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Skin Segmentation is widely used in biometric applications such as face
detection, face recognition, face tracking, and hand gesture recognition.
However, several challenges such as nonlinear illumination, equipment effects,
personal interferences, ethnicity variations, etc., are involved in detection
process that result in the inefficiency of color based methods. Even though
many ideas have already been proposed, the problem has not been satisfactorily
solved yet. This paper introduces a technique that addresses some limitations
of the previous works. The proposed algorithm consists of three main steps
including initial seed generation of skin map, Otsu segmentation in color
images, and finally a two-stage diffusion. The initial seed of skin pixels is
provided based on the idea of ternary image as there are certain pixels in
images which are associated to human complexion with very high probability. The
Otsu segmentation is performed on several color channels in order to identify
homogeneous regions. The result accompanying with the edge map of the image is
utilized in two consecutive diffusion steps in order to annex initially
unidentified skin pixels to the seed. Both quantitative and qualitative results
demonstrate the effectiveness of the proposed system in compare with the
state-of-the-art works. | [
"cs.CV"
] |
Automatically captioning images with natural language sentences is an
important research topic. State of the art models are able to produce
human-like sentences. These models typically describe the depicted scene as a
whole and do not target specific objects of interest or emotional relationships
between these objects in the image. However, marketing companies require to
describe these important attributes of a given scene. In our case, objects of
interest are consumer goods, which are usually identifiable by a product logo
and are associated with certain brands. From a marketing point of view, it is
desirable to also evaluate the emotional context of a trademarked product,
i.e., whether it appears in a positive or a negative connotation. We address
the problem of finding brands in images and deriving corresponding captions by
introducing a modified image captioning network. We also add a third output
modality, which simultaneously produces real-valued image ratings. Our network
is trained using a classification-aware loss function in order to stimulate the
generation of sentences with an emphasis on words identifying the brand of a
product. We evaluate our model on a dataset of images depicting interactions
between humans and branded products. The introduced network improves mean class
accuracy by 24.5 percent. Thanks to adding the third output modality, it also
considerably improves the quality of generated captions for images depicting
branded products. | [
"cs.CV"
] |
This paper presents a novel deep learning architecture for short term load
forecasting of building energy loads. The architecture is based on a simple
base learner and multiple boosting systems that are modelled as a single deep
neural network. The architecture transforms the original multivariate time
series into multiple cascading univariate time series. Together with sparse
interactions, parameter sharing and equivariant representations, this approach
makes it possible to combat against overfitting while still achieving good
presentation power with a deep network architecture. The architecture is
evaluated in several short-term load forecasting tasks with energy data from an
office building in Finland. The proposed architecture outperforms
state-of-the-art load forecasting model in all the tasks. | [
"cs.LG",
"stat.ML"
] |
Personal robots and driverless cars need to be able to operate in novel
environments and thus quickly and efficiently learn to recognise new object
classes. We address this problem by considering the task of video object
segmentation. Previous accurate methods for this task finetune a model using
the first annotated frame, and/or use additional inputs such as optical flow
and complex post-processing. In contrast, we develop a fast, causal algorithm
that requires no finetuning, auxiliary inputs or post-processing, and segments
a variable number of objects in a single forward-pass. We represent an object
with clusters, or "visual words", in the embedding space, which correspond to
object parts in the image space. This allows us to robustly match to the
reference objects throughout the video, because although the global appearance
of an object changes as it undergoes occlusions and deformations, the
appearance of more local parts may stay consistent. We learn these visual words
in an unsupervised manner, using meta-learning to ensure that our training
objective matches our inference procedure. We achieve comparable accuracy to
finetuning based methods (whilst being 1 to 2 orders of magnitude faster), and
state-of-the-art in terms of speed/accuracy trade-offs on four video
segmentation datasets. Code is available at
