard GAN
algorithm, which discriminates between real and model-generated data, with a
reconstruction loss given by an auto-encoder. Such models aim to prevent mode
collapse in the learned generative model by ensuring that it is grounded in all
the available training data. In this paper, we develop a principle upon which
auto-encoders can be combined with generative adversarial networks by
exploiting the hierarchical structure of the generative model. The underlying
principle shows that variational inference can be used a basic tool for
learning, but with the in- tractable likelihood replaced by a synthetic
likelihood, and the unknown posterior distribution replaced by an implicit
distribution; both synthetic likelihoods and implicit posterior distributions
can be learned using discriminators. This allows us to develop a natural fusion
of variational auto-encoders and generative adversarial networks, combining the
best of both these methods. We describe a unified objective for optimization,
discuss the constraints needed to guide learning, connect to the wide range of
existing work, and use a battery of tests to systematically and quantitatively
assess the performance of our method. | [
"stat.ML",
"cs.LG"
] |
Reliable epistemic uncertainty estimation is an essential component for
backend applications of deep object detectors in safety-critical environments.
Modern network architectures tend to give poorly calibrated confidences with
limited predictive power. Here, we introduce novel gradient-based uncertainty
metrics and investigate them for different object detection architectures.
Experiments on the MS COCO, PASCAL VOC and the KITTI dataset show significant
improvements in true positive / false positive discrimination and prediction of
intersection over union as compared to network confidence. We also find
improvement over Monte-Carlo dropout uncertainty metrics and further
significant boosts by aggregating different sources of uncertainty metrics.The
resulting uncertainty models generate well-calibrated confidences in all
instances. Furthermore, we implement our uncertainty quantification models into
object detection pipelines as a means to discern true against false
predictions, replacing the ordinary score-threshold-based decision rule. In our
experiments, we achieve a significant boost in detection performance in terms
of mean average precision. With respect to computational complexity, we find
that computing gradient uncertainty metrics results in floating point operation
counts similar to those of Monte-Carlo dropout. | [
"cs.CV"
] |
We consider retrieving a specific temporal segment, or moment, from a video
given a natural language text description. Methods designed to retrieve whole
video clips with natural language determine what occurs in a video but not
when. To address this issue, we propose the Moment Context Network (MCN) which
effectively localizes natural language queries in videos by integrating local
and global video features over time. A key obstacle to training our MCN model
is that current video datasets do not include pairs of localized video segments
and referring expressions, or text descriptions which uniquely identify a
corresponding moment. Therefore, we collect the Distinct Describable Moments
(DiDeMo) dataset which consists of over 10,000 unedited, personal videos in
diverse visual settings with pairs of localized video segments and referring
expressions. We demonstrate that MCN outperforms several baseline methods and
believe that our initial results together with the release of DiDeMo will
inspire further research on localizing video moments with natural language. | [
"cs.CV"
] |
Deep Convolutional Neural Networks (CNNs) are widely employed in modern
computer vision algorithms, where the input image is convolved iteratively by
many kernels to extract the knowledge behind it. However, with the depth of
convolutional layers getting deeper and deeper in recent years, the enormous
computational complexity makes it difficult to be deployed on embedded systems
with limited hardware resources. In this paper, we propose two
computation-performance optimization methods to reduce the redundant
convolution kernels of a CNN with performance and architecture constraints, and
apply it to a network for super resolution (SR). Using PSNR drop compared to
the original network as the performance criterion, our method can get the
optimal PSNR under a certain computation budget constraint. On the other hand,
our method is also capable of minimizing the computation required under a given
PSNR drop. | [
"cs.CV"
] |
Having access to multi-modal cues (e.g. vision and audio) empowers some
cognitive tasks to be done faster compared to learning from a single modality.
In this work, we propose to transfer knowledge across heterogeneous modalities,
even though these data modalities may not be semantically correlated. Rather
than directly aligning the representations of different modalities, we compose
audio, image, and video representations across modalities to uncover richer
multi-modal knowledge. Our main idea is to learn a compositional embedding that
closes the cross-modal semantic gap and captures the task-relevant semantics,
which facilitates pulling together representations across modalities by
compositional contrastive learning. We establish a new, comprehensive
multi-modal distillation benchmark on three video datasets: UCF101,
ActivityNet, and VGGSound. Moreover, we demonstrate that our model
significantly outperforms a variety of existing knowledge distillation methods
in transferring audio-visual knowledge to improve video representation
learning. Code is released here: https://github.com/yanbeic/CCL. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
In this paper, we propose a new homological method to study weighted directed
networks. Our model of such networks is a directed graph $Q$ equipped with a
weight function $w$ on the set $Q_{1}$ of arrows in $Q$. We require that the
range $W$ of our weight function is equipped with an addition or a
multiplication, i.e., $W$ is a monoid in the mathematical terminology. When $W$
is equipped with a representation on a vector space $M$, the standard method of
homological algebra allows us to define the homology groups $H_{*}(Q,w;M)$. It
is known that when $Q$ has no oriented cycles, $H_{n}(Q,w;M)=0$ for $n\ge 2$
and $H_{1}(Q,w;M)$ can be easily computed. This fact allows us to define a new
graph kernel for weighted directed graphs. We made two sample computations with
real data and found that our method is practically applicable. | [
"cs.LG",
"stat.ML"
] |
Interpretability of deep learning (DL) systems is gaining attention in
medical imaging to increase experts' trust in the obtained predictions and
facilitate their integration in clinical settings. We propose a deep
visualization method to generate interpretability of DL classification tasks in
medical imaging by means of visual evidence augmentation. The proposed method
iteratively unveils abnormalities based on the prediction of a classifier
trained only with image-level labels. For each image, initial visual evidence
of the prediction is extracted with a given visual attribution technique. This
provides localization of abnormalities that are then removed through selective
inpainting. We iteratively apply this procedure until the system considers the
image as normal. This yields augmented visual evidence, including less
discriminative lesions which were not detected at first but should be
considered for final diagnosis. We apply the method to grading of two retinal
diseases in color fundus images: diabetic retinopathy (DR) and age-related
macular degeneration (AMD). We evaluate the generated visual evidence and the
performance of weakly-supervised localization of different types of DR and AMD
abnormalities, both qualitatively and quantitatively. We show that the
augmented visual evidence of the predictions highlights the biomarkers
considered by the experts for diagnosis and improves the final localization
performance. It results in a relative increase of 11.2$\pm$2.0% per image
regarding average sensitivity per average 10 false positives, when applied to
different classification tasks, visual attribution techniques and network
architectures. This makes the proposed method a useful tool for exhaustive
visual support of DL classifiers in medical imaging. | [
"cs.CV"
] |
Reinforcement Learning (RL) can be used to fit a mapping from patient state
to a medication regimen. Prior studies have used deterministic and value-based
tabular learning to learn a propofol dose from an observed anesthetic state.
Deep RL replaces the table with a deep neural network and has been used to
learn medication regimens from registry databases. Here we perform the first
application of deep RL to closed-loop control of anesthetic dosing in a
simulated environment. We use the cross-entropy method to train a deep neural
network to map an observed anesthetic state to a probability of infusing a
fixed propofol dosage. During testing, we implement a deterministic policy that
transforms the probability of infusion to a continuous infusion rate. The model
is trained and tested on simulated pharmacokinetic/pharmacodynamic models with
randomized parameters to ensure robustness to patient variability. The deep RL
agent significantly outperformed a proportional-integral-derivative controller
(median absolute performance error 1.7% +/- 0.6 and 3.4% +/- 1.2). Modeling
continuous input variables instead of a table affords more robust pattern
recognition and utilizes our prior domain knowledge. Deep RL learned a smooth
policy with a natural interpretation to data scientists and anesthesia care
providers alike. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Point cloud registration has been one of the basic steps of point cloud
processing, which has a lot of applications in remote sensing and robotics. In
this report, we summarized the basic workflow of target-less point cloud
registration,namely correspondence determination and transformation estimation.
Then we reviewed three commonly used groups of registration approaches, namely
the feature matching based methods, the iterative closest points algorithm and
the randomly hypothesis and verify based methods. Besides, we analyzed the
advantage and disadvantage of these methods are introduced their common
application scenarios. At last, we discussed the challenges of current point
cloud registration methods and proposed several open questions for the future
development of automatic registration approaches. | [
"cs.CV",
"cs.RO"
] |
Deep reinforcement learning (RL) has shown great empirical successes, but
suffers from brittleness and sample inefficiency. A potential remedy is to use
a previously-trained policy as a source of supervision. In this work, we refer
to these policies as teachers and study how to transfer their expertise to new
student policies by focusing on data usage. We propose a framework, Data
CUrriculum for Reinforcement learning (DCUR), which first trains teachers using
online deep RL, and stores the logged environment interaction history. Then,
students learn by running either offline RL or by using teacher data in
combination with a small amount of self-generated data. DCUR's central idea
involves defining a class of data curricula which, as a function of training
time, limits the student to sampling from a fixed subset of the full teacher
data. We test teachers and students using state-of-the-art deep RL algorithms
across a variety of data curricula. Results suggest that the choice of data
curricula significantly impacts student learning, and that it is beneficial to
limit the data during early training stages while gradually letting the data
availability grow over time. We identify when the student can learn offline and
match teacher performance without relying on specialized offline RL algorithms.
