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Imitation learning is a primary approach to improve the efficiency of
reinforcement learning by exploiting the expert demonstrations. However, in
many real scenarios, obtaining expert demonstrations could be extremely
expensive or even impossible. To overcome this challenge, in this paper, we
propose a novel learning framework called Co-Imitation Learning (CoIL) to
exploit the past good experiences of the agents themselves without expert
demonstration. Specifically, we train two different agents via letting each of
them alternately explore the environment and exploit the peer agent's
experience. While the experiences could be valuable or misleading, we propose
to estimate the potential utility of each piece of experience with the expected
gain of the value function. Thus the agents can selectively imitate from each
other by emphasizing the more useful experiences while filtering out noisy
ones. Experimental results on various tasks show significant superiority of the
proposed Co-Imitation Learning framework, validating that the agents can
benefit from each other without external supervision. | [
"cs.LG",
"cs.AI"
] |
State-of-the-art methods for zero-shot visual recognition formulate learning
as a joint embedding problem of images and side information. In these
formulations the current best complement to visual features are attributes:
manually encoded vectors describing shared characteristics among categories.
Despite good performance, attributes have limitations: (1) finer-grained
recognition requires commensurately more attributes, and (2) attributes do not
provide a natural language interface. We propose to overcome these limitations
by training neural language models from scratch; i.e. without pre-training and
only consuming words and characters. Our proposed models train end-to-end to
align with the fine-grained and category-specific content of images. Natural
language provides a flexible and compact way of encoding only the salient
visual aspects for distinguishing categories. By training on raw text, our
model can do inference on raw text as well, providing humans a familiar mode
both for annotation and retrieval. Our model achieves strong performance on
zero-shot text-based image retrieval and significantly outperforms the
attribute-based state-of-the-art for zero-shot classification on the Caltech
UCSD Birds 200-2011 dataset. | [
"cs.CV"
] |
Video restoration tasks, including super-resolution, deblurring, etc, are
drawing increasing attention in the computer vision community. A challenging
benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark
challenges existing methods from two aspects: (1) how to align multiple frames
given large motions, and (2) how to effectively fuse different frames with
diverse motion and blur. In this work, we propose a novel Video Restoration
framework with Enhanced Deformable networks, termed EDVR, to address these
challenges. First, to handle large motions, we devise a Pyramid, Cascading and
Deformable (PCD) alignment module, in which frame alignment is done at the
feature level using deformable convolutions in a coarse-to-fine manner. Second,
we propose a Temporal and Spatial Attention (TSA) fusion module, in which
attention is applied both temporally and spatially, so as to emphasize
important features for subsequent restoration. Thanks to these modules, our
EDVR wins the champions and outperforms the second place by a large margin in
all four tracks in the NTIRE19 video restoration and enhancement challenges.
EDVR also demonstrates superior performance to state-of-the-art published
methods on video super-resolution and deblurring. The code is available at
https://github.com/xinntao/EDVR. | [
"cs.CV"
] |
The multinomial and related distributions have long been used to model
categorical, count-based data in fields ranging from bioinformatics to natural
language processing. Commonly utilized variants include the standard
multinomial and the Dirichlet multinomial distributions due to their
computational efficiency and straightforward parameter estimation process.
However, these distributions make strict assumptions about the mean, variance,
and covariance between the categorical features being modeled. If these
assumptions are not met by the data, it may result in poor parameter estimates
and loss in accuracy for downstream applications like classification. Here, we
explore efficient parameter estimation and supervised classification methods
using an alternative distribution, called the Beta-Liouville multinomial, which
relaxes some of the multinomial assumptions. We show that the Beta-Liouville
multinomial is comparable in efficiency to the Dirichlet multinomial for
Newton-Raphson maximum likelihood estimation, and that its performance on
simulated data matches or exceeds that of the multinomial and Dirichlet
multinomial distributions. Finally, we demonstrate that the Beta-Liouville
multinomial outperforms the multinomial and Dirichlet multinomial on two out of
four gold standard datasets, supporting its use in modeling data with low to
medium class overlap in a supervised classification context. | [
"stat.ML",
"cs.LG"
] |
We explore the impact of learning paradigms on training deep neural networks
for the Travelling Salesman Problem. We design controlled experiments to train
supervised learning (SL) and reinforcement learning (RL) models on fixed graph
sizes up to 100 nodes, and evaluate them on variable sized graphs up to 500
nodes. Beyond not needing labelled data, our results reveal favorable
properties of RL over SL: RL training leads to better emergent generalization
to variable graph sizes and is a key component for learning scale-invariant
solvers for novel combinatorial problems. | [
"cs.LG",
"stat.ML"
] |
A new brand of technical artificial intelligence ( Explainable AI ) research
has focused on trying to open up the 'black box' and provide some
explainability. This paper presents a novel visual explanation method for deep
learning networks in the form of a saliency map that can effectively localize
entire object regions. In contrast to the current state-of-the art methods, the
proposed method shows quite promising visual explanations that can gain greater
trust of human expert. Both quantitative and qualitative evaluations are
carried out on both general and clinical data sets to confirm the effectiveness
of the proposed method. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Graphs have been widely used to represent complex data in many applications.
Efficient and effective analysis of graphs is important for graph-based
applications. However, most graph analysis tasks are combinatorial optimization
(CO) problems, which are NP-hard. Recent studies have focused a lot on the
potential of using machine learning (ML) to solve graph-based CO problems. Most
recent methods follow the two-stage framework. The first stage is graph
representation learning, which embeds the graphs into low-dimension vectors.
The second stage uses ML to solve the CO problems using the embeddings of the
graphs learned in the first stage. The works for the first stage can be
classified into two categories, graph embedding (GE) methods and end-to-end
(E2E) learning methods. For GE methods, learning graph embedding has its own
objective, which may not rely on the CO problems to be solved. The CO problems
are solved by independent downstream tasks. For E2E learning methods, the
learning of graph embeddings does not have its own objective and is an
intermediate step of the learning procedure of solving the CO problems. The
works for the second stage can also be classified into two categories,
non-autoregressive methods and autoregressive methods. Non-autoregressive
methods predict a solution for a CO problem in one shot. A non-autoregressive
method predicts a matrix that denotes the probability of each node/edge being a
part of a solution of the CO problem. The solution can be computed from the
matrix. Autoregressive methods iteratively extend a partial solution step by
step. At each step, an autoregressive method predicts a node/edge conditioned
to current partial solution, which is used to its extension. In this survey, we
provide a thorough overview of recent studies of the graph learning-based CO
methods. The survey ends with several remarks on future research directions. | [
"cs.LG",
"stat.ML"
] |
The effective representation, processing, analysis, and visualization of
large-scale structured data, especially those related to complex domains such
as networks and graphs, are one of the key questions in modern machine
learning. Graph signal processing (GSP), a vibrant branch of signal processing
models and algorithms that aims at handling data supported on graphs, opens new
paths of research to address this challenge. In this article, we review a few
important contributions made by GSP concepts and tools, such as graph filters
and transforms, to the development of novel machine learning algorithms. In
particular, our discussion focuses on the following three aspects: exploiting
data structure and relational priors, improving data and computational
efficiency, and enhancing model interpretability. Furthermore, we provide new
perspectives on future development of GSP techniques that may serve as a bridge
between applied mathematics and signal processing on one side, and machine
learning and network science on the other. Cross-fertilization across these
different disciplines may help unlock the numerous challenges of complex data
analysis in the modern age. | [
"cs.LG",
"cs.SI",
"eess.SP",
"stat.ML"
] |
3D object detection is vital for many robotics applications. For tasks where
a 2D perspective range image exists, we propose to learn a 3D representation
directly from this range image view. To this end, we designed a 2D
convolutional network architecture that carries the 3D spherical coordinates of
each pixel throughout the network. Its layers can consume any arbitrary
convolution kernel in place of the default inner product kernel and exploit the
underlying local geometry around each pixel. We outline four such kernels: a
dense kernel according to the bag-of-words paradigm, and three graph kernels
inspired by recent graph neural network advances: the Transformer, the
PointNet, and the Edge Convolution. We also explore cross-modality fusion with
the camera image, facilitated by operating in the perspective range image view.
Our method performs competitively on the Waymo Open Dataset and improves the
state-of-the-art AP for pedestrian detection from 69.7% to 75.5%. It is also
efficient in that our smallest model, which still outperforms the popular
PointPillars in quality, requires 180 times fewer FLOPS and model parameters | [
"cs.CV"
] |
Deep neural network (DNN) models have proven to be vulnerable to adversarial
attacks. In this paper, we propose VisionGuard, a novel attack- and
dataset-agnostic and computationally-light defense mechanism for adversarial
inputs to DNN-based perception systems. In particular, VisionGuard relies on
the observation that adversarial images are sensitive to lossy compression
transformations. Specifically, to determine if an image is adversarial,
VisionGuard checks if the output of the target classifier on a given input
image changes significantly after feeding it a transformed version of the image
under investigation. Moreover, we show that VisionGuard is
computationally-light both at runtime and design-time which makes it suitable
for real-time applications that may also involve large-scale image domains. To
highlight this, we demonstrate the efficiency of VisionGuard on ImageNet, a
task that is computationally challenging for the majority of relevant defenses.
Finally, we include extensive comparative experiments on the MNIST, CIFAR10,
and ImageNet datasets that show that VisionGuard outperforms existing defenses
in terms of scalability and detection performance. | [
"cs.CV",
"cs.RO",
"eess.IV"
] |
For a global breeding organization, identifying the next generation of
superior crops is vital for its success. Recognizing new genetic varieties
requires years of in-field testing to gather data about the crop's yield, pest
resistance, heat resistance, etc. At the conclusion of the growing season,
organizations need to determine which varieties will be advanced to the next
growing season (or sold to farmers) and which ones will be discarded from the
candidate pool. Specifically for soybeans, identifying their relative maturity
is a vital piece of information used for advancement decisions. However, this
trait needs to be physically observed, and there are resource limitations
(time, money, etc.) that bottleneck the data collection process. To combat
this, breeding organizations are moving toward advanced image capturing
devices. In this paper, we develop a robust and automatic approach for
estimating the relative maturity of soybeans using a time series of UAV images.
An end-to-end hybrid model combining Convolutional Neural Networks (CNN) and
Long Short-Term Memory (LSTM) is proposed to extract features and capture the
sequential behavior of time series data. The proposed deep learning model was
tested on six different environments across the United States. Results suggest
the effectiveness of our proposed CNN-LSTM model compared to the local
regression method. Furthermore, we demonstrate how this newfound information
can be used to aid in plant breeding advancement decisions. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Order-agnostic autoregressive distribution (density) estimation (OADE), i.e.,
autoregressive distribution estimation where the features can occur in an
arbitrary order, is a challenging problem in generative machine learning. Prior
work on OADE has encoded feature identity by assigning each feature to a
distinct fixed position in an input vector. As a result, architectures built
for these inputs must strategically mask either the input or model weights to
learn the various conditional distributions necessary for inferring the full
joint distribution of the dataset in an order-agnostic way. In this paper, we
propose an alternative approach for encoding feature identities, where each
feature's identity is included alongside its value in the input. This feature
identity encoding strategy allows neural architectures designed for sequential
data to be applied to the OADE task without modification. As a proof of
concept, we show that a Transformer trained on this input (which we refer to as
"the DEformer", i.e., the distribution estimating Transformer) can effectively
model binarized-MNIST, approaching the performance of fixed-order
autoregressive distribution estimating algorithms while still being entirely
order-agnostic. Additionally, we find that the DEformer surpasses the
performance of recent flow-based architectures when modeling a tabular dataset. | [
"cs.LG"
] |
Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization
(PPO), as the widely employed policy based reinforcement learning (RL) methods,
are prone to converge to a sub-optimal solution as they limit the policy
representation to a particular parametric distribution class. To address this
issue, we develop an innovative Optimistic Distributionally Robust Policy
Optimization (ODRPO) algorithm, which effectively utilizes Optimistic
Distributionally Robust Optimization (DRO) approach to solve the trust region
constrained optimization problem without parameterizing the policies. Our
algorithm improves TRPO and PPO with a higher sample efficiency and a better
performance of the final policy while attaining the learning stability.