https://github.com/harkiratbehl/MetaVOS. | [
"cs.CV"
] |
The mechanism of message passing in graph neural networks (GNNs) is still
mysterious. Apart from convolutional neural networks, no theoretical origin for
GNNs has been proposed. To our surprise, message passing can be best understood
in terms of power iteration. By fully or partly removing activation functions
and layer weights of GNNs, we propose subspace power iteration clustering
(SPIC) models that iteratively learn with only one aggregator. Experiments show
that our models extend GNNs and enhance their capability to process random
featured networks. Moreover, we demonstrate the redundancy of some
state-of-the-art GNNs in design and define a lower limit for model evaluation
by a random aggregator of message passing. Our findings push the boundaries of
the theoretical understanding of neural networks. | [
"cs.LG",
"stat.ML"
] |
We introduce GNeRF, a framework to marry Generative Adversarial Networks
(GAN) with Neural Radiance Field (NeRF) reconstruction for the complex
scenarios with unknown and even randomly initialized camera poses. Recent
NeRF-based advances have gained popularity for remarkable realistic novel view
synthesis. However, most of them heavily rely on accurate camera poses
estimation, while few recent methods can only optimize the unknown camera poses
in roughly forward-facing scenes with relatively short camera trajectories and
require rough camera poses initialization. Differently, our GNeRF only utilizes
randomly initialized poses for complex outside-in scenarios. We propose a novel
two-phases end-to-end framework. The first phase takes the use of GANs into the
new realm for optimizing coarse camera poses and radiance fields jointly, while
the second phase refines them with additional photometric loss. We overcome
local minima using a hybrid and iterative optimization scheme. Extensive
experiments on a variety of synthetic and natural scenes demonstrate the
effectiveness of GNeRF. More impressively, our approach outperforms the
baselines favorably in those scenes with repeated patterns or even low textures
that are regarded as extremely challenging before. | [
"cs.CV"
] |
We study the problem of learning the Markov order in categorical sequences
that represent paths in a network, i.e. sequences of variable lengths where
transitions between states are constrained to a known graph. Such data pose
challenges for standard Markov order detection methods and demand modelling
techniques that explicitly account for the graph constraint. Adopting a
multi-order modelling framework for paths, we develop a Bayesian learning
technique that (i) more reliably detects the correct Markov order compared to a
competing method based on the likelihood ratio test, (ii) requires considerably
less data compared to methods using AIC or BIC, and (iii) is robust against
partial knowledge of the underlying constraints. We further show that a
recently published method that uses a likelihood ratio test has a tendency to
overfit the true Markov order of paths, which is not the case for our Bayesian
technique. Our method is important for data scientists analyzing patterns in
categorical sequence data that are subject to (partially) known constraints,
e.g. sequences with forbidden words, mobility trajectories and click stream
data, or sequence data in bioinformatics. Addressing the key challenge of model
selection, our work is further relevant for the growing body of research that
emphasizes the need for higher-order models in network analysis. | [
"cs.LG",
"cs.SI",
"stat.ME",
"stat.ML",
"60J20 (Primary) 62F07, 68T05 (Secondary)",
"G.3; I.5.1"
] |
The problem of change-point estimation is considered under a general
framework where the data are generated by unknown stationary ergodic process
distributions. In this context, the consistent estimation of the number of
change-points is provably impossible. However, it is shown that a consistent
clustering method may be used to estimate the number of change points, under
the additional constraint that the correct number of process distributions that
generate the data is provided. This additional parameter has a natural
interpretation in many real-world applications. An algorithm is proposed that
estimates the number of change-points and locates the changes. The proposed
algorithm is shown to be asymptotically consistent; its empirical evaluations
are provided. | [
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT",
"math.ST",
"stat.TH"
] |
One of the most promising approaches for unsupervised learning is combining
deep representation learning and deep clustering. Some recent works propose to
simultaneously learn representation using deep neural networks and perform
clustering by defining a clustering loss on top of embedded features. However,
these approaches are sensitive to imbalanced data and out-of-distribution
samples. Hence, these methods optimize clustering by pushing data close to
randomly initialized cluster centers. This is problematic when the number of