Furthermore, we show that collecting a small fraction of online data provides
complementary benefits with the data curriculum. Supplementary material is
available at https://tinyurl.com/teach-dcur. | [
"cs.LG",
"cs.RO"
] |
On account of its many successes in inference tasks and denoising
applications, Dictionary Learning (DL) and its related sparse optimization
problems have garnered a lot of research interest. While most solutions have
focused on single layer dictionaries, the improved recently proposed Deep DL
(DDL) methods have also fallen short on a number of issues. We propose herein,
a novel DDL approach where each DL layer can be formulated as a combination of
one linear layer and a Recurrent Neural Network (RNN). The RNN is shown to
flexibly account for the layer-associated and learned metric. Our proposed work
unveils new insights into Neural Networks and DDL and provides a new, efficient
and competitive approach to jointly learn a deep transform and a metric for
inference applications. Extensive experiments are carried out to demonstrate
that the proposed method can not only outperform existing DDL but also
state-of-the-art generic CNNs. | [
"cs.LG",
"stat.ML"
] |
Semantic Segmentation (SS) is promising for outdoor scene perception in
safety-critical applications like autonomous vehicles, assisted navigation and
so on. However, traditional SS is primarily based on RGB images, which limits
the reliability of SS in complex outdoor scenes, where RGB images lack
necessary information dimensions to fully perceive unconstrained environments.
As preliminary investigation, we examine SS in an unexpected obstacle detection
scenario, which demonstrates the necessity of multimodal fusion. Thereby, in
this work, we present EAFNet, an Efficient Attention-bridged Fusion Network to
exploit complementary information coming from different optical sensors.
Specifically, we incorporate polarization sensing to obtain supplementary
information, considering its optical characteristics for robust representation
of diverse materials. By using a single-shot polarization sensor, we build the
first RGB-P dataset which consists of 394 annotated pixel-aligned
RGB-Polarization images. A comprehensive variety of experiments shows the
effectiveness of EAFNet to fuse polarization and RGB information, as well as
the flexibility to be adapted to other sensor combination scenarios. | [
"cs.CV",
"cs.RO",
"eess.IV"
] |
The cost-efficiency of visual(-inertial) SLAM (VSLAM) is a critical
characteristic of resource-limited applications. While hardware and algorithm
advances have been significantly improved the cost-efficiency of VSLAM
front-ends, the cost-efficiency of VSLAM back-ends remains a bottleneck. This
paper describes a novel, rigorous method to improve the cost-efficiency of
local BA in a BA-based VSLAM back-end. An efficient algorithm, called Good
Graph, is developed to select size-reduced graphs optimized in local BA with
condition preservation. To better suit BA-based VSLAM back-ends, the Good Graph
predicts future estimation needs, dynamically assigns an appropriate size
budget, and selects a condition-maximized subgraph for BA estimation.
Evaluations are conducted on two scenarios: 1) VSLAM as standalone process, and
2) VSLAM as part of closed-loop navigation system. Results from the first
scenario show Good Graph improves accuracy and robustness of VSLAM estimation,
when computational limits exist. Results from the second scenario, indicate
that Good Graph benefits the trajectory tracking performance of VSLAM-based
closed-loop navigation systems, which is a primary application of VSLAM. | [
"cs.CV",
"cs.RO"
] |
Point cloud processing and 3D shape understanding are very challenging tasks
for which deep learning techniques have demonstrated great potentials. Still
further progresses are essential to allow artificial intelligent agents to
interact with the real world, where the amount of annotated data may be limited
and integrating new sources of knowledge becomes crucial to support autonomous
learning. Here we consider several possible scenarios involving synthetic and
real-world point clouds where supervised learning fails due to data scarcity
and large domain gaps. We propose to enrich standard feature representations by
leveraging self-supervision through a multi-task model that can solve a 3D
puzzle while learning the main task of shape classification or part
segmentation. An extensive analysis investigating few-shot, transfer learning
and cross-domain settings shows the effectiveness of our approach with
state-of-the-art results for 3D shape classification and part segmentation. | [
"cs.CV"
] |
In this work we present a novel system for PET estimation using CT scans. We
explore the use of fully convolutional networks (FCN) and conditional
generative adversarial networks (GAN) to export PET data from CT data. Our
dataset includes 25 pairs of PET and CT scans where 17 were used for training
and 8 for testing. The system was tested for detection of malignant tumors in
the liver region. Initial results look promising showing high detection
performance with a TPR of 92.3% and FPR of 0.25 per case. Future work entails
expansion of the current system to the entire body using a much larger dataset.
Such a system can be used for tumor detection and drug treatment evaluation in
a CT-only environment instead of the expansive and radioactive PET-CT scan. | [
"cs.CV",
"cs.AI"
] |
Exploration is an extremely challenging problem in reinforcement learning,
especially in high dimensional state and action spaces and when only sparse
rewards are available. Effective representations can indicate which components
of the state are task relevant and thus reduce the dimensionality of the space
to explore. In this work, we take a representation learning viewpoint on
exploration, utilizing prior experience to learn effective latent
representations, which can subsequently indicate which regions to explore.
Prior experience on separate but related tasks help learn representations of
the state which are effective at predicting instantaneous rewards. These
learned representations can then be used with an entropy-based exploration
method to effectively perform exploration in high dimensional spaces by
effectively lowering the dimensionality of the search space. We show the
benefits of this representation for meta-exploration in a simulated object
pushing environment. | [
"cs.LG",
"stat.ML"
] |
Learning image transformations is essential to the idea of mental simulation
as a method of cognitive inference. We take a connectionist modeling approach,
using planar neural networks to learn fundamental imagery transformations, like
translation, rotation, and scaling, from perceptual experiences in the form of
image sequences. We investigate how variations in network topology, training
data, and image shape, among other factors, affect the efficiency and
effectiveness of learning visual imagery transformations, including
effectiveness of transfer to operating on new types of data. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Several works have proposed Simplicity Bias (SB)---the tendency of standard
training procedures such as Stochastic Gradient Descent (SGD) to find simple
models---to justify why neural networks generalize well [Arpit et al. 2017,
Nakkiran et al. 2019, Soudry et al. 2018]. However, the precise notion of
simplicity remains vague. Furthermore, previous settings that use SB to
theoretically justify why neural networks generalize well do not simultaneously
capture the non-robustness of neural networks---a widely observed phenomenon in
practice [Goodfellow et al. 2014, Jo and Bengio 2017]. We attempt to reconcile
SB and the superior standard generalization of neural networks with the
non-robustness observed in practice by designing datasets that (a) incorporate
a precise notion of simplicity, (b) comprise multiple predictive features with
varying levels of simplicity, and (c) capture the non-robustness of neural
networks trained on real data. Through theory and empirics on these datasets,
we make four observations: (i) SB of SGD and variants can be extreme: neural
networks can exclusively rely on the simplest feature and remain invariant to
all predictive complex features. (ii) The extreme aspect of SB could explain
why seemingly benign distribution shifts and small adversarial perturbations
significantly degrade model performance. (iii) Contrary to conventional wisdom,
SB can also hurt generalization on the same data distribution, as SB persists
even when the simplest feature has less predictive power than the more complex
features. (iv) Common approaches to improve generalization and
robustness---ensembles and adversarial training---can fail in mitigating SB and
its pitfalls. Given the role of SB in training neural networks, we hope that
the proposed datasets and methods serve as an effective testbed to evaluate
novel algorithmic approaches aimed at avoiding the pitfalls of SB. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
We propose pixelNeRF, a learning framework that predicts a continuous neural
scene representation conditioned on one or few input images. The existing
approach for constructing neural radiance fields involves optimizing the
representation to every scene independently, requiring many calibrated views
and significant compute time. We take a step towards resolving these
shortcomings by introducing an architecture that conditions a NeRF on image
inputs in a fully convolutional manner. This allows the network to be trained
across multiple scenes to learn a scene prior, enabling it to perform novel
view synthesis in a feed-forward manner from a sparse set of views (as few as
one). Leveraging the volume rendering approach of NeRF, our model can be
trained directly from images with no explicit 3D supervision. We conduct
extensive experiments on ShapeNet benchmarks for single image novel view
synthesis tasks with held-out objects as well as entire unseen categories. We
further demonstrate the flexibility of pixelNeRF by demonstrating it on
multi-object ShapeNet scenes and real scenes from the DTU dataset. In all
cases, pixelNeRF outperforms current state-of-the-art baselines for novel view
synthesis and single image 3D reconstruction. For the video and code, please
visit the project website: https://alexyu.net/pixelnerf | [
"cs.CV",
"cs.GR",
"cs.LG"
] |
We introduce FPConv, a novel surface-style convolution operator designed for
3D point cloud analysis. Unlike previous methods, FPConv doesn't require
transforming to intermediate representation like 3D grid or graph and directly
works on surface geometry of point cloud. To be more specific, for each point,
FPConv performs a local flattening by automatically learning a weight map to
softly project surrounding points onto a 2D grid. Regular 2D convolution can
thus be applied for efficient feature learning. FPConv can be easily integrated
into various network architectures for tasks like 3D object classification and
3D scene segmentation, and achieve comparable performance with existing
volumetric-type convolutions. More importantly, our experiments also show that
FPConv can be a complementary of volumetric convolutions and jointly training
them can further boost overall performance into state-of-the-art results. | [
"cs.CV"
] |
Human attention mechanisms often work in a top-down manner, yet it is not
well explored in vision research. Here, we propose the Top-Down Attention
Framework (TDAF) to capture top-down attentions, which can be easily adopted in
most existing models. The designed Recursive Dual-Directional Nested Structure
in it forms two sets of orthogonal paths, recursive and structural ones, where
bottom-up spatial features and top-down attention features are extracted
respectively. Such spatial and attention features are nested deeply, therefore,
the proposed framework works in a mixed top-down and bottom-up manner.