Moreover, it achieves a globally optimal policy update that is not promised in
the prevailing policy based RL algorithms. Experiments across tabular domains
and robotic locomotion tasks demonstrate the effectiveness of our approach. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
Depth information is essential for on-board perception in autonomous driving
and driver assistance. Monocular depth estimation (MDE) is very appealing since
it allows for appearance and depth being on direct pixelwise correspondence
without further calibration. Best MDE models are based on Convolutional Neural
Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground
truth (GT). Usually, this GT is acquired at training time through a calibrated
multi-modal suite of sensors. However, also using only a monocular system at
training time is cheaper and more scalable. This is possible by relying on
structure-from-motion (SfM) principles to generate self-supervision.
Nevertheless, problems of camouflaged objects, visibility changes,
static-camera intervals, textureless areas, and scale ambiguity, diminish the
usefulness of such self-supervision. In this paper, we perform monocular depth
estimation by virtual-world supervision (MonoDEVS) and real-world SfM
self-supervision. We compensate the SfM self-supervision limitations by
leveraging virtual-world images with accurate semantic and depth supervision
and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms
previous MDE CNNs trained on monocular and even stereo sequences. | [
"cs.CV"
] |
First-person (egocentric) and third person (exocentric) videos are
drastically different in nature. The relationship between these two views have
been studied in recent years, however, it has yet to be fully explored. In this
work, we introduce two datasets (synthetic and natural/real) containing
simultaneously recorded egocentric and exocentric videos. We also explore
relating the two domains (egocentric and exocentric) in two aspects. First, we
synthesize images in the egocentric domain from the exocentric domain using a
conditional generative adversarial network (cGAN). We show that with enough
training data, our network is capable of hallucinating how the world would look
like from an egocentric perspective, given an exocentric video. Second, we
address the cross-view retrieval problem across the two views. Given an
egocentric query frame (or its momentary optical flow), we retrieve its
corresponding exocentric frame (or optical flow) from a gallery set. We show
that using synthetic data could be beneficial in retrieving real data. We show
that performing domain adaptation from the synthetic domain to the natural/real
domain, is helpful in tasks such as retrieval. We believe that the presented
datasets and the proposed baselines offer new opportunities for further
research in this direction. The code and dataset are publicly available. | [
"cs.CV"
] |
Deep learning-based style transfer between images has recently become a
popular area of research. A common way of encoding "style" is through a feature
representation based on the Gram matrix of features extracted by some
pre-trained neural network or some other form of feature statistics. Such a
definition is based on an arbitrary human decision and may not best capture
what a style really is. In trying to gain a better understanding of "style", we
propose a metric learning-based method to explicitly encode the style of an
artwork. In particular, our definition of style captures the differences
between artists, as shown by classification performances, and such that the
style representation can be interpreted, manipulated and visualized through
style-conditioned image generation through a Generative Adversarial Network. We
employ this method to explore the style space of anime portrait illustrations. | [
"cs.CV",
"stat.ML"
] |
This paper presents novel reconfigurable architectures for reducing the
latency of recurrent neural networks (RNNs) that are used for detecting
gravitational waves. Gravitational interferometers such as the LIGO detectors
capture cosmic events such as black hole mergers which happen at unknown times
and of varying durations, producing time-series data. We have developed a new
architecture capable of accelerating RNN inference for analyzing time-series
data from LIGO detectors. This architecture is based on optimizing the
initiation intervals (II) in a multi-layer LSTM (Long Short-Term Memory)
network, by identifying appropriate reuse factors for each layer. A
customizable template for this architecture has been designed, which enables
the generation of low-latency FPGA designs with efficient resource utilization
using high-level synthesis tools. The proposed approach has been evaluated
based on two LSTM models, targeting a ZYNQ 7045 FPGA and a U250 FPGA.
Experimental results show that with balanced II, the number of DSPs can be
reduced up to 42% while achieving the same IIs. When compared to other
FPGA-based LSTM designs, our design can achieve about 4.92 to 12.4 times lower
latency. | [
"cs.LG",
"cs.AR",
"physics.ins-det"
] |
Speech-driven facial video generation has been a complex problem due to its
multi-modal aspects namely audio and video domain. The audio comprises lots of
underlying features such as expression, pitch, loudness, prosody(speaking
style) and facial video has lots of variability in terms of head movement, eye
blinks, lip synchronization and movements of various facial action units along
with temporal smoothness. Synthesizing highly expressive facial videos from the
audio input and static image is still a challenging task for generative
adversarial networks. In this paper, we propose a multi-modal adaptive
normalization(MAN) based architecture to synthesize a talking person video of
arbitrary length using as input: an audio signal and a single image of a
person. The architecture uses the multi-modal adaptive normalization, keypoint
heatmap predictor, optical flow predictor and class activation map[58] based
layers to learn movements of expressive facial components and hence generates a
highly expressive talking-head video of the given person. The multi-modal
adaptive normalization uses the various features of audio and video such as Mel
spectrogram, pitch, energy from audio signals and predicted keypoint
heatmap/optical flow and a single image to learn the respective affine
parameters to generate highly expressive video. Experimental evaluation
demonstrates superior performance of the proposed method as compared to
Realistic Speech-Driven Facial Animation with GANs(RSDGAN) [53], Speech2Vid
[10], and other approaches, on multiple quantitative metrics including: SSIM
(structural similarity index), PSNR (peak signal to noise ratio), CPBD (image
sharpness), WER(word error rate), blinks/sec and LMD(landmark distance).
Further, qualitative evaluation and Online Turing tests demonstrate the
efficacy of our approach. | [
"cs.CV"
] |
Automating molecular design using deep reinforcement learning (RL) has the
potential to greatly accelerate the search for novel materials. Despite recent
progress on leveraging graph representations to design molecules, such methods
are fundamentally limited by the lack of three-dimensional (3D) information. In
light of this, we propose a novel actor-critic architecture for 3D molecular
design that can generate molecular structures unattainable with previous
approaches. This is achieved by exploiting the symmetries of the design process
through a rotationally covariant state-action representation based on a
spherical harmonics series expansion. We demonstrate the benefits of our
approach on several 3D molecular design tasks, where we find that building in
such symmetries significantly improves generalization and the quality of
generated molecules. | [
"stat.ML",
"cs.LG",
"physics.chem-ph"
] |
Advancements in neural machinery have led to a wide range of algorithmic
solutions for molecular property prediction. Two classes of models in
particular have yielded promising results: neural networks applied to computed
molecular fingerprints or expert-crafted descriptors, and graph convolutional
neural networks that construct a learned molecular representation by operating
on the graph structure of the molecule. However, recent literature has yet to
clearly determine which of these two methods is superior when generalizing to
new chemical space. Furthermore, prior research has rarely examined these new
models in industry research settings in comparison to existing employed models.
In this paper, we benchmark models extensively on 19 public and 16 proprietary
industrial datasets spanning a wide variety of chemical endpoints. In addition,
we introduce a graph convolutional model that consistently matches or
outperforms models using fixed molecular descriptors as well as previous graph
neural architectures on both public and proprietary datasets. Our empirical
findings indicate that while approaches based on these representations have yet
to reach the level of experimental reproducibility, our proposed model
nevertheless offers significant improvements over models currently used in
industrial workflows. | [
"cs.LG",
"stat.ML"
] |
Visual kinship recognition aims to identify blood relatives from facial
images. Its practical application-- like in law-enforcement, video
surveillance, automatic family album management, and more-- has motivated many
researchers to put forth effort on the topic as of recent. In this paper, we
focus on a new view of visual kinship technology: kin-based face generation.
Specifically, we propose a two-stage kin-face generation model to predict the
appearance of a child given a pair of parents. The first stage includes a deep
generative adversarial autoencoder conditioned on ages and genders to map
between facial appearance and high-level features. The second stage is our
proposed DNA-Net, which serves as a transformation between the deep and genetic
features based on a random selection process to fuse genes of a parent pair to
form the genes of a child. We demonstrate the effectiveness of the proposed
method quantitatively and qualitatively: quantitatively, pre-trained models and
human subjects perform kinship verification on the generated images of
children; qualitatively, we show photo-realistic face images of children that
closely resemble the given pair of parents. In the end, experiments validate
that the proposed model synthesizes convincing kin-faces using both subjective
and objective standards. | [
"cs.LG",
"cs.CV"
] |
Image inpainting task requires filling the corrupted image with contents
coherent with the context. This research field has achieved promising progress
by using neural image inpainting methods. Nevertheless, there is still a
critical challenge in guessing the missed content with only the context pixels.
The goal of this paper is to fill the semantic information in corrupted images
according to the provided descriptive text. Unique from existing text-guided
image generation works, the inpainting models are required to compare the
semantic content of the given text and the remaining part of the image, then
find out the semantic content that should be filled for missing part. To
fulfill such a task, we propose a novel inpainting model named Text-Guided Dual
Attention Inpainting Network (TDANet). Firstly, a dual multimodal attention
mechanism is designed to extract the explicit semantic information about the
corrupted regions, which is done by comparing the descriptive text and
complementary image areas through reciprocal attention. Secondly, an image-text
matching loss is applied to maximize the semantic similarity of the generated
image and the text. Experiments are conducted on two open datasets. Results
show that the proposed TDANet model reaches new state-of-the-art on both
quantitative and qualitative measures. Result analysis suggests that the
generated images are consistent with the guidance text, enabling the generation
of various results by providing different descriptions. Codes are available at
https://github.com/idealwhite/TDANet | [
"cs.CV",
"cs.CL"
] |
Deep learning based approaches have been utilized to model and generate
graphs subjected to different distributions recently. However, they are
typically unsupervised learning based and unconditioned generative models or
simply conditioned on the graph-level contexts, which are not associated with
rich semantic node-level contexts. Differently, in this paper, we are
interested in a novel problem named Time Series Conditioned Graph Generation:
given an input multivariate time series, we aim to infer a target relation
graph modeling the underlying interrelationships between time series with each
node corresponding to each time series. For example, we can study the
interrelationships between genes in a gene regulatory network of a certain
disease conditioned on their gene expression data recorded as time series. To
achieve this, we propose a novel Time Series conditioned Graph
Generation-Generative Adversarial Networks (TSGG-GAN) to handle challenges of
rich node-level context structures conditioning and measuring similarities
directly between graphs and time series. Extensive experiments on synthetic and
real-word gene regulatory networks datasets demonstrate the effectiveness and
generalizability of the proposed TSGG-GAN. | [
"cs.LG",
"cs.SI",
"stat.ML"
] |
Dynamic scene blurring is an important yet challenging topic. Recently, deep
learning methods have achieved impressive performance for dynamic scene
deblurring. However, the motion information contained in a blurry image has yet
to be fully explored and accurately formulated because: (i) the ground truth of
blurry motion is difficult to obtain; (ii) the temporal ordering is destroyed
during the exposure; and (iii) the motion estimation is highly ill-posed. By
revisiting the principle of camera exposure, dynamic blur can be described by
the relative motions of sharp content with respect to each exposed pixel. We
define exposure trajectories, which record the trajectories of relative motions
to represent the motion information contained in a blurry image and explain the
causes of the dynamic blur. A new blur representation, which we call motion
offset, is proposed to model pixel-wise displacements of the latent sharp image
at multiple timepoints. Under mild constraints, the learned motion offsets can
recover dense, (non-)linear exposure trajectories, which significantly reduce
temporal disorder and ill-posed problems. Finally, we demonstrate that the
estimated exposure trajectories can fit real-world dynamic blurs and further
contribute to motion-aware image deblurring and warping-based video extraction
from a single blurry image. | [
"cs.CV"
] |
We present a unified, efficient and effective framework for point-cloud based
3D object detection. Our two-stage approach utilizes both voxel representation
and raw point cloud data to exploit respective advantages. The first stage
network, with voxel representation as input, only consists of light
convolutional operations, producing a small number of high-quality initial
predictions. Coordinate and indexed convolutional feature of each point in
initial prediction are effectively fused with the attention mechanism,
preserving both accurate localization and context information. The second stage
works on interior points with their fused feature for further refining the
prediction. Our method is evaluated on KITTI dataset, in terms of both 3D and
Bird's Eye View (BEV) detection, and achieves state-of-the-arts with a 15FPS
detection rate. | [
"cs.CV"
] |
While Generative Adversarial Networks (GANs) show increasing performance and
the level of realism is becoming indistinguishable from natural images, this
also comes with high demands on data and computation. We show that
state-of-the-art GAN models -- such as they are being publicly released by
researchers and industry -- can be used for a range of applications beyond
unconditional image generation. We achieve this by an iterative scheme that
also allows gaining control over the image generation process despite the
highly non-linear latent spaces of the latest GAN models. We demonstrate that
this opens up the possibility to re-use state-of-the-art, difficult to train,
pre-trained GANs with a high level of control even if only black-box access is
granted. Our work also raises concerns and awareness that the use cases of a
published GAN model may well reach beyond the creators' intention, which needs
to be taken into account before a full public release. Code is available at
https://github.com/a514514772/hijackgan. | [
"cs.CV",
"cs.CR"
] |
While state of the art image segmentation models typically output
segmentations in raster format, applications in geographic information systems
often require vector polygons. To help bridge the gap between deep network
output and the format used in downstream tasks, we add a frame field output to
a deep segmentation model for extracting buildings from remote sensing images.