instances varies largely in different classes or a cluster with few samples has
less chance to be assigned a good centroid. To overcome these limitations, we
introduce StatDEC, a new unsupervised framework for joint statistical
representation learning and clustering. StatDEC simultaneously trains two deep
learning models, a deep statistics network that captures the data distribution,
and a deep clustering network that learns embedded features and performs
clustering by explicitly defining a clustering loss. Specifically, the
clustering network and representation network both take advantage of our
proposed statistics pooling layer that represents mean, variance, and
cardinality to handle the out-of-distribution samples as well as a class
imbalance. Our experiments show that using these representations, one can
considerably improve results on imbalanced image clustering across a variety of
image datasets. Moreover, the learned representations generalize well when
transferred to the out-of-distribution dataset. | [
"cs.CV"
] |
Recent works demonstrate the texture bias in Convolutional Neural Networks
(CNNs), conflicting with early works claiming that networks identify objects
using shape. It is commonly believed that the cost function forces the network
to take a greedy route to increase accuracy using texture, failing to explore
any global statistics. We propose a novel intuitive architecture, namely
CognitiveCNN, inspired from feature integration theory in psychology to utilise
human-interpretable feature like shape, texture, edges etc. to reconstruct, and
classify the image. We define two metrics, namely TIC and RIC to quantify the
importance of each stream using attention maps. We introduce a regulariser
which ensures that the contribution of each feature is same for any task, as it
is for reconstruction; and perform experiments to show the resulting boost in
accuracy and robustness besides imparting explainability. Lastly, we adapt
these ideas to conventional CNNs and propose Augmented Cognitive CNN to achieve
superior performance in object recognition. | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] |
Given new tasks with very little data$-$such as new classes in a
classification problem or a domain shift in the input$-$performance of modern
vision systems degrades remarkably quickly. In this work, we illustrate how the
neural network representations which underpin modern vision systems are subject
to supervision collapse, whereby they lose any information that is not
necessary for performing the training task, including information that may be
necessary for transfer to new tasks or domains. We then propose two methods to
mitigate this problem. First, we employ self-supervised learning to encourage
general-purpose features that transfer better. Second, we propose a novel
Transformer based neural network architecture called CrossTransformers, which
can take a small number of labeled images and an unlabeled query, find coarse
spatial correspondence between the query and the labeled images, and then infer
class membership by computing distances between spatially-corresponding
features. The result is a classifier that is more robust to task and domain
shift, which we demonstrate via state-of-the-art performance on Meta-Dataset, a
recent dataset for evaluating transfer from ImageNet to many other vision
datasets. | [
"cs.CV"
] |
Extracting context from visual representations is of utmost importance in the
advancement of Computer Science. Representation of such a format in Natural
Language has a huge variety of applications such as helping the visually
impaired etc. Such an approach is a combination of Computer Vision and Natural
Language techniques which is a hard problem to solve. This project aims to
compare different approaches for solving the image captioning problem. In
specific, the focus was on comparing two different types of models:
Encoder-Decoder approach and a Multi-model approach. In the encoder-decoder
approach, inject and merge architectures were compared against a multi-modal
image captioning approach based primarily on object detection. These approaches
have been compared on the basis on state of the art sentence comparison metrics
such as BLEU, GLEU, Meteor, and Rouge on a subset of the Google Conceptual
captions dataset which contains 100k images. On the basis of this comparison,
we observed that the best model was the Inception injected encoder model. This
best approach has been deployed as a web-based system. On uploading an image,
such a system will output the best caption associated with the image. | [
"cs.CV"
] |
Recent advancements in self-supervised learning (SSL) made it possible to
learn generalizable visual representations from unlabeled data. The performance
of Deep Learning models fine-tuned on pretrained SSL representations is on par
with models fine-tuned on the state-of-the-art supervised learning (SL)
representations. Irrespective of the progress made in SSL, its generalizability