Empirical evidence shows that our TDAF can capture effective stratified
attention information and boost performance. ResNet with TDAF achieves 2.0%
improvements on ImageNet. For object detection, the performance is improved by
2.7% AP over FCOS. For pose estimation, TDAF improves the baseline by 1.6%. And
for action recognition, the 3D-ResNet adopting TDAF achieves improvements of
1.7% accuracy. | [
"cs.CV"
] |
In medical image analysis, semi-supervised learning is an effective method to
extract knowledge from a small amount of labeled data and a large amount of
unlabeled data. This paper focuses on a popular pipeline known as self
learning, and points out a weakness named lazy learning that refers to the
difficulty for a model to learn from the pseudo labels generated by itself. To
alleviate this issue, we propose ATSO, an asynchronous version of
teacher-student optimization. ATSO partitions the unlabeled data into two
subsets and alternately uses one subset to fine-tune the model and updates the
label on the other subset. We evaluate ATSO on two popular medical image
segmentation datasets and show its superior performance in various
semi-supervised settings. With slight modification, ATSO transfers well to
natural image segmentation for autonomous driving data. | [
"cs.CV"
] |
Given the importance of remote sensing, surprisingly little attention has
been paid to it by the representation learning community. To address it and to
establish baselines and a common evaluation protocol in this domain, we provide
simplified access to 5 diverse remote sensing datasets in a standardized form.
Specifically, we investigate in-domain representation learning to develop
generic remote sensing representations and explore which characteristics are
important for a dataset to be a good source for remote sensing representation
learning. The established baselines achieve state-of-the-art performance on
these datasets. | [
"cs.CV"
] |
Alzheimer's disease (AD) is an irreversible devastative neurodegenerative
disorder associated with progressive impairment of memory and cognitive
functions. Its early diagnosis is crucial for the development of possible
future treatment option(s). Structural magnetic resonance images (sMRI) plays
an important role to help in understanding the anatomical changes related to AD
especially in its early stages. Conventional methods require the expertise of
domain experts and extract hand-picked features such as gray matter
substructures and train a classifier to distinguish AD subjects from healthy
subjects. Different from these methods, this paper proposes to construct
multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various
features from local brain images which are combined to make the final
classification for AD diagnosis. The whole brain image was passed through two
transfer learning architectures; Inception version 3 and Xception; as well as
custom Convolutional Neural Network (CNN) built with the help of separable
convolutional layers which can automatically learn the generic features from
imaging data for classification. Our study is conducted using cross-sectional
T1-weighted structural MRI brain images from Open Access Series of Imaging
Studies (OASIS) database to maintain the size and contrast over different MRI
scans. Experimental results show that the transfer learning approaches exceed
the performance of non-transfer learning based approaches demonstrating the
effectiveness of these approaches for the binary AD classification task. | [
"cs.CV",
"cs.LG",
"q-bio.QM"
] |
Learning local descriptors is an important problem in computer vision. While
there are many techniques for learning local patch descriptors for 2D images,
recently efforts have been made for learning local descriptors for 3D points.
The recent progress towards solving this problem in 3D leverages the strong
feature representation capability of image based convolutional neural networks
by utilizing RGB-D or multi-view representations. However, in this paper, we
propose to learn 3D local descriptors by directly processing unstructured 3D
point clouds without needing any intermediate representation. The method
constitutes a deep network for learning permutation invariant representation of
3D points. To learn the local descriptors, we use a multi-margin contrastive
loss which discriminates between similar and dissimilar points on a surface
while also leveraging the extent of dissimilarity among the negative samples at
the time of training. With comprehensive evaluation against strong baselines,
we show that the proposed method outperforms state-of-the-art methods for
matching points in 3D point clouds. Further, we demonstrate the effectiveness
of the proposed method on various applications achieving state-of-the-art
results. | [
"cs.CV"
] |
The most sophisticated existing methods to generate 3D isotropic
super-resolution (SR) from non-isotropic electron microscopy (EM) are based on
learned dictionaries. Unfortunately, none of the existing methods generate
practically satisfying results. For 2D natural images, recently developed
super-resolution methods that use deep learning have been shown to
significantly outperform the previous state of the art.
We have adapted one of the most successful architectures (FSRCNN) for 3D
super-resolution, and compared its performance to a 3D U-Net architecture that
has not been used previously to generate super-resolution.
We trained both architectures on artificially downscaled isotropic ground
truth from focused ion beam milling scanning EM (FIB-SEM) and tested the
performance for various hyperparameter settings.
Our results indicate that both architectures can successfully generate 3D
isotropic super-resolution from non-isotropic EM, with the U-Net performing
consistently better. We propose several promising directions for practical
application. | [
"cs.CV"
] |
Lack of transparency has been the Achilles heal of Neural Networks and their
wider adoption in industry. Despite significant interest this shortcoming has
not been adequately addressed. This study proposes a novel framework called
Hide-and-Seek (HnS) for training Interpretable Neural Networks and establishes
a theoretical foundation for exploring and comparing similar ideas. Extensive
experimentation indicates that a high degree of interpretability can be imputed
into Neural Networks, without sacrificing their predictive power. | [
"cs.LG",
"cs.AI",
"stat.ML",
"62M45",
"I.2.6"
] |
The global spread of COVID-19, the disease caused by the novel coronavirus
SARS-CoV-2, has cast a significant threat to mankind. As the COVID-19 situation
continues to evolve, predicting localized disease severity is crucial for
advanced resource allocation. This paper proposes a method named COURAGE
(COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of
2-week-ahead COVID-19 related deaths for each county in the United States,
leveraging modern deep learning techniques. Specifically, our method adopts a
self-attention model from Natural Language Processing, known as the transformer
model, to capture both short-term and long-term dependencies within the time
series while enjoying computational efficiency. Our model fully utilizes
publicly available information of COVID-19 related confirmed cases, deaths,
community mobility trends and demographic information, and can produce
state-level prediction as an aggregation of the corresponding county-level
predictions. Our numerical experiments demonstrate that our model achieves the
state-of-the-art performance among the publicly available benchmark models. | [
"cs.LG",
"stat.AP"
] |
Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear
processing of graph signals, exhibiting success in recommender systems, power
outage prediction, and motion planning, among others. GNNs consists of a
cascade of layers, each of which applies a graph convolution, followed by a
pointwise nonlinearity. In this work, we study the impact that changes in the
underlying topology have on the output of the GNN. First, we show that GNNs are
permutation equivariant, which implies that they effectively exploit internal
symmetries of the underlying topology. Then, we prove that graph convolutions
with integral Lipschitz filters, in combination with the frequency mixing
effect of the corresponding nonlinearities, yields an architecture that is both
stable to small changes in the underlying topology and discriminative of
information located at high frequencies. These are two properties that cannot
simultaneously hold when using only linear graph filters, which are either
discriminative or stable, thus explaining the superior performance of GNNs. | [
"cs.LG",
"stat.ML"
] |
Model-free learning for multi-agent stochastic games is an active area of
research. Existing reinforcement learning algorithms, however, are often
restricted to zero-sum games, and are applicable only in small state-action
spaces or other simplified settings. Here, we develop a new data efficient
Deep-Q-learning methodology for model-free learning of Nash equilibria for
general-sum stochastic games. The algorithm uses a local linear-quadratic
expansion of the stochastic game, which leads to analytically solvable optimal
actions. The expansion is parametrized by deep neural networks to give it
sufficient flexibility to learn the environment without the need to experience
all state-action pairs. We study symmetry properties of the algorithm stemming
from label-invariant stochastic games and as a proof of concept, apply our
algorithm to learning optimal trading strategies in competitive electronic
markets. | [
"cs.LG",
"cs.GT",
"q-fin.CP",
"stat.ML"
] |
We introduce TextWorld, a sandbox learning environment for the training and
evaluation of RL agents on text-based games. TextWorld is a Python library that
handles interactive play-through of text games, as well as backend functions
like state tracking and reward assignment. It comes with a curated list of
games whose features and challenges we have analyzed. More significantly, it
enables users to handcraft or automatically generate new games. Its generative
mechanisms give precise control over the difficulty, scope, and language of
constructed games, and can be used to relax challenges inherent to commercial
text games like partial observability and sparse rewards. By generating sets of
varied but similar games, TextWorld can also be used to study generalization
and transfer learning. We cast text-based games in the Reinforcement Learning
formalism, use our framework to develop a set of benchmark games, and evaluate
several baseline agents on this set and the curated list. | [
"cs.LG",
"cs.CL",
"stat.ML"
] |
Self-supervised learning allows for better utilization of unlabelled data.