We train a deep neural network that aligns a predicted frame field to ground
truth contours. This additional objective improves segmentation quality by
leveraging multi-task learning and provides structural information that later
facilitates polygonization; we also introduce a polygonization algorithm that
utilizes the frame field along with the raster segmentation. Our code is
available at https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Fluorescence microscopy images contain several channels, each indicating a
marker staining the sample. Since many different marker combinations are
utilized in practice, it has been challenging to apply deep learning based
segmentation models, which expect a predefined channel combination for all
training samples as well as at inference for future application. Recent work
circumvents this problem using a modality attention approach to be effective
across any possible marker combination. However, for combinations that do not
exist in a labeled training dataset, one cannot have any estimation of
potential segmentation quality if that combination is encountered during
inference. Without this, not only one lacks quality assurance but one also does
not know where to put any additional imaging and labeling effort. We herein
propose a method to estimate segmentation quality on unlabeled images by (i)
estimating both aleatoric and epistemic uncertainties of convolutional neural
networks for image segmentation, and (ii) training a Random Forest model for
the interpretation of uncertainty features via regression to their
corresponding segmentation metrics. Additionally, we demonstrate that including
these uncertainty measures during training can provide an improvement on
segmentation performance. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Modeling time-evolving knowledge graphs (KGs) has recently gained increasing
interest. Here, graph representation learning has become the dominant paradigm
for link prediction on temporal KGs. However, the embedding-based approaches
largely operate in a black-box fashion, lacking the ability to interpret their
predictions. This paper provides a link forecasting framework that reasons over
query-relevant subgraphs of temporal KGs and jointly models the structural
dependencies and the temporal dynamics. Especially, we propose a temporal
relational attention mechanism and a novel reverse representation update scheme
to guide the extraction of an enclosing subgraph around the query. The subgraph
is expanded by an iterative sampling of temporal neighbors and by attention
propagation. Our approach provides human-understandable evidence explaining the
forecast. We evaluate our model on four benchmark temporal knowledge graphs for
the link forecasting task. While being more explainable, our model obtains a
relative improvement of up to 20% on Hits@1 compared to the previous best KG
forecasting method. We also conduct a survey with 53 respondents, and the
results show that the evidence extracted by the model for link forecasting is
aligned with human understanding. | [
"cs.LG",
"cs.AI"
] |
Is all of machine learning supervised to some degree? The field of machine
learning has traditionally been categorized pedagogically into
$supervised~vs~unsupervised~learning$; where supervised learning has typically
referred to learning from labeled data, while unsupervised learning has
typically referred to learning from unlabeled data. In this paper, we assert
that all machine learning is in fact supervised to some degree, and that the
scope of supervision is necessarily commensurate to the scope of learning
potential. In particular, we argue that clustering algorithms such as k-means,
and dimensionality reduction algorithms such as principal component analysis,
variational autoencoders, and deep belief networks are each internally
supervised by the data itself to learn their respective representations of its
features. Furthermore, these algorithms are not capable of external inference
until their respective outputs (clusters, principal components, or
representation codes) have been identified and externally labeled in effect. As
such, they do not suffice as examples of unsupervised learning. We propose that
the categorization `supervised vs unsupervised learning' be dispensed with, and
instead, learning algorithms be categorized as either
$internally~or~externally~supervised$ (or both). We believe this change in
perspective will yield new fundamental insights into the structure and
character of data and of learning algorithms. | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] |
Predicting vulnerable road user behavior is an essential prerequisite for
deploying Automated Driving Systems (ADS) in the real-world. Pedestrian
crossing intention should be recognized in real-time, especially for urban
driving. Recent works have shown the potential of using vision-based deep
neural network models for this task. However, these models are not robust and
certain issues still need to be resolved. First, the global spatio-temproal
context that accounts for the interaction between the target pedestrian and the
scene has not been properly utilized. Second, the optimum strategy for fusing
different sensor data has not been thoroughly investigated. This work addresses
the above limitations by introducing a novel neural network architecture to
fuse inherently different spatio-temporal features for pedestrian crossing
intention prediction. We fuse different phenomena such as sequences of RGB
imagery, semantic segmentation masks, and ego-vehicle speed in an optimum way
using attention mechanisms and a stack of recurrent neural networks. The
optimum architecture was obtained through exhaustive ablation and comparison
studies. Extensive comparative experiments on the JAAD pedestrian action
prediction benchmark demonstrate the effectiveness of the proposed method,
where state-of-the-art performance was achieved. Our code is open-source and
publicly available. | [
"cs.CV"
] |
Haze removal is important for computational photography and computer vision
applications. However, most of the existing methods for dehazing are designed
for daytime images, and cannot always work well in the nighttime. Different
from the imaging conditions in the daytime, images captured in nighttime haze
condition may suffer from non-uniform illumination due to artificial light
sources, which exhibit low brightness/contrast and color distortion. In this
paper, we present a new nighttime hazy imaging model that takes into account
both the non-uniform illumination from artificial light sources and the
scattering and attenuation effects of haze. Accordingly, we propose an
efficient dehazing algorithm for nighttime hazy images. The proposed algorithm
includes three sequential steps. i) It enhances the overall brightness by
performing a gamma correction step after estimating the illumination from the
original image. ii) Then it achieves a color-balance result by performing a
color correction step after estimating the color characteristics of the
incident light. iii) Finally, it remove the haze effect by applying the dark
channel prior and estimating the point-wise environmental light based on the
previous illumination-balance result. Experimental results show that the
proposed algorithm can achieve illumination-balance and haze-free results with
good color rendition ability. | [
"cs.CV"
] |
Imitation from observation (IfO) is the problem of learning directly from
state-only demonstrations without having access to the demonstrator's actions.
The lack of action information both distinguishes IfO from most of the
literature in imitation learning, and also sets it apart as a method that may
enable agents to learn from a large set of previously inapplicable resources
such as internet videos. In this paper, we propose both a general framework for
IfO approaches and also a new IfO approach based on generative adversarial
networks called generative adversarial imitation from observation (GAIfO). We
conduct experiments in two different settings: (1) when demonstrations consist
of low-dimensional, manually-defined state features, and (2) when
demonstrations consist of high-dimensional, raw visual data. We demonstrate
that our approach performs comparably to classical imitation learning
approaches (which have access to the demonstrator's actions) and significantly
outperforms existing imitation from observation methods in high-dimensional
simulation environments. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Self-supervised contrastive representation learning has proved incredibly
successful in the vision and natural language domains, enabling
state-of-the-art performance with orders of magnitude less labeled data.
However, such methods are domain-specific and little has been done to leverage
this technique on real-world tabular datasets. We propose SCARF, a simple,
widely-applicable technique for contrastive learning, where views are formed by
corrupting a random subset of features. When applied to pre-train deep neural
networks on the 69 real-world, tabular classification datasets from the
OpenML-CC18 benchmark, SCARF not only improves classification accuracy in the
fully-supervised setting but does so also in the presence of label noise and in
the semi-supervised setting where only a fraction of the available training
data is labeled. We show that SCARF complements existing strategies and
outperforms alternatives like autoencoders. We conduct comprehensive ablations,
detailing the importance of a range of factors. | [
"cs.LG",
"cs.AI"
] |
We present an approach for constructing a surrogate from ensembles of
information sources of varying cost and accuracy. The multifidelity surrogate
encodes connections between information sources as a directed acyclic graph,
and is trained via gradient-based minimization of a nonlinear least squares
objective. While the vast majority of state-of-the-art assumes hierarchical
connections between information sources, our approach works with flexibly
structured information sources that may not admit a strict hierarchy. The
formulation has two advantages: (1) increased data efficiency due to
parsimonious multifidelity networks that can be tailored to the application;
and (2) no constraints on the training data -- we can combine noisy, non-nested
evaluations of the information sources. Numerical examples ranging from
synthetic to physics-based computational mechanics simulations indicate the
error in our approach can be orders-of-magnitude smaller, particularly in the
low-data regime, than single-fidelity and hierarchical multifidelity
approaches. | [
"cs.LG",
"stat.ML",
"62J02, 65D15, 41A10"
] |
3D point clouds are often perturbed by noise due to the inherent limitation
of acquisition equipments, which obstructs downstream tasks such as surface
reconstruction, rendering and so on. Previous works mostly infer the
displacement of noisy points from the underlying surface, which however are not
designated to recover the surface explicitly and may lead to sub-optimal
denoising results. To this end, we propose to learn the underlying manifold of
a noisy point cloud from differentiably subsampled points with trivial noise
perturbation and their embedded neighborhood feature, aiming to capture
intrinsic structures in point clouds. Specifically, we present an
autoencoder-like neural network. The encoder learns both local and non-local
feature representations of each point, and then samples points with low noise
via an adaptive differentiable pooling operation. Afterwards, the decoder
infers the underlying manifold by transforming each sampled point along with
the embedded feature of its neighborhood to a local surface centered around the
point. By resampling on the reconstructed manifold, we obtain a denoised point
cloud. Further, we design an unsupervised training loss, so that our network
can be trained in either an unsupervised or supervised fashion. Experiments
show that our method significantly outperforms state-of-the-art denoising
methods under both synthetic noise and real world noise. The code and data are
available at https://github.com/luost26/DMRDenoise | [
"cs.CV"
] |
Single-stage object detectors have been widely applied in computer vision
applications due to their high efficiency. However, we find that the loss
functions adopted by single-stage object detectors hurt the localization
accuracy seriously. Firstly, the standard cross-entropy loss for classification
is independent of the localization task and drives all the positive examples to
learn as high classification scores as possible regardless of localization
accuracy during training. As a result, there will be many detections that have
high classification scores but low IoU or detections that have low
classification scores but high IoU. Secondly, for the standard smooth L1 loss,
the gradient is dominated by the outliers that have poor localization accuracy
during training. The above two problems will decrease the localization accuracy
of single-stage detectors. In this work, IoU-balanced loss functions that
consist of IoU-balanced classification loss and IoU-balanced localization loss
are proposed to solve the above problems. The IoU-balanced classification loss
pays more attention to positive examples with high IoU and can enhance the
correlation between classification and localization tasks. The IoU-balanced
localization loss decreases the gradient of examples with low IoU and increases
the gradient of examples with high IoU, which can improve the localization
accuracy of models. Extensive experiments on challenging public datasets such
as MS COCO, PASCAL VOC and Cityscapes demonstrate that both IoU-balanced losses
can bring substantial improvement for the popular single-stage detectors,
especially for the localization accuracy. On COCO test-dev, the proposed
methods can substantially improve AP by $1.0\%\sim1.7\%$ and AP75 by
$1.0\%\sim2.4\%$. On PASCAL VOC, it can also substantially improve AP by
$1.3\%\sim1.5\%$ and AP80, AP90 by $1.6\%\sim3.9\%$. | [
"cs.CV"
] |
Color image segmentation is an important topic in the image processing field.