has not been studied extensively. In this article, we perform a deeper analysis
of the generalizability of pretrained SSL and SL representations by conducting
a domain-based study for transfer learning classification tasks. The
representations are learned from the ImageNet source data, which are then
fine-tuned using two types of target datasets: similar to the source dataset,
and significantly different from the source dataset. We study generalizability
of the SSL and SL-based models via their prediction accuracy as well as
prediction confidence. In addition to this, we analyze the attribution of the
final convolutional layer of these models to understand how they reason about
the semantic identity of the data. We show that the SSL representations are
more generalizable as compared to the SL representations. We explain the
generalizability of the SSL representations by investigating its invariance
property, which is shown to be better than that observed in the SL
representations. | [
"cs.LG",
"cs.CV"
] |
Image deconvolution is the process of recovering convolutional degraded
images, which is always a hard inverse problem because of its mathematically
ill-posed property. On the success of the recently proposed deep image prior
(DIP), we build an image deconvolution model with deep image and kernel priors
(DIKP). DIP is a learning-free representation which uses neural net structures
to express image prior information, and it showed great success in many
energy-based models, e.g. denoising, super-resolution, inpainting. Instead, our
DIKP model uses such priors in image deconvolution to model not only images but
also kernels, combining the ideas of traditional learning-free deconvolution
methods with neural nets. In this paper, we show that DIKP improve the
performance of learning-free image deconvolution, and we experimentally
demonstrate this on the standard benchmark of six standard test images in terms
of PSNR and visual effects. | [
"cs.CV"
] |
Optical flow, semantic segmentation, and surface normals represent different
information modalities, yet together they bring better cues for scene
understanding problems. In this paper, we study the influence between the three
modalities: how one impacts on the others and their efficiency in combination.
We employ a modular approach using a convolutional refinement network which is
trained supervised but isolated from RGB images to enforce joint modality
features. To assist the training process, we create a large-scale synthetic
outdoor dataset that supports dense annotation of semantic segmentation,
optical flow, and surface normals. The experimental results show positive
influence among the three modalities, especially for objects' boundaries,
region consistency, and scene structures. | [
"cs.CV"
] |
One of the main challenges in reinforcement learning (RL) is generalisation.
In typical deep RL methods this is achieved by approximating the optimal value
function with a low-dimensional representation using a deep network. While this
approach works well in many domains, in domains where the optimal value
function cannot easily be reduced to a low-dimensional representation, learning
can be very slow and unstable. This paper contributes towards tackling such
challenging domains, by proposing a new method, called Hybrid Reward
Architecture (HRA). HRA takes as input a decomposed reward function and learns
a separate value function for each component reward function. Because each
component typically only depends on a subset of all features, the corresponding
value function can be approximated more easily by a low-dimensional
representation, enabling more effective learning. We demonstrate HRA on a
toy-problem and the Atari game Ms. Pac-Man, where HRA achieves above-human
performance. | [
"cs.LG"
] |
We introduce a new image segmentation task, termed Entity Segmentation (ES)
with the aim to segment all visual entities in an image without considering
semantic category labels. It has many practical applications in image
manipulation/editing where the segmentation mask quality is typically crucial
but category labels are less important. In this setting, all
semantically-meaningful segments are equally treated as categoryless entities
and there is no thing-stuff distinction. Based on our unified entity
representation, we propose a center-based entity segmentation framework with
two novel modules to improve mask quality. Experimentally, both our new task
and framework demonstrate superior advantages as against existing work. In
particular, ES enables the following: (1) merging multiple datasets to form a
large training set without the need to resolve label conflicts; (2) any model
trained on one dataset can generalize exceptionally well to other datasets with
unseen domains. Our code is made publicly available at
https://github.com/dvlab-research/Entity. | [
"cs.CV",
"cs.LG"
] |
Recent works have demonstrated reasonable success of representation learning
in hypercomplex space. Specifically, "fully-connected layers with Quaternions"