The feature representation obtained by self-supervision can be used in
downstream tasks such as classification, object detection, segmentation, and
anomaly detection. While classification, object detection, and segmentation
have been investigated with self-supervised learning, anomaly detection needs
more attention. We consider the problem of anomaly detection in images and
videos, and present a new visual anomaly detection technique for videos.
Numerous seminal and state-of-the-art self-supervised methods are evaluated for
anomaly detection on a variety of image datasets. The best performing
image-based self-supervised representation learning method is then used for
video anomaly detection to see the importance of spatial features in visual
anomaly detection in videos. We also propose a simple self-supervision approach
for learning temporal coherence across video frames without the use of any
optical flow information. At its core, our method identifies the frame indices
of a jumbled video sequence allowing it to learn the spatiotemporal features of
the video. This intuitive approach shows superior performance of visual anomaly
detection compared to numerous methods for images and videos on UCF101 and
ILSVRC2015 video datasets. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
In a data-scarce field such as healthcare, where models often deliver
predictions on patients with rare conditions, the ability to measure the
uncertainty of a model's prediction could potentially lead to improved
effectiveness of decision support tools and increased user trust. This work
advances the understanding of uncertainty estimation for classification and
risk prediction on medical tabular data, in a two-fold way. First, we expand
and refine the set of heuristics to select an uncertainty estimation technique,
introducing tests for clinically-relevant scenarios such as generalization to
uncommon pathologies, changes in clinical protocol and simulations of corrupted
data. We furthermore differentiate these heuristics depending on the clinical
use-case. Second, we observe that ensembles and related techniques perform
poorly when it comes to detecting out-of-domain examples, a critical task which
is carried out more successfully by auto-encoders. These remarks are enriched
by considerations of the interplay of uncertainty estimation with class
imbalance, post-modeling calibration and other modeling procedures. Our
findings are supported by an array of experiments on toy and real-world data. | [
"stat.ML",
"cs.LG"
] |
First-order methods for quadratic optimization such as OSQP are widely used
for large-scale machine learning and embedded optimal control, where many
related problems must be rapidly solved. These methods face two persistent
challenges: manual hyperparameter tuning and convergence time to high-accuracy
solutions. To address these, we explore how Reinforcement Learning (RL) can
learn a policy to tune parameters to accelerate convergence. In experiments
with well-known QP benchmarks we find that our RL policy, RLQP, significantly
outperforms state-of-the-art QP solvers by up to 3x. RLQP generalizes
surprisingly well to previously unseen problems with varying dimension and
structure from different applications, including the QPLIB, Netlib LP and
Maros-Meszaros problems. Code for RLQP is available at
https://github.com/berkeleyautomation/rlqp. | [
"cs.LG",
"math.OC"
] |
In this work, we propose \texttt{TimeGrad}, an autoregressive model for
multivariate probabilistic time series forecasting which samples from the data
distribution at each time step by estimating its gradient. To this end, we use
diffusion probabilistic models, a class of latent variable models closely
connected to score matching and energy-based methods. Our model learns
gradients by optimizing a variational bound on the data likelihood and at
inference time converts white noise into a sample of the distribution of
interest through a Markov chain using Langevin sampling. We demonstrate
experimentally that the proposed autoregressive denoising diffusion model is
the new state-of-the-art multivariate probabilistic forecasting method on
real-world data sets with thousands of correlated dimensions. We hope that this
method is a useful tool for practitioners and lays the foundation for future
research in this area. | [
"cs.LG",
"cs.AI"
] |
The relational model is a ubiquitous representation of big-data, in part due
to its extensive use in databases. In this paper, we propose the Equivariant
Entity-Relationship Network (EERN), which is a Multilayer Perceptron
equivariant to the symmetry transformations of the Entity-Relationship model.
To this end, we identify the most expressive family of linear maps that are
exactly equivariant to entity relationship symmetries, and further show that
they subsume recently introduced equivariant maps for sets, exchangeable
tensors, and graphs. The proposed feed-forward layer has linear complexity in
the data and can be used for both inductive and transductive reasoning about
relational databases, including database embedding, and the prediction of
missing records. This provides a principled theoretical foundation for the
application of deep learning to one of the most abundant forms of data.
Empirically, EERN outperforms different variants of coupled matrix tensor
factorization in both synthetic and real-data experiments. | [
"cs.LG",
"stat.ML"
] |
The popularity of Deep Learning for real-world applications is ever-growing.
With the introduction of high performance hardware, applications are no longer
limited to image recognition. With the introduction of more complex problems
comes more and more complex solutions, and the increasing need for explainable
AI. Deep Neural Networks for Video tasks are amongst the most complex models,
with at least twice the parameters of their Image counterparts. However,
explanations for these models are often ill-adapted to the video domain. The
current work in explainability for video models is still overshadowed by Image
techniques, while Video Deep Learning itself is quickly gaining on methods for
still images. This paper seeks to highlight the need for explainability methods
designed with video deep learning models, and by association spatio-temporal
input in mind, by first illustrating the cutting edge for video deep learning,
and then noting the scarcity of research into explanations for these methods. | [
"cs.LG",
"cs.CV",
"cs.HC",
"eess.IV",
"stat.ML"
] |
Recent studies identified that sequential Recommendation is improved by the
attention mechanism. By following this development, we propose Relation-Aware
Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the
Transformer with augmentation of a probabilistic model. The original
self-attention of Transformer is a deterministic measure without
relation-awareness. Therefore, we introduce a latent space to the
self-attention, and the latent space models the recommendation context from
relation as a multivariate skew-normal distribution with a kernelized
covariance matrix from co-occurrences, item characteristics, and user
information. This work merges the self-attention of the Transformer and the
sequential recommendation by adding a probabilistic model of the recommendation
task specifics. We experimented RKSA over the benchmark datasets, and RKSA
shows significant improvements compared to the recent baseline models. Also,
RKSA were able to produce a latent space model that answers the reasons for
recommendation. | [
"cs.LG",
"cs.IR",
"stat.ML"
] |
We propose a universal building block of Convolutional Neural Network
(ConvNet) to improve the performance without any inference-time costs. The
block is named Diverse Branch Block (DBB), which enhances the representational
capacity of a single convolution by combining diverse branches of different
scales and complexities to enrich the feature space, including sequences of
convolutions, multi-scale convolutions, and average pooling. After training, a
DBB can be equivalently converted into a single conv layer for deployment.
Unlike the advancements of novel ConvNet architectures, DBB complicates the
training-time microstructure while maintaining the macro architecture, so that
it can be used as a drop-in replacement for regular conv layers of any
architecture. In this way, the model can be trained to reach a higher level of
performance and then transformed into the original inference-time structure for
inference. DBB improves ConvNets on image classification (up to 1.9% higher
top-1 accuracy on ImageNet), object detection and semantic segmentation. The
PyTorch code and models are released at
https://github.com/DingXiaoH/DiverseBranchBlock. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
As the request for deep learning solutions increases, the need for
explainability is even more fundamental. In this setting, particular attention
has been given to visualization techniques, that try to attribute the right
relevance to each input pixel with respect to the output of the network. In
this paper, we focus on Class Activation Mapping (CAM) approaches, which
provide an effective visualization by taking weighted averages of the
activation maps. To enhance the evaluation and the reproducibility of such
approaches, we propose a novel set of metrics to quantify explanation maps,
which show better effectiveness and simplify comparisons between approaches. To
evaluate the appropriateness of the proposal, we compare different CAM-based
visualization methods on the entire ImageNet validation set, fostering proper
comparisons and reproducibility. | [
"cs.CV"
] |
Most classification models treat different object classes in parallel and the
misclassifications between any two classes are treated equally. In contrast,
human beings can exploit high-level information in making a prediction of an
unknown object. Inspired by this observation, the paper proposes a super-class
guided network (SGNet) to integrate the high-level semantic information into
the network so as to increase its performance in inference. SGNet takes
two-level class annotations that contain both super-class and finer class
labels. The super-classes are higher-level semantic categories that consist of
a certain amount of finer classes. A super-class branch (SCB), trained on
super-class labels, is introduced to guide finer class prediction. At the
inference time, we adopt two different strategies: Two-step inference (TSI) and
direct inference (DI). TSI first predicts the super-class and then makes
predictions of the corresponding finer class. On the other hand, DI directly
generates predictions from the finer class branch (FCB). Extensive experiments
have been performed on CIFAR-100 and MS COCO datasets. The experimental results
validate the proposed approach and demonstrate its superior performance on
image classification and object detection. | [
"cs.CV"
] |
Modeling a structured, dynamic environment like a video game requires keeping
track of the objects and their states declarative knowledge) as well as
predicting how objects behave (procedural knowledge). Black-box models with a
monolithic hidden state often fail to apply procedural knowledge consistently
and uniformly, i.e., they lack systematicity. For example, in a video game,
correct prediction of one enemy's trajectory does not ensure correct prediction
of another's. We address this issue via an architecture that factorizes
declarative and procedural knowledge and that imposes modularity within each
form of knowledge. The architecture consists of active modules called object
files that maintain the state of a single object and invoke passive external
knowledge sources called schemata that prescribe state updates. To use a video
game as an illustration, two enemies of the same type will share schemata but
will have separate object files to encode their distinct state (e.g., health,
position). We propose to use attention to determine which object files to
update, the selection of schemata, and the propagation of information between
object files. The resulting architecture is a drop-in replacement conforming to
the same input-output interface as normal recurrent networks (e.g., LSTM, GRU)
yet achieves substantially better generalization on environments that have
multiple object tokens of the same type, including a challenging intuitive
physics benchmark. | [
"cs.LG",
"stat.ML"
] |
Photo retouching enables photographers to invoke dramatic visual impressions
by artistically enhancing their photos through stylistic color and tone
adjustments. However, it is also a time-consuming and challenging task that
requires advanced skills beyond the abilities of casual photographers. Using an
automated algorithm is an appealing alternative to manual work but such an
algorithm faces many hurdles. Many photographic styles rely on subtle
adjustments that depend on the image content and even its semantics. Further,
these adjustments are often spatially varying. Because of these
characteristics, existing automatic algorithms are still limited and cover only
a subset of these challenges. Recently, deep machine learning has shown unique
abilities to address hard problems that resisted machine algorithms for long.