MRF-MAP is often adopted in the unsupervised segmentation methods, but their
performance are far behind recent interactive segmentation tools supervised by
user inputs. Furthermore, the existing related unsupervised methods also suffer
from the low efficiency, and high risk of being trapped in the local optima,
because MRF-MAP is currently solved by iterative frameworks with inaccurate
initial color distribution models. To address these problems, the letter
designs an efficient method to calculate the energy functions approximately in
the non-iteration style, and proposes a new binary segmentation algorithm based
on the slightly tuned Lanczos eigensolver. The experiments demonstrate that the
new algorithm achieves competitive performance compared with two state-of-art
segmentation methods. | [
"cs.CV"
] |
Full attention, which generates an attention value per element of the input
feature maps, has been successfully demonstrated to be beneficial in visual
tasks. In this work, we propose a fully attentional network, termed {\it
channel recurrent attention network}, for the task of video pedestrian
retrieval. The main attention unit, \textit{channel recurrent attention},
identifies attention maps at the frame level by jointly leveraging spatial and
channel patterns via a recurrent neural network. This channel recurrent
attention is designed to build a global receptive field by recurrently
receiving and learning the spatial vectors. Then, a \textit{set aggregation}
cell is employed to generate a compact video representation. Empirical
experimental results demonstrate the superior performance of the proposed deep
network, outperforming current state-of-the-art results across standard video
person retrieval benchmarks, and a thorough ablation study shows the
effectiveness of the proposed units. | [
"cs.CV",
"cs.LG"
] |
Current value-based multi-agent reinforcement learning methods optimize
individual Q values to guide individuals' behaviours via centralized training
with decentralized execution (CTDE). However, such expected, i.e.,
risk-neutral, Q value is not sufficient even with CTDE due to the randomness of
rewards and the uncertainty in environments, which causes the failure of these
methods to train coordinating agents in complex environments. To address these
issues, we propose RMIX, a novel cooperative MARL method with the Conditional
Value at Risk (CVaR) measure over the learned distributions of individuals' Q
values. Specifically, we first learn the return distributions of individuals to
analytically calculate CVaR for decentralized execution. Then, to handle the
temporal nature of the stochastic outcomes during executions, we propose a
dynamic risk level predictor for risk level tuning. Finally, we optimize the
CVaR policies with CVaR values used to estimate the target in TD error during
centralized training and the CVaR values are used as auxiliary local rewards to
update the local distribution via Quantile Regression loss. Empirically, we
show that our method significantly outperforms state-of-the-art methods on
challenging StarCraft II tasks, demonstrating enhanced coordination and
improved sample efficiency. | [
"cs.LG",
"cs.MA"
] |
Most practical recommender systems focus on estimating immediate user
engagement without considering the long-term effects of recommendations on user
behavior. Reinforcement learning (RL) methods offer the potential to optimize
recommendations for long-term user engagement. However, since users are often
presented with slates of multiple items - which may have interacting effects on
user choice - methods are required to deal with the combinatorics of the RL
action space. In this work, we address the challenge of making slate-based
recommendations to optimize long-term value using RL. Our contributions are
three-fold. (i) We develop SLATEQ, a decomposition of value-based
temporal-difference and Q-learning that renders RL tractable with slates. Under
mild assumptions on user choice behavior, we show that the long-term value
(LTV) of a slate can be decomposed into a tractable function of its component
item-wise LTVs. (ii) We outline a methodology that leverages existing myopic
learning-based recommenders to quickly develop a recommender that handles LTV.
(iii) We demonstrate our methods in simulation, and validate the scalability of
decomposed TD-learning using SLATEQ in live experiments on YouTube. | [
"cs.LG",
"cs.AI",
"cs.IR",
"stat.ML"
] |
Cardiovascular diseases and their associated disorder of heart failure are
one of the major death causes globally, being a priority for doctors to detect
and predict its onset and medical consequences. Artificial Intelligence (AI)
allows doctors to discover clinical indicators and enhance their diagnosis and
treatments. Specifically, explainable AI offers tools to improve the clinical
prediction models that experience poor interpretability of their results. This
work presents an explainability analysis and evaluation of a prediction model
for heart failure survival by using a dataset that comprises 299 patients who
suffered heart failure. The model employs a data workflow pipeline able to
select the best ensemble tree algorithm as well as the best feature selection
technique. Moreover, different post-hoc techniques have been used for the
explainability analysis of the model. The paper's main contribution is an
explainability-driven approach to select the best prediction model for HF
survival based on an accuracy-explainability balance. Therefore, the most
balanced explainable prediction model implements an Extra Trees classifier over
5 selected features (follow-up time, serum creatinine, ejection fraction, age
and diabetes) out of 12, achieving a balanced-accuracy of 85.1% and 79.5% with
cross-validation and new unseen data respectively. The follow-up time is the
most influencing feature followed by serum-creatinine and ejection-fraction.
The explainable prediction model for HF survival presented in this paper would
improve a further adoption of clinical prediction models by providing doctors
with intuitions to better understand the reasoning of, usually, black-box AI
clinical solutions, and make more reasonable and data-driven decisions. | [
"cs.LG",
"cs.AI"
] |
We propose a way to learn visual features that are compatible with previously
computed ones even when they have different dimensions and are learned via
different neural network architectures and loss functions. Compatible means
that, if such features are used to compare images, then "new" features can be
compared directly to "old" features, so they can be used interchangeably. This
enables visual search systems to bypass computing new features for all
previously seen images when updating the embedding models, a process known as
backfilling. Backward compatibility is critical to quickly deploy new embedding
models that leverage ever-growing large-scale training datasets and
improvements in deep learning architectures and training methods. We propose a
framework to train embedding models, called backward-compatible training (BCT),
as a first step towards backward compatible representation learning. In
experiments on learning embeddings for face recognition, models trained with
BCT successfully achieve backward compatibility without sacrificing accuracy,
thus enabling backfill-free model updates of visual embeddings. | [
"cs.CV"
] |
Many current deep learning approaches make extensive use of backbone networks
pre-trained on large datasets like ImageNet, which are then fine-tuned to
perform a certain task. In remote sensing, the lack of comparable large
annotated datasets and the wide diversity of sensing platforms impedes similar
developments. In order to contribute towards the availability of pre-trained
backbone networks in remote sensing, we devise a self-supervised approach for
pre-training deep neural networks. By exploiting the correspondence between
geo-tagged audio recordings and remote sensing imagery, this is done in a
completely label-free manner, eliminating the need for laborious manual
annotation. For this purpose, we introduce the SoundingEarth dataset, which
consists of co-located aerial imagery and audio samples all around the world.
Using this dataset, we then pre-train ResNet models to map samples from both
modalities into a common embedding space, which encourages the models to
understand key properties of a scene that influence both visual and auditory
appearance. To validate the usefulness of the proposed approach, we evaluate
the transfer learning performance of pre-trained weights obtained against
weights obtained through other means. By fine-tuning the models on a number of
commonly used remote sensing datasets, we show that our approach outperforms
existing pre-training strategies for remote sensing imagery. The dataset, code
and pre-trained model weights will be available at
https://github.com/khdlr/SoundingEarth. | [
"cs.CV"
] |
The vector autoregressive (VAR) model is a powerful tool in modeling complex
time series and has been exploited in many fields. However, fitting high
dimensional VAR model poses some unique challenges: On one hand, the
dimensionality, caused by modeling a large number of time series and higher
order autoregressive processes, is usually much higher than the time series
length; On the other hand, the temporal dependence structure in the VAR model
gives rise to extra theoretical challenges. In high dimensions, one popular
approach is to assume the transition matrix is sparse and fit the VAR model
using the "least squares" method with a lasso-type penalty. In this manuscript,
we propose an alternative way in estimating the VAR model. The main idea is,
via exploiting the temporal dependence structure, to formulate the estimating
problem into a linear program. There is instant advantage for the proposed
approach over the lasso-type estimators: The estimation equation can be
decomposed into multiple sub-equations and accordingly can be efficiently
solved in a parallel fashion. In addition, our method brings new theoretical
insights into the VAR model analysis. So far the theoretical results developed
in high dimensions (e.g., Song and Bickel (2011) and Kock and Callot (2012))
mainly pose assumptions on the design matrix of the formulated regression
problems. Such conditions are indirect about the transition matrices and not
transparent. In contrast, our results show that the operator norm of the
transition matrices plays an important role in estimation accuracy. We provide
explicit rates of convergence for both estimation and prediction. In addition,
we provide thorough experiments on both synthetic and real-world equity data to
show that there are empirical advantages of our method over the lasso-type
estimators in both parameter estimation and forecasting. | [
"stat.ML"
] |
Selective weeding is one of the key challenges in the field of agriculture
robotics. To accomplish this task, a farm robot should be able to accurately
detect plants and to distinguish them between crop and weeds. Most of the
promising state-of-the-art approaches make use of appearance-based models
trained on large annotated datasets. Unfortunately, creating large agricultural
datasets with pixel-level annotations is an extremely time consuming task,
actually penalizing the usage of data-driven techniques. In this paper, we face
this problem by proposing a novel and effective approach that aims to
dramatically minimize the human intervention needed to train the detection and
classification algorithms. The idea is to procedurally generate large synthetic
training datasets randomizing the key features of the target environment (i.e.,
crop and weed species, type of soil, light conditions). More specifically, by
tuning these model parameters, and exploiting a few real-world textures, it is
possible to render a large amount of realistic views of an artificial
agricultural scenario with no effort. The generated data can be directly used
to train the model or to supplement real-world images. We validate the proposed
methodology by using as testbed a modern deep learning based image segmentation
architecture. We compare the classification results obtained using both real
and synthetic images as training data. The reported results confirm the
effectiveness and the potentiality of our approach. | [
"cs.CV",
"cs.RO"
] |
Many applications of deep learning for image generation use perceptual losses
for either training or fine-tuning of the generator networks. The use of
perceptual loss however incurs repeated forward-backward passes in a large
image classification network as well as a considerable memory overhead required
to store the activations of this network. It is therefore desirable or
sometimes even critical to get rid of these overheads.
In this work, we propose a way to train generator networks using
approximations of perceptual loss that are computed without forward-backward
passes. Instead, we use a simpler perceptual gradient network that directly
synthesizes the gradient field of a perceptual loss. We introduce the concept
of proxy targets, which stabilize the predicted gradient, meaning that learning
with it does not lead to divergence or oscillations. In addition, our method
allows interpretation of the predicted gradient, providing insight into the
internals of perceptual loss and suggesting potential ways to improve it in
future work. | [
"cs.LG",
"cs.CV"
] |
A growing number of commercially available mobile phones come with integrated
high-resolution digital cameras. That enables a new class of dedicated
applications to image analysis such as mobile visual search, image cropping,
object detection, content-based image retrieval, image classification. In this
paper, a new mobile application for image content retrieval and classification
for mobile device display is proposed to enrich the visual experience of users.
The mobile application can extract a certain number of images based on the
content of an image with visual saliency methods aiming at detecting the most
critical regions in a given image from a perceptual viewpoint. First, the most
critical areas from a perceptual perspective are extracted using the local
maxima of a 2D saliency function. Next, a salient region is cropped using the
bounding box centred on the local maxima of the thresholded Saliency Map of the
image. Then, each image crop feds into an Image Classification system based on
SVM and SIFT descriptors to detect the class of object present in the image.
ImageNet repository was used as the reference for semantic category
classification. Android platform was used to implement the mobile application
on a client-server architecture. A mobile client sends the photo taken by the
camera to the server, which processes the image and returns the results (image
contents such as image crops and related target classes) to the mobile client.
The application was run on thousands of pictures and showed encouraging results
towards a better user visual experience with mobile displays. | [
"cs.CV",
"cs.LG",
"I.4; I.5"
] |
Nowadays research has expanded to extracting auxiliary information from
various biometric techniques like fingerprints, face, iris, palm and voice .
This information contains some major features like gender, age, beard,
mustache, scars, height, hair, skin color, glasses, weight, facial marks and
tattoos. All this information contributes strongly to identification of human.
The major challenges that come across face recognition are to find age & gender
of the person. This paper contributes a survey of various face recognition
techniques for finding the age and gender. The existing techniques are
discussed based on their performances. This paper also provides future
directions for further research. | [
"cs.CV"
] |
Retinal image quality assessment is an essential prerequisite for diagnosis
of retinal diseases. Its goal is to identify retinal images in which anatomic
structures and lesions attracting ophthalmologists' attention most are
exhibited clearly and definitely while reject poor quality fundus images.
Motivated by this, we mimic the way that ophthalmologists assess the quality of
retinal images and propose a method termed SalStructuIQA. First, two salient
structures for automated retinal quality assessment. One is the large-size
salient structures including optic disc region and exudates in large-size. The
other is the tiny-size salient structures which mainly include vessels. Then we
incorporate the proposed two salient structure priors with deep convolutional
neural network (CNN) to shift the focus of CNN to salient structures.