(4D hypercomplex numbers), which replace real-valued matrix multiplications in
fully-connected layers with Hamilton products of Quaternions, both enjoy
parameter savings with only 1/4 learnable parameters and achieve comparable
performance in various applications. However, one key caveat is that
hypercomplex space only exists at very few predefined dimensions (4D, 8D, and
16D). This restricts the flexibility of models that leverage hypercomplex
multiplications. To this end, we propose parameterizing hypercomplex
multiplications, allowing models to learn multiplication rules from data
regardless of whether such rules are predefined. As a result, our method not
only subsumes the Hamilton product, but also learns to operate on any arbitrary
nD hypercomplex space, providing more architectural flexibility using
arbitrarily $1/n$ learnable parameters compared with the fully-connected layer
counterpart. Experiments of applications to the LSTM and Transformer models on
natural language inference, machine translation, text style transfer, and
subject verb agreement demonstrate architectural flexibility and effectiveness
of the proposed approach. | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] |
Using a martingale concentration inequality, concentration bounds `from time
$n_0$ on' are derived for stochastic approximation algorithms with contractive
maps and both martingale difference and Markov noises. These are applied to
reinforcement learning algorithms, in particular to asynchronous Q-learning and
TD(0). | [
"cs.LG",
"cs.SY",
"eess.SY"
] |
The capacity of meta-learning algorithms to quickly adapt to a variety of
tasks, including ones they did not experience during meta-training, has been a
key factor in the recent success of these methods on few-shot learning
problems. This particular advantage of using meta-learning over standard
supervised or reinforcement learning is only well founded under the assumption
that the adaptation phase does improve the performance of our model on the task
of interest. However, in the classical framework of meta-learning, this
constraint is only mildly enforced, if not at all, and we only see an
improvement on average over a distribution of tasks. In this paper, we show
that the adaptation in an algorithm like MAML can significantly decrease the
performance of an agent in a meta-reinforcement learning setting, even on a
range of meta-training tasks. | [
"cs.LG",
"stat.ML"
] |
In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to
learn long-range contextual features for semantic segmentation. Rather than
directly construct the graph based on the backbone features, our BGR module
explores a reasonable way to combine segmentation erroneous regions with the
graph construction scenario. Motivated by the fact that most hard-to-segment
pixels broadly distribute on boundary regions, our BGR module uses the boundary
score map as prior knowledge to intensify the graph node connections and
thereby guide the graph reasoning focus on boundary regions. In addition, we
employ an efficient graph convolution implementation to reduce the
computational cost, which benefits the integration of our BGR module into
current segmentation backbones. Extensive experiments on three challenging
segmentation benchmarks demonstrate the effectiveness of our proposed BGR
module for semantic segmentation. | [
"cs.CV"
] |
We propose an efficient lighting estimation pipeline that is suitable to run
on modern mobile devices, with comparable resource complexities to
state-of-the-art mobile deep learning models. Our pipeline, PointAR, takes a
single RGB-D image captured from the mobile camera and a 2D location in that
image, and estimates 2nd order spherical harmonics coefficients. This estimated
spherical harmonics coefficients can be directly utilized by rendering engines
for supporting spatially variant indoor lighting, in the context of augmented
reality. Our key insight is to formulate the lighting estimation as a point
cloud-based learning problem directly from point clouds, which is in part
inspired by the Monte Carlo integration leveraged by real-time spherical
harmonics lighting. While existing approaches estimate lighting information
with complex deep learning pipelines, our method focuses on reducing the
computational complexity. Through both quantitative and qualitative
experiments, we demonstrate that PointAR achieves lower lighting estimation
errors compared to state-of-the-art methods. Further, our method requires an
order of magnitude lower resource, comparable to that of mobile-specific DNNs. | [
"cs.CV",
"eess.IV"
] |
Proximal Policy Optimization (PPO) is a popular on-policy reinforcement
learning algorithm but is significantly less utilized than off-policy learning
algorithms in multi-agent settings. This is often due the belief that on-policy
methods are significantly less sample efficient than their off-policy
counterparts in multi-agent problems. In this work, we investigate Multi-Agent
PPO (MAPPO), a variant of PPO which is specialized for multi-agent settings.