This motivated us to explore the use of deep learning in the context of photo
editing. In this paper, we explain how to formulate the automatic photo
adjustment problem in a way suitable for this approach. We also introduce an
image descriptor that accounts for the local semantics of an image. Our
experiments demonstrate that our deep learning formulation applied using these
descriptors successfully capture sophisticated photographic styles. In
particular and unlike previous techniques, it can model local adjustments that
depend on the image semantics. We show on several examples that this yields
results that are qualitatively and quantitatively better than previous work. | [
"cs.CV",
"cs.GR",
"cs.LG",
"eess.IV"
] |
Good temporal representations are crucial for video understanding, and the
state-of-the-art video recognition framework is based on two-stream networks.
In such framework, besides the regular ConvNets responsible for RGB frame
inputs, a second network is introduced to handle the temporal representation,
usually the optical flow (OF). However, OF or other task-oriented flow is
computationally costly, and is thus typically pre-computed. Critically, this
prevents the two-stream approach from being applied to reinforcement learning
(RL) applications such as video game playing, where the next state depends on
current state and action choices. Inspired by the early vision systems of
mammals and insects, we propose a fast event-driven representation (EDR) that
models several major properties of early retinal circuits: (1) logarithmic
input response, (2) multi-timescale temporal smoothing to filter noise, and (3)
bipolar (ON/OFF) pathways for primitive event detection[12]. Trading off the
directional information for fast speed (> 9000 fps), EDR en-ables fast
real-time inference/learning in video applications that require interaction
between an agent and the world such as game-playing, virtual robotics, and
domain adaptation. In this vein, we use EDR to demonstrate performance
improvements over state-of-the-art reinforcement learning algorithms for Atari
games, something that has not been possible with pre-computed OF. Moreover,
with UCF-101 video action recognition experiments, we show that EDR performs
near state-of-the-art in accuracy while achieving a 1,500x speedup in input
representation processing, as compared to optical flow. | [
"cs.CV"
] |
We introduce Position Adaptive Convolution (PAConv), a generic convolution
operation for 3D point cloud processing. The key of PAConv is to construct the
convolution kernel by dynamically assembling basic weight matrices stored in
Weight Bank, where the coefficients of these weight matrices are
self-adaptively learned from point positions through ScoreNet. In this way, the
kernel is built in a data-driven manner, endowing PAConv with more flexibility
than 2D convolutions to better handle the irregular and unordered point cloud
data. Besides, the complexity of the learning process is reduced by combining
weight matrices instead of brutally predicting kernels from point positions.
Furthermore, different from the existing point convolution operators whose
network architectures are often heavily engineered, we integrate our PAConv
into classical MLP-based point cloud pipelines without changing network
configurations. Even built on simple networks, our method still approaches or
even surpasses the state-of-the-art models, and significantly improves baseline
performance on both classification and segmentation tasks, yet with decent
efficiency. Thorough ablation studies and visualizations are provided to
understand PAConv. Code is released on https://github.com/CVMI-Lab/PAConv. | [
"cs.CV"
] |
We present DocFormer -- a multi-modal transformer based architecture for the
task of Visual Document Understanding (VDU). VDU is a challenging problem which
aims to understand documents in their varied formats (forms, receipts etc.) and
layouts. In addition, DocFormer is pre-trained in an unsupervised fashion using
carefully designed tasks which encourage multi-modal interaction. DocFormer
uses text, vision and spatial features and combines them using a novel
multi-modal self-attention layer. DocFormer also shares learned spatial
embeddings across modalities which makes it easy for the model to correlate
text to visual tokens and vice versa. DocFormer is evaluated on 4 different
datasets each with strong baselines. DocFormer achieves state-of-the-art
results on all of them, sometimes beating models 4x its size (in no. of
parameters). | [
"cs.CV"
] |
Deep Convolution Neural Networks (CNNs) have shown impressive performance in
various vision tasks such as image classification, object detection and
semantic segmentation. For object detection, particularly in still images, the
performance has been significantly increased last year thanks to powerful deep
networks (e.g. GoogleNet) and detection frameworks (e.g. Regions with CNN
features (R-CNN)). The lately introduced ImageNet task on object detection from
video (VID) brings the object detection task into the video domain, in which
objects' locations at each frame are required to be annotated with bounding
boxes. In this work, we introduce a complete framework for the VID task based
on still-image object detection and general object tracking. Their relations
and contributions in the VID task are thoroughly studied and evaluated. In
addition, a temporal convolution network is proposed to incorporate temporal
information to regularize the detection results and shows its effectiveness for
the task. | [
"cs.CV"
] |
Remote sensing image scene classification, which aims at labeling remote
sensing images with a set of semantic categories based on their contents, has
broad applications in a range of fields. Propelled by the powerful feature
learning capabilities of deep neural networks, remote sensing image scene
classification driven by deep learning has drawn remarkable attention and
achieved significant breakthroughs. However, to the best of our knowledge, a
comprehensive review of recent achievements regarding deep learning for scene
classification of remote sensing images is still lacking. Considering the rapid
evolution of this field, this paper provides a systematic survey of deep
learning methods for remote sensing image scene classification by covering more
than 160 papers. To be specific, we discuss the main challenges of remote
sensing image scene classification and survey (1) Autoencoder-based remote
sensing image scene classification methods, (2) Convolutional Neural
Network-based remote sensing image scene classification methods, and (3)
Generative Adversarial Network-based remote sensing image scene classification
methods. In addition, we introduce the benchmarks used for remote sensing image
scene classification and summarize the performance of more than two dozen of
representative algorithms on three commonly-used benchmark data sets. Finally,
we discuss the promising opportunities for further research. | [
"cs.CV"
] |
Inspired by the cache replacement problem, we propose and solve a new variant
of the well-known multi-armed bandit (MAB), thus providing a solution for
improving existing state-of-the-art cache management methods. Each arm (or
expert) represents a distinct cache replacement policy, which advises on the
page to evict from the cache when needed. Feedback on the eviction comes in the
form of a "miss", but at an indeterminate time after the action is taken, and
the cost of the eviction is set to be inversely proportional to the response
time. The feedback is ignored if it comes after a threshold value for the
delay, which we set to be equal to the size of the page eviction history. Thus,
for delays beyond the threshold, its cost is assumed to be zero. Consequently,
we call this problem with delayed feedback and decaying costs. We introduce an
adaptive reinforcement learning algorithm EXP4-DFDC that provides a solution to
the problem. We derive an optimal learning rate for EXP4-DFDC that defines the
balance between exploration and exploitation and proves theoretically that the
expected regret of our algorithm is a vanishing quantity as a function of time.
As an application, we show that LeCaR, a recent top-performing machine learning
algorithm for cache replacement, can be enhanced with adaptive learning using
our formulations. We present an improved adaptive version of LeCaR, called
OLeCaR, with the learning rate set as determined by the theoretical derivation
presented here to minimize regret for EXP4-DFDC. It then follows that LeCaR and
OLeCaR are theoretically guaranteed to have vanishing regret over time. | [
"cs.LG",
"stat.ML"
] |
We introduce a new loss function for the weakly-supervised training of
semantic image segmentation models based on three guiding principles: to seed
with weak localization cues, to expand objects based on the information about
which classes can occur in an image, and to constrain the segmentations to
coincide with object boundaries. We show experimentally that training a deep
convolutional neural network using the proposed loss function leads to
substantially better segmentations than previous state-of-the-art methods on
the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the
working mechanism of our method by a detailed experimental study that
illustrates how the segmentation quality is affected by each term of the
proposed loss function as well as their combinations. | [
"cs.CV"
] |
Complex analyses involving multiple, dependent random quantities often lead
to graphical models - a set of nodes denoting variables of interest, and
corresponding edges denoting statistical interactions between nodes. To develop
statistical analyses for graphical data, especially towards generative
modeling, one needs mathematical representations and metrics for matching and
comparing graphs, and subsequent tools, such as geodesics, means, and
covariances. This paper utilizes a quotient structure to develop efficient
algorithms for computing these quantities, leading to useful statistical tools,
including principal component analysis, statistical testing, and modeling. We
demonstrate the efficacy of this framework using datasets taken from several
problem areas, including letters, biochemical structures, and social networks. | [
"cs.CV",
"cs.LG",
"stat.ME"
] |
We consider reinforcement learning (RL) in episodic Markov decision processes
(MDPs) with linear function approximation under drifting environment.