Accordingly, we develop two CNN architectures: Dual-branch SalStructIQA and
Single-branch SalStructIQA. Dual-branch SalStructIQA contains two CNN branches
and one is guided by large-size salient structures while the other is guided by
tiny-size salient structures. Single-branch SalStructIQA contains one CNN
branch, which is guided by the concatenation of salient structures in both
large-size and tiny-size. Experimental results on Eye-Quality dataset show that
our proposed Dual-branch SalStructIQA outperforms the state-of-the-art methods
for retinal image quality assessment and Single-branch SalStructIQA is much
light-weight comparing with state-of-the-art deep retinal image quality
assessment methods and still achieves competitive performances. | [
"cs.CV"
] |
The vulnerability of deep neural networks (DNNs) to adversarial examples has
drawn great attention from the community. In this paper, we study the
transferability of such examples, which lays the foundation of many black-box
attacks on DNNs. We revisit a not so new but definitely noteworthy hypothesis
of Goodfellow et al.'s and disclose that the transferability can be enhanced by
improving the linearity of DNNs in an appropriate manner. We introduce linear
backpropagation (LinBP), a method that performs backpropagation in a more
linear fashion using off-the-shelf attacks that exploit gradients. More
specifically, it calculates forward as normal but backpropagates loss as if
some nonlinear activations are not encountered in the forward pass.
Experimental results demonstrate that this simple yet effective method
obviously outperforms current state-of-the-arts in crafting transferable
adversarial examples on CIFAR-10 and ImageNet, leading to more effective
attacks on a variety of DNNs. | [
"cs.LG",
"cs.CR",
"cs.CV"
] |
We propose a novel neural network architecture, named the Global Workspace
Network (GWN), which addresses the challenge of dynamic and unspecified
uncertainties in multimodal data fusion. Our GWN is a model of attention across
modalities and evolving through time, and is inspired by the well-established
Global Workspace Theory from the field of cognitive science. The GWN achieved
average F1 score of 0.92 for discrimination between pain patients and healthy
participants and average F1 score = 0.75 for further classification of three
pain levels for a patient, both based on the multimodal EmoPain dataset
captured from people with chronic pain and healthy people performing different
types of exercise movements in unconstrained settings. In these tasks, the GWN
significantly outperforms the typical fusion approach of merging by
concatenation. We further provide extensive analysis of the behaviour of the
GWN and its ability to address uncertainties (hidden noise) in multimodal data. | [
"cs.LG",
"stat.ML"
] |
Deep neural network pruning and quantization techniques have demonstrated it
is possible to achieve high levels of compression with surprisingly little
degradation to test set accuracy. However, this measure of performance conceals
significant differences in how different classes and images are impacted by
model compression techniques. We find that models with radically different
numbers of weights have comparable top-line performance metrics but diverge
considerably in behavior on a narrow subset of the dataset. This small subset
of data points, which we term Pruning Identified Exemplars (PIEs) are
systematically more impacted by the introduction of sparsity. Compression
disproportionately impacts model performance on the underrepresented long-tail
of the data distribution. PIEs over-index on atypical or noisy images that are
far more challenging for both humans and algorithms to classify. Our work
provides intuition into the role of capacity in deep neural networks and the
trade-offs incurred by compression. An understanding of this disparate impact
is critical given the widespread deployment of compressed models in the wild. | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.HC",
"stat.ML"
] |
This work explores how to design a single neural network that is capable of
adapting to multiple heterogeneous tasks of computer vision, such as image
segmentation, 3D detection, and video recognition. This goal is challenging
because network architecture designs in different tasks are inconsistent. We
solve this challenge by proposing Network Coding Propagation (NCP), a novel
"neural predictor", which is able to predict an architecture's performance in
multiple datasets and tasks. Unlike prior arts of neural architecture search
(NAS) that typically focus on a single task, NCP has several unique benefits.
(1) NCP can be trained on different NAS benchmarks, such as NAS-Bench-201 and
NAS-Bench-MR, which contains a novel network space designed by us for jointly
searching an architecture among multiple tasks, including ImageNet, Cityscapes,
KITTI, and HMDB51. (2) NCP learns from network codes but not original data,
enabling it to update the architecture efficiently across datasets. (3)
Extensive experiments evaluate NCP on object classification, detection,
segmentation, and video recognition. For example, with 17\% fewer FLOPs, a
single architecture returned by NCP achieves 86\% and 77.16\% on
ImageNet-50-1000 and Cityscapes respectively, outperforming its counterparts.
More interestingly, NCP enables a single architecture applicable to both image
segmentation and video recognition, which achieves competitive performance on
both HMDB51 and ADE20K compared to the singular counterparts. Code is available
at https://github.com/dingmyu/NCP}{https://github.com/dingmyu/NCP. | [
"cs.CV"
] |
Color Constancy is the ability of the human visual system to perceive colors
unchanged independently of the illumination. Giving a machine this feature will
be beneficial in many fields where chromatic information is used. Particularly,
it significantly improves scene understanding and object recognition. In this
paper, we propose transfer learning-based algorithm, which has two main
features: accuracy higher than many state-of-the-art algorithms and simplicity
of implementation. Despite the fact that GoogLeNet was used in the experiments,
given approach may be applied to any CNN. Additionally, we discuss design of a
new loss function oriented specifically to this problem, and propose a few the
most suitable options. | [
"cs.CV"
] |
Object Detection (OD) is an important computer vision problem for industry,
which can be used for quality control in the production lines, among other
applications. Recently, Deep Learning (DL) methods have enabled practitioners
to train OD models performing well on complex real world images. However, the
adoption of these models in industry is still limited by the difficulty and the
significant cost of collecting high quality training datasets. On the other
hand, when applying OD to the context of production lines, CAD models of the
objects to be detected are often available. In this paper, we introduce a fully
automated method that uses a CAD model of an object and returns a fully trained
OD model for detecting this object. To do this, we created a Blender script
that generates realistic labeled datasets of images containing the object,
which are then used for training the OD model. The method is validated
experimentally on two practical examples, showing that this approach can
generate OD models performing well on real images, while being trained only on
synthetic images. The proposed method has potential to facilitate the adoption
of object detection models in industry as it is easy to adapt for new objects
and highly flexible. Hence, it can result in significant costs reduction, gains
in productivity and improved products quality. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
In face-related applications with a public available dataset, synthesizing
non-linear facial variations (e.g., facial expression, head-pose, illumination,
etc.) through a generative model is helpful in addressing the lack of training
data. In reality, however, there is insufficient data to even train the
generative model for face synthesis. In this paper, we propose Differential
Generative Adversarial Networks (D-GAN) that can perform photo-realistic face
synthesis even when training data is small. Two discriminators are devised to
ensure the generator to approximate a face manifold, which can express face
changes as it wants. Experimental results demonstrate that the proposed method
is robust to the amount of training data and synthesized images are useful to
improve the performance of a face expression classifier. | [
"cs.CV"
] |
Data augmentation is a key element of deep learning pipelines, as it informs
the network during training about transformations of the input data that keep
the label unchanged. Manually finding adequate augmentation methods and
parameters for a given pipeline is however rapidly cumbersome. In particular,
while intuition can guide this decision for images, the design and choice of
augmentation policies remains unclear for more complex types of data, such as
neuroscience signals. Moreover, label independent strategies might not be
suitable for such structured data and class-dependent augmentations might be
necessary. This idea has been surprisingly unexplored in the literature, while
it is quite intuitive: changing the color of a car image does not change the
object class to be predicted, but doing the same to the picture of an orange
does. This paper aims to increase the generalization power added through
class-wise data augmentation. Yet, as seeking transformations depending on the
class largely increases the complexity of the task, using gradient-free
optimization techniques as done by most existing automatic approaches becomes
intractable for real-world datasets. For this reason we propose to use
differentiable data augmentation amenable to gradient-based learning. EEG
signals are a perfect example of data for which good augmentation policies are
mostly unknown. In this work, we demonstrate the relevance of our approach on
the clinically relevant sleep staging classification task, for which we also
propose differentiable transformations. | [
"cs.LG"
] |
Adverse weather conditions such as haze and rain corrupt the quality of
captured images, which cause detection networks trained on clean images to
perform poorly on these images. To address this issue, we propose an
unsupervised prior-based domain adversarial object detection framework for
adapting the detectors to hazy and rainy conditions. In particular, we use
weather-specific prior knowledge obtained using the principles of image
formation to define a novel prior-adversarial loss. The prior-adversarial loss
used to train the adaptation process aims to reduce the weather-specific
information in the features, thereby mitigating the effects of weather on the
detection performance. Additionally, we introduce a set of residual feature
recovery blocks in the object detection pipeline to de-distort the feature
space, resulting in further improvements. Evaluations performed on various
datasets (Foggy-Cityscapes, Rainy-Cityscapes, RTTS and UFDD) for rainy and hazy
conditions demonstrates the effectiveness of the proposed approach. | [
"cs.CV"
] |
We aim to address the problem of Natural Language Video Localization
(NLVL)-localizing the video segment corresponding to a natural language
description in a long and untrimmed video. State-of-the-art NLVL methods are
almost in one-stage fashion, which can be typically grouped into two
categories: 1) anchor-based approach: it first pre-defines a series of video
segment candidates (e.g., by sliding window), and then does classification for
each candidate; 2) anchor-free approach: it directly predicts the probabilities
for each video frame as a boundary or intermediate frame inside the positive
segment. However, both kinds of one-stage approaches have inherent drawbacks:
the anchor-based approach is susceptible to the heuristic rules, further
limiting the capability of handling videos with variant length. While the
anchor-free approach fails to exploit the segment-level interaction thus
achieving inferior results. In this paper, we propose a novel Boundary Proposal
Network (BPNet), a universal two-stage framework that gets rid of the issues
mentioned above. Specifically, in the first stage, BPNet utilizes an
anchor-free model to generate a group of high-quality candidate video segments
with their boundaries. In the second stage, a visual-language fusion layer is
proposed to jointly model the multi-modal interaction between the candidate and
the language query, followed by a matching score rating layer that outputs the
alignment score for each candidate. We evaluate our BPNet on three challenging
NLVL benchmarks (i.e., Charades-STA, TACoS and ActivityNet-Captions). Extensive
experiments and ablative studies on these datasets demonstrate that the BPNet
outperforms the state-of-the-art methods. | [
"cs.CV"
] |
We introduce a Bayesian Gaussian process latent variable model that
explicitly captures spatial correlations in data using a parameterized spatial
kernel and leveraging structure-exploiting algebra on the model covariance
matrices for computational tractability. Inference is made tractable through a
collapsed variational bound with similar computational complexity to that of
the traditional Bayesian GP-LVM. Inference over partially-observed test cases
is achieved by optimizing a "partially-collapsed" bound. Modeling
high-dimensional time series systems is enabled through use of a dynamical GP
latent variable prior. Examples imputing missing data on images and
super-resolution imputation of missing video frames demonstrate the model. | [
"stat.ML",
"cs.LG"
] |
Reinforcement learning (RL) has achieved tremendous success as a general
framework for learning how to make decisions. However, this success relies on
the interactive hand-tuning of a reward function by RL experts. On the other
hand, inverse reinforcement learning (IRL) seeks to learn a reward function
from readily-obtained human demonstrations. Yet, IRL suffers from two major
limitations: 1) reward ambiguity - there are an infinite number of possible
reward functions that could explain an expert's demonstration and 2)
heterogeneity - human experts adopt varying strategies and preferences, which
makes learning from multiple demonstrators difficult due to the common
assumption that demonstrators seeks to maximize the same reward. In this work,
we propose a method to jointly infer a task goal and humans' strategic
preferences via network distillation. This approach enables us to distill a
robust task reward (addressing reward ambiguity) and to model each strategy's
objective (handling heterogeneity). We demonstrate our algorithm can better
recover task reward and strategy rewards and imitate the strategies in two
simulated tasks and a real-world table tennis task. | [
"cs.LG",
"cs.AI",
"cs.RO",
"stat.ML"
] |
Accurate segmentation of critical anatomical structures is at the core of
medical image analysis. The main bottleneck lies in gathering the requisite
expert-labeled image annotations in a scalable manner. Methods that permit to
produce accurate anatomical structure segmentation without using a large amount
of fully annotated training images are highly desirable. In this work, we
propose a novel contribution of Contour Transformer Network (CTN), a one-shot
anatomy segmentor including a naturally built-in human-in-the-loop mechanism.