Using a 1-GPU desktop, we show that MAPPO achieves surprisingly strong
performance in three popular multi-agent testbeds: the particle-world
environments, the Starcraft multi-agent challenge, and the Hanabi challenge,
with minimal hyperparameter tuning and without any domain-specific algorithmic
modifications or architectures. In the majority of environments, we find that
compared to off-policy baselines, MAPPO achieves strong results while
exhibiting comparable sample efficiency. Finally, through ablation studies, we
present the implementation and algorithmic factors which are most influential
to MAPPO's practical performance. | [
"cs.LG",
"cs.AI",
"cs.MA"
] |
Semantic image segmentation aims to obtain object labels with precise
boundaries, which usually suffers from overfitting. Recently, various data
augmentation strategies like regional dropout and mix strategies have been
proposed to address the problem. These strategies have proved to be effective
for guiding the model to attend on less discriminative parts. However, current
strategies operate at the image level, and objects and the background are
coupled. Thus, the boundaries are not well augmented due to the fixed semantic
scenario. In this paper, we propose ObjectAug to perform object-level
augmentation for semantic image segmentation. ObjectAug first decouples the
image into individual objects and the background using the semantic labels.
Next, each object is augmented individually with commonly used augmentation
methods (e.g., scaling, shifting, and rotation). Then, the black area brought
by object augmentation is further restored using image inpainting. Finally, the
augmented objects and background are assembled as an augmented image. In this
way, the boundaries can be fully explored in the various semantic scenarios. In
addition, ObjectAug can support category-aware augmentation that gives various
possibilities to objects in each category, and can be easily combined with
existing image-level augmentation methods to further boost performance.
Comprehensive experiments are conducted on both natural image and medical image
datasets. Experiment results demonstrate that our ObjectAug can evidently
improve segmentation performance. | [
"cs.CV"
] |
Training deep networks with limited labeled data while achieving a strong
generalization ability is key in the quest to reduce human annotation efforts.
This is the goal of semi-supervised learning, which exploits more widely
available unlabeled data to complement small labeled data sets. In this paper,
we propose a novel framework for discriminative pixel-level tasks using a
generative model of both images and labels. Concretely, we learn a generative
adversarial network that captures the joint image-label distribution and is
trained efficiently using a large set of unlabeled images supplemented with
only few labeled ones. We build our architecture on top of StyleGAN2, augmented
with a label synthesis branch. Image labeling at test time is achieved by first
embedding the target image into the joint latent space via an encoder network
and test-time optimization, and then generating the label from the inferred
embedding. We evaluate our approach in two important domains: medical image
segmentation and part-based face segmentation. We demonstrate strong in-domain
performance compared to several baselines, and are the first to showcase
extreme out-of-domain generalization, such as transferring from CT to MRI in
medical imaging, and photographs of real faces to paintings, sculptures, and
even cartoons and animal faces. Project Page:
\url{https://nv-tlabs.github.io/semanticGAN/} | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
In recent years, 3D detection based on stereo cameras has made great
progress, but most state-of-the-art methods use anchor-based 2D detection or
depth estimation to solve this problem. However, the high computational cost
makes these methods difficult to meet real-time performance. In this work, we
propose a 3D object detection method using geometric information in stereo
images, called Stereo CenterNet. Stereo CenterNet predicts the four semantic
key points of the 3D bounding box of the object in space and uses 2D left right
boxes, 3D dimension, orientation and key points to restore the bounding box of
the object in the 3D space. Then, we use an improved photometric alignment
module to further optimize the position of the 3D bounding box. Experiments
conducted on the KITTI dataset show that our method achieves the best
speed-accuracy trade-off compared with the state-of-the-art methods that
without extra required data. | [
"cs.CV"
] |
We present techniques for effective Gaussian process (GP) modelling of
multiple short time series. These problems are common when applying GP models
independently to each gene in a gene expression time series data set. Such sets
typically contain very few time points. Naive application of common GP
modelling techniques can lead to severe over-fitting or under-fitting in a
significant fraction of the fitted models, depending on the details of the data
set. We propose avoiding over-fitting by constraining the GP length-scale to
values that focus most of the energy spectrum to frequencies below the Nyquist
frequency corresponding to the sampling frequency in the data set.