Specifically, both the reward and state transition functions can evolve over
time, as long as their respective total variations, quantified by suitable
metrics, do not exceed certain \textit{variation budgets}. We first develop the
$\texttt{LSVI-UCB-Restart}$ algorithm, an optimistic modification of
least-squares value iteration combined with periodic restart, and establish its
dynamic regret bound when variation budgets are known. We then propose a
parameter-free algorithm, $\texttt{Ada-LSVI-UCB-Restart}$, that works without
knowing the variation budgets, but with a slightly worse dynamic regret bound.
We also derive the first minimax dynamic regret lower bound for nonstationary
MDPs to show that our proposed algorithms are near-optimal. As a byproduct, we
establish a minimax regret lower bound for linear MDPs, which is unsolved by
\cite{jin2020provably}. In addition, we provide numerical experiments to
demonstrate the effectiveness of our proposed algorithms. As far as we know,
this is the first dynamic regret analysis in nonstationary reinforcement
learning with function approximation. | [
"cs.LG",
"stat.ML"
] |
We propose a new 'Bi-Reduced Space' approach to solving 3D Variational Data
Assimilation using Convolutional Autoencoders. We prove that our approach has
the same solution as previous methods but has significantly lower computational
complexity; in other words, we reduce the computational cost without affecting
the data assimilation accuracy. We tested the new method with data from a
real-world application: a pollution model of a site in Elephant and Castle,
London and found that we could reduce the size of the background covariance
matrix representation by O(10^3) and, at the same time, increase our data
assimilation accuracy with respect to existing reduced space methods. | [
"cs.LG",
"cs.CE"
] |
Large scale object detection with thousands of classes introduces the problem
of many contradicting false positive detections, which have to be suppressed.
Class-independent non-maximum suppression has traditionally been used for this
step, but it does not scale well as the number of classes grows. Traditional
non-maximum suppression does not consider label- and instance-level
relationships nor does it allow an exploitation of the spatial layout of
detection proposals. We propose a new multi-class spatial semantic
regularisation method based on affinity propagation clustering, which
simultaneously optimises across all categories and all proposed locations in
the image, to improve both the localisation and categorisation of selected
detection proposals. Constraints are shared across the labels through the
semantic WordNet hierarchy. Our approach proves to be especially useful in
large scale settings with thousands of classes, where spatial and semantic
interactions are very frequent and only weakly supervised detectors can be
built due to a lack of bounding box annotations. Detection experiments are
conducted on the ImageNet and COCO dataset, and in settings with thousands of
detected categories. Our method provides a significant precision improvement by
reducing false positives, while simultaneously improving the recall. | [
"cs.CV"
] |
To generate "accurate" scene graphs, almost all existing methods predict
pairwise relationships in a deterministic manner. However, we argue that visual
relationships are often semantically ambiguous. Specifically, inspired by
linguistic knowledge, we classify the ambiguity into three types: Synonymy
Ambiguity, Hyponymy Ambiguity, and Multi-view Ambiguity. The ambiguity
naturally leads to the issue of \emph{implicit multi-label}, motivating the
need for diverse predictions. In this work, we propose a novel plug-and-play
Probabilistic Uncertainty Modeling (PUM) module. It models each union region as
a Gaussian distribution, whose variance measures the uncertainty of the
corresponding visual content. Compared to the conventional deterministic
methods, such uncertainty modeling brings stochasticity of feature
representation, which naturally enables diverse predictions. As a byproduct,
PUM also manages to cover more fine-grained relationships and thus alleviates
the issue of bias towards frequent relationships. Extensive experiments on the
large-scale Visual Genome benchmark show that combining PUM with newly proposed
ResCAGCN can achieve state-of-the-art performances, especially under the mean
recall metric. Furthermore, we prove the universal effectiveness of PUM by
plugging it into some existing models and provide insightful analysis of its
ability to generate diverse yet plausible visual relationships. | [
"cs.CV"
] |
The neural ordinary differential equation (neural ODE) model has attracted
increasing attention in time series analysis for its capability to process
irregular time steps, i.e., data are not observed over equally-spaced time
intervals. In multi-dimensional time series analysis, a task is to conduct
evolutionary subspace clustering, aiming at clustering temporal data according
to their evolving low-dimensional subspace structures. Many existing methods
can only process time series with regular time steps while time series are
unevenly sampled in many situations such as missing data. In this paper, we
propose a neural ODE model for evolutionary subspace clustering to overcome
this limitation and a new objective function with subspace self-expressiveness
constraint is introduced. We demonstrate that this method can not only
interpolate data at any time step for the evolutionary subspace clustering
task, but also achieve higher accuracy than other state-of-the-art evolutionary
subspace clustering methods. Both synthetic and real-world data are used to
illustrate the efficacy of our proposed method. | [
"cs.LG",
"cs.AI"
] |
We extend first-order model agnostic meta-learning algorithms (including
FOMAML and Reptile) to image segmentation, present a novel neural network
architecture built for fast learning which we call EfficientLab, and leverage a
formal definition of the test error of meta-learning algorithms to decrease
error on out of distribution tasks. We show state of the art results on the
FSS-1000 dataset by meta-training EfficientLab with FOMAML and using Bayesian
optimization to infer the optimal test-time adaptation routine hyperparameters.
We also construct a small benchmark dataset, FP-k, for the empirical study of
how meta-learning systems perform in both few- and many-shot settings. On the
FP-k dataset, we show that meta-learned initializations provide value for
canonical few-shot image segmentation but their performance is quickly matched
by conventional transfer learning with performance being equal beyond 10
labeled examples. Our code, meta-learned model, and the FP-k dataset are
available at https://github.com/ml4ai/mliis . | [
"cs.LG",
"cs.CV",
"eess.IV",
"stat.ML"
] |
Learning RAW-to-sRGB mapping has drawn increasing attention in recent years,
wherein an input raw image is trained to imitate the target sRGB image captured
by another camera. However, the severe color inconsistency makes it very
challenging to generate well-aligned training pairs of input raw and target
sRGB images. While learning with inaccurately aligned supervision is prone to
causing pixel shift and producing blurry results. In this paper, we circumvent
such issue by presenting a joint learning model for image alignment and
RAW-to-sRGB mapping. To diminish the effect of color inconsistency in image
alignment, we introduce to use a global color mapping (GCM) module to generate
an initial sRGB image given the input raw image, which can keep the spatial
location of the pixels unchanged, and the target sRGB image is utilized to
guide GCM for converting the color towards it. Then a pre-trained optical flow
estimation network (e.g., PWC-Net) is deployed to warp the target sRGB image to
align with the GCM output. To alleviate the effect of inaccurately aligned
supervision, the warped target sRGB image is leveraged to learn RAW-to-sRGB
mapping. When training is done, the GCM module and optical flow network can be
detached, thereby bringing no extra computation cost for inference. Experiments
show that our method performs favorably against state-of-the-arts on ZRR and
SR-RAW datasets. With our joint learning model, a light-weight backbone can
achieve better quantitative and qualitative performance on ZRR dataset. Codes
are available at https://github.com/cszhilu1998/RAW-to-sRGB. | [
"cs.CV"
] |
We present the Colorization Transformer, a novel approach for diverse high
fidelity image colorization based on self-attention. Given a grayscale image,
the colorization proceeds in three steps. We first use a conditional
autoregressive transformer to produce a low resolution coarse coloring of the
grayscale image. Our architecture adopts conditional transformer layers to
effectively condition grayscale input. Two subsequent fully parallel networks
upsample the coarse colored low resolution image into a finely colored high
resolution image. Sampling from the Colorization Transformer produces diverse
colorings whose fidelity outperforms the previous state-of-the-art on
colorising ImageNet based on FID results and based on a human evaluation in a
Mechanical Turk test. Remarkably, in more than 60% of cases human evaluators
prefer the highest rated among three generated colorings over the ground truth.
The code and pre-trained checkpoints for Colorization Transformer are publicly
available at
https://github.com/google-research/google-research/tree/master/coltran | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
The paper proposes the ScatterNet Hybrid Deep Learning (SHDL) network that
extracts invariant and discriminative image representations for object
recognition. SHDL framework is constructed with a multi-layer ScatterNet
front-end, an unsupervised learning middle, and a supervised learning back-end
module. Each layer of the SHDL network is automatically designed as an explicit
optimization problem leading to an optimal deep learning architecture with
improved computational performance as compared to the more usual deep network
architectures. SHDL network produces the state-of-the-art classification
performance against unsupervised and semi-supervised learning (GANs) on two
image datasets. Advantages of the SHDL network over supervised methods (NIN,
VGG) are also demonstrated with experiments performed on training datasets of
reduced size. | [
"cs.CV"
] |
We present data-dependent learning bounds for the general scenario of
non-stationary non-mixing stochastic processes. Our learning guarantees are
expressed in terms of a data-dependent measure of sequential complexity and a
discrepancy measure that can be estimated from data under some mild
assumptions. We also also provide novel analysis of stable time series
forecasting algorithm using this new notion of discrepancy that we introduce.