Segmentation is formulated by learning a contour evolution behavior process
based on graph convolutional networks (GCNs). Training of our CTN model
requires only one labeled image exemplar and leverages additional unlabeled
data through newly introduced loss functions that measure the global shape and
appearance consistency of contours. We demonstrate that our one-shot learning
method significantly outperforms non-learning-based methods and performs
competitively to the state-of-the-art fully supervised deep learning
approaches. With minimal human-in-the-loop editing feedback, the segmentation
performance can be further improved and tailored towards the observer desired
outcomes. This can facilitate the clinician designed imaging-based biomarker
assessments (to support personalized quantitative clinical diagnosis) and
outperforms fully supervised baselines. | [
"cs.CV"
] |
Weakly-supervised salient object detection (WSOD) aims to develop saliency
models using image-level annotations. Despite of the success of previous works,
explorations on an effective training strategy for the saliency network and
accurate matches between image-level annotations and salient objects are still
inadequate. In this work, 1) we propose a self-calibrated training strategy by
explicitly establishing a mutual calibration loop between pseudo labels and
network predictions, liberating the saliency network from error-prone
propagation caused by pseudo labels. 2) we prove that even a much smaller
dataset (merely 1.8% of ImageNet) with well-matched annotations can facilitate
models to achieve better performance as well as generalizability. This sheds
new light on the development of WSOD and encourages more contributions to the
community. Comprehensive experiments demonstrate that our method outperforms
all the existing WSOD methods by adopting the self-calibrated strategy only.
Steady improvements are further achieved by training on the proposed dataset.
Additionally, our method achieves 94.7% of the performance of fully-supervised
methods on average. And what is more, the fully supervised models adopting our
predicted results as "ground truths" achieve successful results (95.6% for
BASNet and 97.3% for ITSD on F-measure), while costing only 0.32% of labeling
time for pixel-level annotation. | [
"cs.CV"
] |
Local learning methods are a popular class of machine learning algorithms.
The basic idea for the entire cadre is to choose some non-local model family,
to train many of them on small sections of neighboring data, and then to
`stitch' the resulting models together in some way. Due to the limits of
constraining a training dataset to a small neighborhood, research on
locally-learned models has largely been restricted to simple model families.
Also, since simple model families have no complex structure by design, this has
limited use of the individual local models to predictive tasks. We hypothesize
that, using a sufficiently complex local model family, various properties of
the individual local models, such as their learned parameters, can be used as
features for further learning. This dissertation improves upon the current
state of research and works toward establishing this hypothesis by
investigating algorithms for localization of more complex model families and by
studying their applications beyond predictions as a feature extraction
mechanism. We summarize this generic technique of using local models as a
feature extraction step with the term ``local model feature transformations.''
In this document, we extend the local modeling paradigm to Gaussian processes,
orthogonal quadric models and word embedding models, and extend the existing
theory for localized linear classifiers. We then demonstrate applications of
local model feature transformations to epileptic event classification from EEG
readings, activity monitoring via chest accelerometry, 3D surface
reconstruction, 3D point cloud segmentation, handwritten digit classification
and event detection from Twitter feeds. | [
"cs.LG",
"stat.ML"
] |
When a population exhibits heterogeneity, we often model it via a finite
mixture: decompose it into several different but homogeneous subpopulations.
Contemporary practice favors learning the mixtures by maximizing the likelihood
for statistical efficiency and the convenient EM-algorithm for numerical
computation. Yet the maximum likelihood estimate (MLE) is not well defined for
the most widely used finite normal mixture in particular and for finite
location-scale mixture in general. We hence investigate feasible alternatives
to MLE such as minimum distance estimators. Recently, the Wasserstein distance
has drawn increased attention in the machine learning community. It has
intuitive geometric interpretation and is successfully employed in many new
applications. Do we gain anything by learning finite location-scale mixtures
via a minimum Wasserstein distance estimator (MWDE)? This paper investigates
this possibility in several respects. We find that the MWDE is consistent and
derive a numerical solution under finite location-scale mixtures. We study its
robustness against outliers and mild model mis-specifications. Our moderate
scaled simulation study shows the MWDE suffers some efficiency loss against a
penalized version of MLE in general without noticeable gain in robustness. We
reaffirm the general superiority of the likelihood based learning strategies
even for the non-regular finite location-scale mixtures. | [
"stat.ML",
"cs.LG"
] |
Squeeze-and-Excitation (SE) block presents a channel attention mechanism for
modeling global context via explicitly capturing dependencies across channels.
However, we are still far from understanding how the SE block works. In this
work, we first revisit the SE block, and then present a detailed empirical
study of the relationship between global context and attention distribution,
based on which we propose a simple yet effective module, called Linear Context
Transform (LCT) block. We divide all channels into different groups and
normalize the globally aggregated context features within each channel group,
reducing the disturbance from irrelevant channels. Through linear transform of
the normalized context features, we model global context for each channel
independently. The LCT block is extremely lightweight and easy to be plugged
into different backbone models while with negligible parameters and
computational burden increase. Extensive experiments show that the LCT block
outperforms the SE block in image classification task on the ImageNet and
object detection/segmentation on the COCO dataset with different backbone
models. Moreover, LCT yields consistent performance gains over existing
state-of-the-art detection architectures, e.g., 1.5$\sim$1.7% AP$^{bbox}$ and
1.0$\sim$1.2% AP$^{mask}$ improvements on the COCO benchmark, irrespective of
different baseline models of varied capacities. We hope our simple yet
effective approach will shed some light on future research of attention-based
models. | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
Faced with massive data, is it possible to trade off (statistical) risk, and
(computational) space and time? This challenge lies at the heart of large-scale
machine learning. Using k-means clustering as a prototypical unsupervised
learning problem, we show how we can strategically summarize the data (control
space) in order to trade off risk and time when data is generated by a
probabilistic model. Our summarization is based on coreset constructions from
computational geometry. We also develop an algorithm, TRAM, to navigate the
space/time/data/risk tradeoff in practice. In particular, we show that for a
fixed risk (or data size), as the data size increases (resp. risk increases)
the running time of TRAM decreases. Our extensive experiments on real data sets
demonstrate the existence and practical utility of such tradeoffs, not only for
k-means but also for Gaussian Mixture Models. | [
"stat.ML",
"cs.LG"
] |
Existing LiDAR-based 3D object detectors usually focus on the single-frame
detection, while ignoring the spatiotemporal information in consecutive point
cloud frames. In this paper, we propose an end-to-end online 3D video object
detector that operates on point cloud sequences. The proposed model comprises a
spatial feature encoding component and a spatiotemporal feature aggregation
component. In the former component, a novel Pillar Message Passing Network
(PMPNet) is proposed to encode each discrete point cloud frame. It adaptively
collects information for a pillar node from its neighbors by iterative message
passing, which effectively enlarges the receptive field of the pillar feature.
In the latter component, we propose an Attentive Spatiotemporal Transformer GRU
(AST-GRU) to aggregate the spatiotemporal information, which enhances the
conventional ConvGRU with an attentive memory gating mechanism. AST-GRU
contains a Spatial Transformer Attention (STA) module and a Temporal
Transformer Attention (TTA) module, which can emphasize the foreground objects
and align the dynamic objects, respectively. Experimental results demonstrate
that the proposed 3D video object detector achieves state-of-the-art
performance on the large-scale nuScenes benchmark. | [
"cs.CV"
] |
Deep neural networks designed for vision tasks are often prone to failure
when they encounter environmental conditions not covered by the training data.
Single-modal strategies are insufficient when the sensor fails to acquire
information due to malfunction or its design limitations. Multi-sensor
configurations are known to provide redundancy, increase reliability, and are
crucial in achieving robustness against asymmetric sensor failures. To address
the issue of changing lighting conditions and asymmetric sensor degradation in
object detection, we develop a multi-modal 2D object detector, and propose
deterministic and stochastic sensor-aware feature fusion strategies. The
proposed fusion mechanisms are driven by the estimated sensor measurement
reliability values/weights. Reliable object detection in harsh lighting
conditions is essential for applications such as self-driving vehicles and
human-robot interaction. We also propose a new "r-blended" hybrid depth
modality for RGB-D sensors. Through extensive experimentation, we show that the
proposed strategies outperform the existing state-of-the-art methods on the
FLIR-Thermal dataset, and obtain promising results on the SUNRGB-D dataset. We
additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and
demonstrate that the proposed object detection methodologies are highly
effective for a variety of lighting conditions. | [
"cs.CV",
"cs.RO"
] |
Machine learning models have been shown vulnerable to adversarial attacks
launched by adversarial examples which are carefully crafted by attacker to
defeat classifiers. Deep learning models cannot escape the attack either. Most
of adversarial attack methods are focused on success rate or perturbations
size, while we are more interested in the relationship between adversarial
perturbation and the image itself. In this paper, we put forward a novel
adversarial attack based on contour, named FineFool. Finefool not only has
better attack performance compared with other state-of-art white-box attacks in
aspect of higher attack success rate and smaller perturbation, but also capable
of visualization the optimal adversarial perturbation via attention on object
contour. To the best of our knowledge, Finefool is for the first time combines
the critical feature of the original clean image with the optimal perturbations
in a visible manner. Inspired by the correlations between adversarial
perturbations and object contour, slighter perturbations is produced via
focusing on object contour features, which is more imperceptible and difficult
to be defended, especially network add-on defense methods with the trade-off
between perturbations filtering and contour feature loss. Compared with
existing state-of-art attacks, extensive experiments are conducted to show that
Finefool is capable of efficient attack against defensive deep models. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Visual place recognition (VPR) is the problem of recognising a previously
visited location using visual information. Many attempts to improve the
performance of VPR methods have been made in the literature. One approach that
has received attention recently is the multi-process fusion where different VPR
methods run in parallel and their outputs are combined in an effort to achieve
better performance. The multi-process fusion, however, does not have a
well-defined criterion for selecting and combining different VPR methods from a
wide range of available options. To the best of our knowledge, this paper
investigates the complementarity of state-of-the-art VPR methods systematically
for the first time and identifies those combinations which can result in better
performance. The paper presents a well-defined framework which acts as a sanity
check to find the complementarity between two techniques by utilising a
McNemar's test-like approach. The framework allows estimation of upper and
lower complementarity bounds for the VPR techniques to be combined, along with
an estimate of maximum VPR performance that may be achieved. Based on this
framework, results are presented for eight state-of-the-art VPR methods on ten
widely-used VPR datasets showing the potential of different combinations of
techniques for achieving better performance. | [
"cs.CV"
] |
Benefit from large-scale training data, recent advances in Siamese-based
object tracking have achieved compelling results on the normal sequences.
Whilst Siamese-based trackers assume training and test data follow an identical
distribution. Suppose there is a set of foggy or rainy test sequences, it
cannot be guaranteed that the trackers trained on the normal images perform
well on the data belonging to other domains. The problem of domain shift among
training and test data has already been discussed in object detection and
semantic segmentation areas, which, however, has not been investigated for
visual tracking. To this end, based on SiamRPN++, we introduce a Domain
Adaptive SiamRPN++, namely DASiamRPN++, to improve the cross-domain
transferability and robustness of a tracker. Inspired by A-distance theory, we
present two domain adaptive modules, Pixel Domain Adaptation (PDA) and Semantic
Domain Adaptation (SDA). The PDA module aligns the feature maps of template and
search region images to eliminate the pixel-level domain shift caused by
weather, illumination, etc. The SDA module aligns the feature representations
of the tracking target's appearance to eliminate the semantic-level domain
shift. PDA and SDA modules reduce the domain disparity by learning domain
classifiers in an adversarial training manner. The domain classifiers enforce
the network to learn domain-invariant feature representations. Extensive
experiments are performed on the standard datasets of two different domains,
including synthetic foggy and TIR sequences, which demonstrate the
transferability and domain adaptability of the proposed tracker. | [
"cs.CV"
] |
Smart and agile drones are fast becoming ubiquitous at the edge of the cloud.