We use our learning bounds to devise new algorithms for non-stationary time
series forecasting for which we report some preliminary experimental results. | [
"cs.LG"
] |
The success of reinforcement learning for real world robotics has been, in
many cases limited to instrumented laboratory scenarios, often requiring
arduous human effort and oversight to enable continuous learning. In this work,
we discuss the elements that are needed for a robotic learning system that can
continually and autonomously improve with data collected in the real world. We
propose a particular instantiation of such a system, using dexterous
manipulation as our case study. Subsequently, we investigate a number of
challenges that come up when learning without instrumentation. In such
settings, learning must be feasible without manually designed resets, using
only on-board perception, and without hand-engineered reward functions. We
propose simple and scalable solutions to these challenges, and then demonstrate
the efficacy of our proposed system on a set of dexterous robotic manipulation
tasks, providing an in-depth analysis of the challenges associated with this
learning paradigm. We demonstrate that our complete system can learn without
any human intervention, acquiring a variety of vision-based skills with a
real-world three-fingered hand. Results and videos can be found at
https://sites.google.com/view/realworld-rl/ | [
"cs.LG",
"cs.RO",
"stat.ML"
] |
In self-supervised spatio-temporal representation learning, the temporal
resolution and long-short term characteristics are not yet fully explored,
which limits representation capabilities of learned models. In this paper, we
propose a novel self-supervised method, referred to as video Playback Rate
Perception (PRP), to learn spatio-temporal representation in a
simple-yet-effective way. PRP roots in a dilated sampling strategy, which
produces self-supervision signals about video playback rates for representation
model learning. PRP is implemented with a feature encoder, a classification
module, and a reconstructing decoder, to achieve spatio-temporal semantic
retention in a collaborative discrimination-generation manner. The
discriminative perception model follows a feature encoder to prefer perceiving
low temporal resolution and long-term representation by classifying
fast-forward rates. The generative perception model acts as a feature decoder
to focus on comprehending high temporal resolution and short-term
representation by introducing a motion-attention mechanism. PRP is applied on
typical video target tasks including action recognition and video retrieval.
Experiments show that PRP outperforms state-of-the-art self-supervised models
with significant margins. Code is available at github.com/yuanyao366/PRP | [
"cs.CV"
] |
Context, the embedding of previous collected trajectories, is a powerful
construct for Meta-Reinforcement Learning (Meta-RL) algorithms. By conditioning
on an effective context, Meta-RL policies can easily generalize to new tasks
within a few adaptation steps. We argue that improving the quality of context
involves answering two questions: 1. How to train a compact and sufficient
encoder that can embed the task-specific information contained in prior
trajectories? 2. How to collect informative trajectories of which the
corresponding context reflects the specification of tasks? To this end, we
propose a novel Meta-RL framework called CCM (Contrastive learning augmented
Context-based Meta-RL). We first focus on the contrastive nature behind
different tasks and leverage it to train a compact and sufficient context
encoder. Further, we train a separate exploration policy and theoretically
derive a new information-gain-based objective which aims to collect informative
trajectories in a few steps. Empirically, we evaluate our approaches on common
benchmarks as well as several complex sparse-reward environments. The
experimental results show that CCM outperforms state-of-the-art algorithms by
addressing previously mentioned problems respectively. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Radiomic representations can quantify properties of regions of interest in
medical image data. Classically, they account for pre-defined statistics of
shape, texture, and other low-level image features. Alternatively, deep
learning-based representations are derived from supervised learning but require
expensive annotations from experts and often suffer from overfitting and data
imbalance issues. In this work, we address the challenge of learning
representations of 3D medical images for an effective quantification under data
imbalance. We propose a \emph{self-supervised} representation learning
framework to learn high-level features of 3D volumes as a complement to
existing radiomics features. Specifically, we demonstrate how to learn image
representations in a self-supervised fashion using a 3D Siamese network. More
importantly, we deal with data imbalance by exploiting two unsupervised
strategies: a) sample re-weighting, and b) balancing the composition of
training batches. When combining our learned self-supervised feature with
traditional radiomics, we show significant improvement in brain tumor
classification and lung cancer staging tasks covering MRI and CT imaging
modalities. | [
"cs.CV",
"cs.AI"
] |
We provide a comprehensive evaluation of salient object detection (SOD)
models. Our analysis identifies a serious design bias of existing SOD datasets
which assumes that each image contains at least one clearly outstanding salient
object in low clutter. The design bias has led to a saturated high performance
for state-of-the-art SOD models when evaluated on existing datasets. The
models, however, still perform far from being satisfactory when applied to
real-world daily scenes. Based on our analyses, we first identify 7 crucial
aspects that a comprehensive and balanced dataset should fulfill. Then, we
propose a new high quality dataset and update the previous saliency benchmark.
Specifically, our SOC (Salient Objects in Clutter) dataset, includes images
with salient and non-salient objects from daily object categories. Beyond
object category annotations, each salient image is accompanied by attributes
that reflect common challenges in real-world scenes. Finally, we report
attribute-based performance assessment on our dataset. | [
"cs.CV"
] |
Automatic speech emotion recognition provides computers with critical context
to enable user understanding. While methods trained and tested within the same
dataset have been shown successful, they often fail when applied to unseen
datasets. To address this, recent work has focused on adversarial methods to
find more generalized representations of emotional speech. However, many of
these methods have issues converging, and only involve datasets collected in
laboratory conditions. In this paper, we introduce Adversarial Discriminative
Domain Generalization (ADDoG), which follows an easier to train "meet in the
middle" approach. The model iteratively moves representations learned for each
dataset closer to one another, improving cross-dataset generalization. We also
introduce Multiclass ADDoG, or MADDoG, which is able to extend the proposed
method to more than two datasets, simultaneously. Our results show consistent
convergence for the introduced methods, with significantly improved results
when not using labels from the target dataset. We also show how, in most cases,
ADDoG and MADDoG can be used to improve upon baseline state-of-the-art methods
when target dataset labels are added and in-the-wild data are considered. Even
though our experiments focus on cross-corpus speech emotion, these methods
could be used to remove unwanted factors of variation in other settings. | [
"cs.LG",
"cs.SD",
"eess.AS",
"stat.ML"
] |
The Quadratic Assignment Problem (QAP) is a well-known permutation-based
combinatorial optimization problem with real applications in industrial and
logistics environments. Motivated by the challenge that this NP-hard problem
represents, it has captured the attention of the optimization community for
decades. As a result, a large number of algorithms have been proposed to tackle
this problem. Among these, exact methods are only able to solve instances of
size $n<40$. To overcome this limitation, many metaheuristic methods have been
applied to the QAP.
In this work, we follow this direction by approaching the QAP through
Estimation of Distribution Algorithms (EDAs). Particularly, a non-parametric
distance-based exponential probabilistic model is used. Based on the analysis
of the characteristics of the QAP, and previous work in the area, we introduce
Kernels of Mallows Model under the Hamming distance to the context of EDAs.
Conducted experiments point out that the performance of the proposed algorithm
in the QAP is superior to (i) the classical EDAs adapted to deal with the QAP,
and also (ii) to the specific EDAs proposed in the literature to deal with
permutation problems. | [
"stat.ML",
"cs.LG"
] |
Instance segmentation of images is an important tool for automated scene
understanding. Neural networks are usually trained to optimize their overall
performance in terms of accuracy. Meanwhile, in applications such as automated
driving, an overlooked pedestrian seems more harmful than a falsely detected
one. In this work, we present a false negative detection method for image
sequences based on inconsistencies in time series of tracked instances given
the availability of image sequences in online applications. As the number of
instances can be greatly increased by this algorithm, we apply a false positive
pruning using uncertainty estimates aggregated over instances. To this end,
instance-wise metrics are constructed which characterize uncertainty and
geometry of a given instance or are predicated on depth estimation. The
proposed method serves as a post-processing step applicable to any neural
network that can also be trained on single frames only. In our tests, we obtain
an improved trade-off between false negative and false positive instances by
our fused detection approach in comparison to the use of an ordinary score
value provided by the instance segmentation network during inference. | [
"cs.CV"
] |
Human pose transfer has received great attention due to its wide
applications, yet is still a challenging task that is not well solved. Recent
works have achieved great success to transfer the person image from the source
to the target pose. However, most of them cannot well capture the semantic
appearance, resulting in inconsistent and less realistic textures on the
reconstructed results. To address this issue, we propose a new two-stage
framework to handle the pose and appearance translation. In the first stage, we
predict the target semantic parsing maps to eliminate the difficulties of pose
transfer and further benefit the latter translation of per-region appearance
style. In the second one, with the predicted target semantic maps, we suggest a
new person image generation method by incorporating the region-adaptive
normalization, in which it takes the per-region styles to guide the target
appearance generation. Extensive experiments show that our proposed SPGNet can
generate more semantic, consistent, and photo-realistic results and perform
favorably against the state of the art methods in terms of quantitative and
qualitative evaluation. The source code and model are available at
https://github.com/cszy98/SPGNet.git. | [
"cs.CV"
] |
Book covers are usually the very first impression to its readers and they
often convey important information about the content of the book. Book genre
classification based on its cover would be utterly beneficial to many modern
retrieval systems, considering that the complete digitization of books is an
extremely expensive task. At the same time, it is also an extremely challenging
task due to the following reasons: First, there exists a wide variety of book
genres, many of which are not concretely defined. Second, book covers, as
graphic designs, vary in many different ways such as colors, styles, textual
information, etc, even for books of the same genre. Third, book cover designs
may vary due to many external factors such as country, culture, target reader
populations, etc. With the growing competitiveness in the book industry, the
book cover designers and typographers push the cover designs to its limit in
the hope of attracting sales. The cover-based book classification systems
become a particularly exciting research topic in recent years. In this paper,
we propose a multi-modal deep learning framework to solve this problem. The
contribution of this paper is four-fold. First, our method adds an extra
modality by extracting texts automatically from the book covers. Second,
image-based and text-based, state-of-the-art models are evaluated thoroughly
for the task of book cover classification. Third, we develop an efficient and
salable multi-modal framework based on the images and texts shown on the covers
only. Fourth, a thorough analysis of the experimental results is given and
future works to improve the performance is suggested. The results show that the
multi-modal framework significantly outperforms the current state-of-the-art
image-based models. However, more efforts and resources are needed for this
classification task in order to reach a satisfactory level. | [
"cs.CV",
"cs.CL"
] |
The way people look in terms of facial attributes (ethnicity, hair color,
facial hair, etc.) and the clothes or accessories they wear (sunglasses, hat,
hoodies, etc.) is highly dependent on geo-location and weather condition,
respectively. This work explores, for the first time, the use of this
contextual information, as people with wearable cameras walk across different
neighborhoods of a city, in order to learn a rich feature representation for
facial attribute classification, without the costly manual annotation required
by previous methods. By tracking the faces of casual walkers on more than 40
hours of egocentric video, we are able to cover tens of thousands of different
identities and automatically extract nearly 5 million pairs of images connected
by or from different face tracks, along with their weather and location
context, under pose and lighting variations. These image pairs are then fed
into a deep network that preserves similarity of images connected by the same
track, in order to capture identity-related attribute features, and optimizes
for location and weather prediction to capture additional facial attribute
features. Finally, the network is fine-tuned with manually annotated samples.