The usage of these drones are constrained by their limited power and compute
capability. In this paper, we present a Transfer Learning (TL) based approach
to reduce on-board computation required to train a deep neural network for
autonomous navigation via Deep Reinforcement Learning for a target algorithmic
performance. A library of 3D realistic meta-environments is manually designed
using Unreal Gaming Engine and the network is trained end-to-end. These trained
meta-weights are then used as initializers to the network in a test environment
and fine-tuned for the last few fully connected layers. Variation in drone
dynamics and environmental characteristics is carried out to show robustness of
the approach. Using NVIDIA GPU profiler it was shown that the energy
consumption and training latency is reduced by 3.7x and 1.8x respectively
without significant degradation in the performance in terms of average distance
traveled before crash i.e. Mean Safe Flight (MSF). The approach is also tested
on a real environment using DJI Tello drone and similar results were reported. | [
"cs.LG",
"stat.ML"
] |
Cutting and pasting image segments feels intuitive: the choice of source
templates gives artists flexibility in recombining existing source material.
Formally, this process takes an image set as input and outputs a collage of the
set elements. Such selection from sets of source templates does not fit easily
in classical convolutional neural models requiring inputs of fixed size.
Inspired by advances in attention and set-input machine learning, we present a
novel architecture that can generate in one forward pass image collages of
source templates using set-structured representations. This paper has the
following contributions: (i) a novel framework for image generation called
Memory Attentive Generation of Image Collages (MAGIC) which gives artists new
ways to create digital collages; (ii) from the machine-learning perspective, we
show a novel Generative Adversarial Networks (GAN) architecture that uses
Set-Transformer layers and set-pooling to blend sets of random image samples -
a hybrid non-parametric approach. | [
"cs.CV",
"cs.LG",
"eess.IV",
"stat.ML"
] |
The training of deep neural networks (DNNs) requires intensive resources both
for computation and for storage performance. Thus, DNNs cannot be efficiently
applied to mobile phones and embedded devices, which seriously limits their
applicability in industry applications. To address this issue, we propose a
novel encoding scheme of using {-1,+1} to decompose quantized neural networks
(QNNs) into multi-branch binary networks, which can be efficiently implemented
by bitwise operations (xnor and bitcount) to achieve model compression,
computational acceleration and resource saving. Based on our method, users can
easily achieve different encoding precisions arbitrarily according to their
requirements and hardware resources. The proposed mechanism is very suitable
for the use of FPGA and ASIC in terms of data storage and computation, which
provides a feasible idea for smart chips. We validate the effectiveness of our
method on both large-scale image classification tasks (e.g., ImageNet) and
object detection tasks. In particular, our method with low-bit encoding can
still achieve almost the same performance as its full-precision counterparts. | [
"cs.CV"
] |
We study decentralized stochastic linear bandits, where a network of $N$
agents acts cooperatively to efficiently solve a linear bandit-optimization
problem over a $d$-dimensional space. For this problem, we propose DLUCB: a
fully decentralized algorithm that minimizes the cumulative regret over the
entire network. At each round of the algorithm each agent chooses its actions
following an upper confidence bound (UCB) strategy and agents share information
with their immediate neighbors through a carefully designed consensus procedure
that repeats over cycles. Our analysis adjusts the duration of these
communication cycles ensuring near-optimal regret performance
$\mathcal{O}(d\log{NT}\sqrt{NT})$ at a communication rate of
$\mathcal{O}(dN^2)$ per round. The structure of the network affects the regret
performance via a small additive term - coined the regret of delay - that
depends on the spectral gap of the underlying graph. Notably, our results apply
to arbitrary network topologies without a requirement for a dedicated agent
acting as a server. In consideration of situations with high communication
cost, we propose RC-DLUCB: a modification of DLUCB with rare communication
among agents. The new algorithm trades off regret performance for a
significantly reduced total communication cost of $\mathcal{O}(d^3N^{2.5})$
over all $T$ rounds. Finally, we show that our ideas extend naturally to the
emerging, albeit more challenging, setting of safe bandits. For the recently
studied problem of linear bandits with unknown linear safety constraints, we
propose the first safe decentralized algorithm. Our study contributes towards
applying bandit techniques in safety-critical distributed systems that
repeatedly deal with unknown stochastic environments. We present numerical
simulations for various network topologies that corroborate our theoretical
findings. | [
"cs.LG",
"stat.ML"
] |
Highly complex deep learning models are increasingly integrated into modern
cyber-physical systems (CPS), many of which have strict safety requirements.
One problem arising from this is that deep learning lacks interpretability,
operating as a black box. The reliability of deep learning is heavily impacted
by how well the model training data represents runtime test data, especially
when the input space dimension is high as natural images. In response, we
propose a robust out-of-distribution (OOD) detection framework. Our approach
detects unusual movements from driving video in real-time by combining
classical optic flow operation with representation learning via variational
autoencoder (VAE). We also design a method to locate OOD factors in images.
Evaluation on a driving simulation data set shows that our approach is
statistically more robust than related works. | [
"cs.LG"
] |
In this paper we propose a rotation-invariant deep network for point clouds
analysis. Point-based deep networks are commonly designed to recognize roughly
aligned 3D shapes based on point coordinates, but suffer from performance drops
with shape rotations. Some geometric features, e.g., distances and angles of
points as inputs of network, are rotation-invariant but lose positional
information of points. In this work, we propose a novel deep network for point
clouds by incorporating positional information of points as inputs while
yielding rotation-invariance. The network is hierarchical and relies on two
modules: a positional feature embedding block and a relational feature
embedding block. Both modules and the whole network are proven to be
rotation-invariant when processing point clouds as input. Experiments show
state-of-the-art classification and segmentation performances on benchmark
datasets, and ablation studies demonstrate effectiveness of the network design. | [
"cs.CV"
] |
We propose a novel locally adaptive learning estimator for enhancing the
inter- and intra- discriminative capabilities of Deep Neural Networks, which
can be used as improved loss layer for semantic image segmentation tasks. Most
loss layers compute pixel-wise cost between feature maps and ground truths,
ignoring spatial layouts and interactions between neighboring pixels with same
object category, and thus networks cannot be effectively sensitive to
intra-class connections. Stride by stride, our method firstly conducts adaptive
pooling filter operating over predicted feature maps, aiming to merge predicted
distributions over a small group of neighboring pixels with same category, and
then it computes cost between the merged distribution vector and their category
label. Such design can make groups of neighboring predictions from same
category involved into estimations on predicting correctness with respect to
their category, and hence train networks to be more sensitive to regional
connections between adjacent pixels based on their categories. In the
experiments on Pascal VOC 2012 segmentation datasets, the consistently improved
results show that our proposed approach achieves better segmentation masks
against previous counterparts. | [
"cs.CV",
"cs.LG"
] |
In image editing, the most common task is pasting objects from one image to
the other and then eventually adjusting the manifestation of the foreground
object with the background object. This task is called image compositing. But
image compositing is a challenging problem that requires professional editing
skills and a considerable amount of time. Not only these professionals are
expensive to hire, but the tools (like Adobe Photoshop) used for doing such
tasks are also expensive to purchase making the overall task of image
compositing difficult for people without this skillset. In this work, we aim to
cater to this problem by making composite images look realistic. To achieve
this, we are using Generative Adversarial Networks (GANS). By training the
network with a diverse range of filters applied to the images and special loss
functions, the model is able to decode the color histogram of the foreground
and background part of the image and also learns to blend the foreground object
with the background. The hue and saturation values of the image play an
important role as discussed in this paper. To the best of our knowledge, this
is the first work that uses GANs for the task of image compositing. Currently,
there is no benchmark dataset available for image compositing. So we created
the dataset and will also make the dataset publicly available for benchmarking.
Experimental results on this dataset show that our method outperforms all
current state-of-the-art methods. | [
"cs.CV"
] |
State-of-the-art learning algorithms, such as random forests or neural
networks, are often qualified as "black-boxes" because of the high number and
complexity of operations involved in their prediction mechanism. This lack of
interpretability is a strong limitation for applications involving critical
decisions, typically the analysis of production processes in the manufacturing
industry. In such critical contexts, models have to be interpretable, i.e.,
simple, stable, and predictive. To address this issue, we design SIRUS (Stable
and Interpretable RUle Set), a new classification algorithm based on random
forests, which takes the form of a short list of rules. While simple models are
usually unstable with respect to data perturbation, SIRUS achieves a remarkable
stability improvement over cutting-edge methods. Furthermore, SIRUS inherits a
predictive accuracy close to random forests, combined with the simplicity of
decision trees. These properties are assessed both from a theoretical and
empirical point of view, through extensive numerical experiments based on our
R/C++ software implementation sirus available from CRAN. | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] |
Estimating the 3D hand pose from a monocular RGB image is important but
challenging. A solution is training on large-scale RGB hand images with
accurate 3D hand keypoint annotations. However, it is too expensive in
practice. Instead, we have developed a learning-based approach to synthesize
realistic, diverse, and 3D pose-preserving hand images under the guidance of 3D
pose information. We propose a 3D-aware multi-modal guided hand generative
network (MM-Hand), together with a novel geometry-based curriculum learning
strategy. Our extensive experimental results demonstrate that the 3D-annotated
images generated by MM-Hand qualitatively and quantitatively outperform
existing options. Moreover, the augmented data can consistently improve the
quantitative performance of the state-of-the-art 3D hand pose estimators on two
benchmark datasets. The code will be available at
https://github.com/ScottHoang/mm-hand. | [
"cs.CV",
"cs.MM"
] |
Vision-based navigation of autonomous vehicles primarily depends on the Deep
Neural Network (DNN) based systems in which the controller obtains input from
sensors/detectors, such as cameras and produces a vehicle control output, such
as a steering wheel angle to navigate the vehicle safely in a roadway traffic
environment. Typically, these DNN-based systems of the autonomous vehicle are
trained through supervised learning; however, recent studies show that a
trained DNN-based system can be compromised by perturbation or adversarial
inputs. Similarly, this perturbation can be introduced into the DNN-based
systems of autonomous vehicle by unexpected roadway hazards, such as debris and
roadblocks. In this study, we first introduce a roadway hazardous environment
(both intentional and unintentional roadway hazards) that can compromise the
DNN-based navigational system of an autonomous vehicle, and produces an
incorrect steering wheel angle, which can cause crashes resulting in fatality
and injury. Then, we develop a DNN-based autonomous vehicle driving system
using object detection and semantic segmentation to mitigate the adverse effect
of this type of hazardous environment, which helps the autonomous vehicle to
navigate safely around such hazards. We find that our developed DNN-based
autonomous vehicle driving system including hazardous object detection and
semantic segmentation improves the navigational ability of an autonomous
vehicle to avoid a potential hazard by 21% compared to the traditional
DNN-based autonomous vehicle driving system. | [
"cs.CV",
"cs.AI"
] |
We provide improved gap-dependent regret bounds for reinforcement learning in
finite episodic Markov decision processes. Compared to prior work, our bounds
depend on alternative definitions of gaps. These definitions are based on the
insight that, in order to achieve a favorable regret, an algorithm does not
need to learn how to behave optimally in states that are not reached by an
optimal policy. We prove tighter upper regret bounds for optimistic algorithms
and accompany them with new information-theoretic lower bounds for a large
class of MDPs. Our results show that optimistic algorithms can not achieve the
information-theoretic lower bounds even in deterministic MDPs unless there is a
unique optimal policy. | [
"cs.LG"
] |
Text-based video segmentation is a challenging task that segments out the
natural language referred objects in videos. It essentially requires semantic
comprehension and fine-grained video understanding. Existing methods introduce
language representation into segmentation models in a bottom-up manner, which
merely conducts vision-language interaction within local receptive fields of
ConvNets. We argue that such interaction is not fulfilled since the model can
barely construct region-level relationships given partial observations, which
is contrary to the description logic of natural language/referring expressions.