We perform an extensive experimental analysis on wearable data and two standard
benchmark datasets based on web images (LFWA and CelebA). Our method
outperforms by a large margin a network trained from scratch. Moreover, even
without using manually annotated identity labels for pre-training as in
previous methods, our approach achieves results that are better than the state
of the art. | [
"cs.CV"
] |
A decision tree looks like a simple computational graph without cycles, where
only the leaf nodes specify the output values and the non-terminals specify
their tests or split conditions. From the numerical perspective, we express
decision trees in the language of computational graph. We explicitly
parameterize the test phase, traversal phase and prediction phase of decision
trees based on the bitvectors of non-terminal nodes. As shown later, the
decision tree is a shallow binary network in some sense. Especially, we
introduce the bitvector matrix to implement the tree traversal in numerical
approach, where the core is to convert the logical `AND' operation to
arithmetic operations. And we apply this numerical representation to extend and
unify diverse decision trees in concept. | [
"cs.LG"
] |
Large amounts of labeled training data are one of the main contributors to
the great success that deep models have achieved in the past. Label acquisition
for tasks other than benchmarks can pose a challenge due to requirements of
both funding and expertise. By selecting unlabeled examples that are promising
in terms of model improvement and only asking for respective labels, active
learning can increase the efficiency of the labeling process in terms of time
and cost.
In this work, we describe combinations of an incremental learning scheme and
methods of active learning. These allow for continuous exploration of newly
observed unlabeled data. We describe selection criteria based on model
uncertainty as well as expected model output change (EMOC). An object detection
task is evaluated in a continuous exploration context on the PASCAL VOC
dataset. We also validate a weakly supervised system based on active and
incremental learning in a real-world biodiversity application where images from
camera traps are analyzed. Labeling only 32 images by accepting or rejecting
proposals generated by our method yields an increase in accuracy from 25.4% to
42.6%. | [
"cs.CV"
] |
Interpretability and fairness are critical in computer vision and machine
learning applications, in particular when dealing with human outcomes, e.g.
inviting or not inviting for a job interview based on application materials
that may include photographs. One promising direction to achieve fairness is by
learning data representations that remove the semantics of protected
characteristics, and are therefore able to mitigate unfair outcomes. All
available models however learn latent embeddings which comes at the cost of
being uninterpretable. We propose to cast this problem as data-to-data
translation, i.e. learning a mapping from an input domain to a fair target
domain, where a fairness definition is being enforced. Here the data domain can
be images, or any tabular data representation. This task would be
straightforward if we had fair target data available, but this is not the case.
To overcome this, we learn a highly unconstrained mapping by exploiting
statistics of residuals - the difference between input data and its translated
version - and the protected characteristics. When applied to the CelebA dataset
of face images with gender attribute as the protected characteristic, our model
enforces equality of opportunity by adjusting the eyes and lips regions.
Intriguingly, on the same dataset we arrive at similar conclusions when using
semantic attribute representations of images for translation. On face images of
the recent DiF dataset, with the same gender attribute, our method adjusts nose
regions. In the Adult income dataset, also with protected gender attribute, our
model achieves equality of opportunity by, among others, obfuscating the wife
and husband relationship. Analyzing those systematic changes will allow us to
scrutinize the interplay of fairness criterion, chosen protected
characteristics, and prediction performance. | [
"cs.LG",
"stat.ML"
] |
Navigation inside a closed area with no GPS-signal accessibility is a highly
challenging task. In order to tackle this problem, recently the imaging-based
methods have grabbed the attention of many researchers. These methods either
extract the features (e.g. using SIFT, or SOSNet) and map the descriptive ones
to the camera position and rotation information, or deploy an end-to-end system
that directly estimates this information out of RGB images, similar to PoseNet.
While the former methods suffer from heavy computational burden during the test
process, the latter suffers from lack of accuracy and robustness against
environmental changes and object movements. However, end-to-end systems are
quite fast during the test and inference and are pretty qualified for
real-world applications, even though their training phase could be longer than
the former ones. In this paper, a novel multi-modal end-to-end system for
large-scale indoor positioning has been proposed, namely APS (Alpha Positioning
System), which integrates a Pix2Pix GAN network to reconstruct the point cloud
pair of the input query image, with a deep CNN network in order to robustly
estimate the position and rotation information of the camera. For this
integration, the existing datasets have the shortcoming of paired RGB/point
cloud images for indoor environments. Therefore, we created a new dataset to
handle this situation. By implementing the proposed APS system, we could
achieve a highly accurate camera positioning with a precision level of less
than a centimeter. | [
"cs.CV"
] |
Recently, local SGD has got much attention and been extensively studied in
the distributed learning community to overcome the communication bottleneck
problem. However, the superiority of local SGD to minibatch SGD only holds in
quite limited situations. In this paper, we study a new local algorithm called
Bias-Variance Reduced Local SGD (BVR-L-SGD) for nonconvex distributed
optimization. Algorithmically, our proposed bias and variance reduced local
gradient estimator fully utilizes small second-order heterogeneity of local
objectives and suggests randomly picking up one of the local models instead of
taking the average of them when workers are synchronized. Theoretically, under
small heterogeneity of local objectives, we show that BVR-L-SGD achieves better
communication complexity than both the previous non-local and local methods
under mild conditions, and particularly BVR-L-SGD is the first method that
breaks the barrier of communication complexity $\Theta(1/\varepsilon)$ for
general nonconvex smooth objectives when the heterogeneity is small and the
local computation budget is large. Numerical results are given to verify the
theoretical findings and give empirical evidence of the superiority of our
method. | [
"cs.LG",
"math.OC"
] |
We propose a framework for transferring any existing policy from a
potentially unknown source MDP to a target MDP. This framework (1) enables
reuse in the target domain of any form of source policy, including classical
controllers, heuristic policies, or deep neural network-based policies, (2)
attains optimality under suitable theoretical conditions, and (3) guarantees
improvement over the source policy in the target MDP. These are achieved by
packaging the source policy as a black-box option in the target MDP and
providing a theoretically grounded way to learn the option's initiation set
through general value functions. Our approach facilitates the learning of new
policies by (1) maximizing the target MDP reward with the help of the black-box
option, and (2) returning the agent to states in the learned initiation set of
the black-box option where it is already optimal. We show that these two
variants are equivalent in performance under some conditions. Through a series
of experiments in simulated environments, we demonstrate that our framework
performs excellently in sparse reward problems given (sub-)optimal source
policies and improves upon prior art in transfer methods such as continual
learning and progressive networks, which lack our framework's desirable
theoretical properties. | [
"cs.LG",
"cs.AI"
] |
The task of video grounding, which temporally localizes a natural language
description in a video, plays an important role in understanding videos.
Existing studies have adopted strategies of sliding window over the entire
video or exhaustively ranking all possible clip-sentence pairs in a
pre-segmented video, which inevitably suffer from exhaustively enumerated
candidates. To alleviate this problem, we formulate this task as a problem of
sequential decision making by learning an agent which regulates the temporal
grounding boundaries progressively based on its policy. Specifically, we
propose a reinforcement learning based framework improved by multi-task
learning and it shows steady performance gains by considering additional
supervised boundary information during training. Our proposed framework
achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset
and Charades-STA dataset while observing only 10 or less clips per video. | [
"cs.CV"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.