In fact, people usually describe a target object using relations with other
objects, which may not be easily understood without seeing the whole video. To
address the issue, we introduce a novel top-down approach by imitating how we
human segment an object with the language guidance. We first figure out all
candidate objects in videos and then choose the refereed one by parsing
relations among those high-level objects. Three kinds of object-level relations
are investigated for precise relationship understanding, i.e., positional
relation, text-guided semantic relation, and temporal relation. Extensive
experiments on A2D Sentences and J-HMDB Sentences show our method outperforms
state-of-the-art methods by a large margin. Qualitative results also show our
results are more explainable. Besides, based on the inspiration, we win the
first place in CVPR2021 Referring Youtube-VOS challenge. | [
"cs.CV"
] |
Predictive auxiliary tasks have been shown to improve performance in numerous
reinforcement learning works, however, this effect is still not well
understood. The primary purpose of the work presented here is to investigate
the impact that an auxiliary task's prediction timescale has on the agent's
policy performance. We consider auxiliary tasks which learn to make on-policy
predictions using temporal difference learning. We test the impact of
prediction timescale using a specific form of auxiliary task in which the input
image is used as the prediction target, which we refer to as temporal
difference autoencoders (TD-AE). We empirically evaluate the effect of TD-AE on
the A2C algorithm in the VizDoom environment using different prediction
timescales. While we do not observe a clear relationship between the prediction
timescale on performance, we make the following observations: 1) using
auxiliary tasks allows us to reduce the trajectory length of the A2C algorithm,
2) in some cases temporally extended TD-AE performs better than a straight
autoencoder, 3) performance with auxiliary tasks is sensitive to the weight
placed on the auxiliary loss, 4) despite this sensitivity, auxiliary tasks
improved performance without extensive hyper-parameter tuning. Our overall
conclusions are that TD-AE increases the robustness of the A2C algorithm to the
trajectory length and while promising, further study is required to fully
understand the relationship between auxiliary task prediction timescale and the
agent's performance. | [
"cs.LG",
"cs.AI"
] |
An effective 3D descriptor should be invariant to different geometric
transformations, such as scale and rotation, repeatable in the case of
occlusions and clutter, and generalisable in different contexts when data is
captured with different sensors. We present a simple but yet effective method
to learn generalisable and distinctive 3D local descriptors that can be used to
register point clouds captured in different contexts with different sensors.
Point cloud patches are extracted, canonicalised with respect to their local
reference frame, and encoded into scale and rotation-invariant compact
descriptors by a point permutation-invariant deep neural network. Our
descriptors can effectively generalise across sensor modalities from locally
and randomly sampled points. We evaluate and compare our descriptors with
alternative handcrafted and deep learning-based descriptors on several indoor
and outdoor datasets reconstructed using both RGBD sensors and laser scanners.
Our descriptors outperform most recent descriptors by a large margin in terms
of generalisation, and become the state of the art also in benchmarks where
training and testing are performed in the same scenarios. | [
"cs.CV",
"cs.RO"
] |
We report the application of machine learning to smartphone based
colorimetric detection of pH values. The strip images were used as the training
set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms
that were able to successfully classify the distinct pH values. The difference
in the obtained image formats was found not to significantly affect the
performance of the proposed machine learning approach. Moreover, the influence
of the illumination conditions on the perceived color of pH strips was
investigated and further experiments were carried out to study effect of color
change on the learning model. Test results on JPEG, RAW and RAW-corrected image
formats captured in different lighting conditions lead to perfect
classification accuracy, sensitivity and specificity, which proves that the
colorimetric detection using machine learning based systems is able to adapt to
various experimental conditions and is a great candidate for smartphone based
sensing in paper-based colorimetric assays. | [
"cs.CV"
] |
Recent developments in deep reinforcement learning have enabled the creation
of agents for solving a large variety of games given a visual input. These
methods have been proven successful for 2D games, like the Atari games, or for
simple tasks, like navigating in mazes. It is still an open question, how to
address more complex environments, in which the reward is sparse and the state
space is huge. In this paper we propose a divide and conquer deep reinforcement
learning solution and we test our agent in the first person shooter (FPS) game
of Doom. Our work is based on previous works in deep reinforcement learning and
in Doom agents. We also present how our agent is able to perform better in
unknown environments compared to a state of the art reinforcement learning
algorithm. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
We introduce a new routing algorithm for capsule networks, in which a child
capsule is routed to a parent based only on agreement between the parent's
state and the child's vote. The new mechanism 1) designs routing via inverted
dot-product attention; 2) imposes Layer Normalization as normalization; and 3)
replaces sequential iterative routing with concurrent iterative routing. When
compared to previously proposed routing algorithms, our method improves
performance on benchmark datasets such as CIFAR-10 and CIFAR-100, and it
performs at-par with a powerful CNN (ResNet-18) with 4x fewer parameters. On a
different task of recognizing digits from overlayed digit images, the proposed
capsule model performs favorably against CNNs given the same number of layers
and neurons per layer. We believe that our work raises the possibility of
applying capsule networks to complex real-world tasks. Our code is publicly
available at: https://github.com/apple/ml-capsules-inverted-attention-routing
An alternative implementation is available at:
https://github.com/yaohungt/Capsules-Inverted-Attention-Routing/blob/master/README.md | [
"cs.LG",
"stat.ML"
] |
Guided super-resolution (GSR) of thermal images using visible range images is
challenging because of the difference in the spectral-range between the images.
This in turn means that there is significant texture-mismatch between the
images, which manifests as blur and ghosting artifacts in the super-resolved
thermal image. To tackle this, we propose a novel algorithm for GSR based on
pyramidal edge-maps extracted from the visible image. Our proposed network has
two sub-networks. The first sub-network super-resolves the low-resolution
thermal image while the second obtains edge-maps from the visible image at a
growing perceptual scale and integrates them into the super-resolution
sub-network with the help of attention-based fusion. Extraction and integration
of multi-level edges allows the super-resolution network to process
texture-to-object level information progressively, enabling more
straightforward identification of overlapping edges between the input images.
Extensive experiments show that our model outperforms the state-of-the-art GSR
methods, both quantitatively and qualitatively. | [
"cs.CV"
] |
Although deep learning models have been successfully applied to a variety of
tasks, due to the millions of parameters, they are becoming increasingly opaque
and complex. In order to establish trust for their widespread commercial use,
it is important to formalize a principled framework to reason over these
models. In this work, we use ideas from causal inference to describe a general
framework to reason over CNN models. Specifically, we build a Structural Causal
Model (SCM) as an abstraction over a specific aspect of the CNN. We also
formulate a method to quantitatively rank the filters of a convolution layer
according to their counterfactual importance. We illustrate our approach with
popular CNN architectures such as LeNet5, VGG19, and ResNet32. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Registration of 3D point clouds is a fundamental task in several applications
of robotics and computer vision. While registration methods such as iterative
closest point and variants are very popular, they are only locally optimal.
There has been some recent work on globally optimal registration, but they
perform poorly in the presence of noise in the measurements. In this work we
develop a mixed integer programming-based approach for globally optimal
registration that explicitly considers uncertainty in its optimization, and
hence produces more accurate estimates. Furthermore, from a practical
implementation perspective we develop a multi-step optimization that combines
fast local methods with our accurate global formulation. Through extensive
simulation and real world experiments we demonstrate improved performance over
state-of-the-art methods for various level of noise and outliers in the data as
well as for partial geometric overlap. | [
"cs.CV"
] |
Typical human actions last several seconds and exhibit characteristic
spatio-temporal structure. Recent methods attempt to capture this structure and
learn action representations with convolutional neural networks. Such
representations, however, are typically learned at the level of a few video
frames failing to model actions at their full temporal extent. In this work we
learn video representations using neural networks with long-term temporal
convolutions (LTC). We demonstrate that LTC-CNN models with increased temporal
extents improve the accuracy of action recognition. We also study the impact of
different low-level representations, such as raw values of video pixels and
optical flow vector fields and demonstrate the importance of high-quality
optical flow estimation for learning accurate action models. We report
state-of-the-art results on two challenging benchmarks for human action
recognition UCF101 (92.7%) and HMDB51 (67.2%). | [
"cs.CV"
] |
With the proliferation of social media, fashion inspired from celebrities,
reputed designers as well as fashion influencers has shortened the cycle of
fashion design and manufacturing. However, with the explosion of fashion
related content and large number of user generated fashion photos, it is an
arduous task for fashion designers to wade through social media photos and
create a digest of trending fashion. This necessitates deep parsing of fashion
photos on social media to localize and classify multiple fashion items from a
given fashion photo. While object detection competitions such as MSCOCO have
thousands of samples for each of the object categories, it is quite difficult
to get large labeled datasets for fast fashion items. Moreover,
state-of-the-art object detectors do not have any functionality to ingest large
amount of unlabeled data available on social media in order to fine tune object
detectors with labeled datasets. In this work, we show application of a generic
object detector, that can be pretrained in an unsupervised manner, on 24
categories from recently released Open Images V4 dataset. We first train the
base architecture of the object detector using unsupervisd learning on 60K
unlabeled photos from 24 categories gathered from social media, and then
subsequently fine tune it on 8.2K labeled photos from Open Images V4 dataset.
On 300 X 300 image inputs, we achieve 72.7% mAP on a test dataset of 2.4K
photos while performing 11% to 17% better as compared to the state-of-the-art
object detectors. We show that this improvement is due to our choice of
architecture that lets us do unsupervised learning and that performs
significantly better in identifying small objects. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
We present a new weakly supervised learning-based method for generating novel
category-specific 3D shapes from unoccluded image collections. Our method is
weakly supervised and only requires silhouette annotations from unoccluded,
category-specific objects. Our method does not require access to the object's
3D shape, multiple observations per object from different views, intra-image
pixel-correspondences, or any view annotations. Key to our method is a novel
multi-projection generative adversarial network (MP-GAN) that trains a 3D shape
generator to be consistent with multiple 2D projections of the 3D shapes, and
without direct access to these 3D shapes. This is achieved through multiple
discriminators that encode the distribution of 2D projections of the 3D shapes
seen from a different views. Additionally, to determine the view information
for each silhouette image, we also train a view prediction network on
visualizations of 3D shapes synthesized by the generator. We iteratively
alternate between training the generator and training the view prediction
network. We validate our multi-projection GAN on both synthetic and real image
datasets. Furthermore, we also show that multi-projection GANs can aid in
learning other high-dimensional distributions from lower dimensional training
datasets, such as material-class specific spatially varying reflectance
properties from images. | [
"cs.CV",
"cs.GR"
] |
Graph neural networks (GNNs) manifest pathologies including over-smoothing
and limited discriminating power as a result of suboptimally expressive
aggregating mechanisms. We herein present a unifying framework for stochastic
aggregation (STAG) in GNNs, where noise is (adaptively) injected into the
aggregation process from the neighborhood to form node embeddings. We provide
theoretical arguments that STAG models, with little overhead, remedy both of
the aforementioned problems. In addition to fixed-noise models, we also propose
probabilistic versions of STAG models and a variational inference framework to
learn the noise posterior. We conduct illustrative experiments clearly
targeting oversmoothing and multiset aggregation limitations. Furthermore, STAG
enhances general performance of GNNs demonstrated by competitive performance in
common citation and molecule graph benchmark datasets. | [
"stat.ML",
"cs.AI",
"cs.LG"
] |
In E-commerce advertising, where product recommendations and product ads are
presented to users simultaneously, the traditional setting is to display ads at
fixed positions. However, under such a setting, the advertising system loses
the flexibility to control the number and positions of ads, resulting in
sub-optimal platform revenue and user experience. Consequently, major
e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible
ways to display ads. In this paper, we investigate the problem of advertising
with adaptive exposure: can we dynamically determine the number and positions
of ads for each user visit under certain business constraints so that the
platform revenue can be increased? More specifically, we consider two types of
constraints: request-level constraint ensures user experience for each user
visit, and platform-level constraint controls the overall platform monetization
rate. We model this problem as a Constrained Markov Decision Process with
per-state constraint (psCMDP) and propose a constrained two-level reinforcement
learning approach to decompose the original problem into two relatively
independent sub-problems. To accelerate policy learning, we also devise a
constrained hindsight experience replay mechanism. Experimental evaluations on
industry-scale real-world datasets demonstrate the merits of our approach in
both obtaining higher revenue under the constraints and the effectiveness of
the constrained hindsight experience replay mechanism. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
The characterization and analysis of microstructure is the foundation of
microstructural science, connecting the materials structure to its composition,
process history, and properties. Microstructural quantification traditionally
involves a human deciding a priori what to measure and then devising a
purpose-built method for doing so. However, recent advances in data science,
including computer vision (CV) and machine learning (ML) offer new approaches
to extracting information from microstructural images. This overview surveys CV
approaches to numerically encode the visual information contained in a
microstructural image, which then provides input to supervised or unsupervised
ML algorithms that find associations and trends in the high-dimensional image
representation. CV/ML systems for microstructural characterization and analysis
span the taxonomy of image analysis tasks, including image classification,
semantic segmentation, object detection, and instance segmentation. These tools
enable new approaches to microstructural analysis, including the development of
new, rich visual metrics and the discovery of
processing-microstructure-property relationships. | [
"cs.CV",
"cond-mat.mtrl-sci"
] |
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