Under-fitting can be avoided by more informative priors on observation noise.
Combining these methods allows applying GP methods reliably automatically to
large numbers of independent instances of short time series. This is
illustrated with experiments with both synthetic data and real gene expression
data. | [
"stat.ML",
"q-bio.QM",
"stat.ME"
] |
Semantic segmentation of raw 3D point clouds is an essential component in 3D
scene analysis, but it poses several challenges, primarily due to the
non-Euclidean nature of 3D point clouds. Although, several deep learning based
approaches have been proposed to address this task, but almost all of them
emphasized on using the latent (global) feature representations from
traditional convolutional neural networks (CNN), resulting in severe loss of
spatial information, thus failing to model the geometry of the underlying 3D
objects, that plays an important role in remote sensing 3D scenes. In this
letter, we have proposed an alternative approach to overcome the limitations of
CNN based approaches by encoding the spatial features of raw 3D point clouds
into undirected symmetrical graph models. These encodings are then combined
with a high-dimensional feature vector extracted from a traditional CNN into a
localized graph convolution operator that outputs the required 3D segmentation
map. We have performed experiments on two standard benchmark datasets
(including an outdoor aerial remote sensing dataset and an indoor synthetic
dataset). The proposed method achieves on par state-of-the-art accuracy with
improved training time and model stability thus indicating strong potential for
further research towards a generalized state-of-the-art method for 3D scene
understanding. | [
"cs.CV"
] |
Estimates of predictive uncertainty are important for accurate model-based
planning and reinforcement learning. However, predictive
uncertainties---especially ones derived from modern deep learning systems---can
be inaccurate and impose a bottleneck on performance. This paper explores which
uncertainties are needed for model-based reinforcement learning and argues that
good uncertainties must be calibrated, i.e. their probabilities should match
empirical frequencies of predicted events. We describe a simple way to augment
any model-based reinforcement learning agent with a calibrated model and show
that doing so consistently improves planning, sample complexity, and
exploration. On the \textsc{HalfCheetah} MuJoCo task, our system achieves
state-of-the-art performance using 50\% fewer samples than the current leading
approach. Our findings suggest that calibration can improve the performance of
model-based reinforcement learning with minimal computational and
implementation overhead. | [
"cs.LG",
"stat.ML"
] |
Arbitrary attribute editing generally can be tackled by incorporating
encoder-decoder and generative adversarial networks. However, the bottleneck
layer in encoder-decoder usually gives rise to blurry and low quality editing
result. And adding skip connections improves image quality at the cost of
weakened attribute manipulation ability. Moreover, existing methods exploit
target attribute vector to guide the flexible translation to desired target
domain. In this work, we suggest to address these issues from selective
transfer perspective. Considering that specific editing task is certainly only
related to the changed attributes instead of all target attributes, our model
selectively takes the difference between target and source attribute vectors as
input. Furthermore, selective transfer units are incorporated with
encoder-decoder to adaptively select and modify encoder feature for enhanced
attribute editing. Experiments show that our method (i.e., STGAN)
simultaneously improves attribute manipulation accuracy as well as perception
quality, and performs favorably against state-of-the-arts in arbitrary facial
attribute editing and season translation. | [
"cs.CV"
] |
Heterogeneous Information Networks (HINs), involving a diversity of node
types and relation types, are pervasive in many real-world applications.
Recently, increasing attention has been paid to heterogeneous graph
representation learning (HGRL) which aims to embed rich structural and
semantics information in HIN into low-dimensional node representations. To
date, most HGRL models rely on manual customisation of meta paths to capture
the semantics underlying the given HIN. However, the dependency on the
handcrafted meta-paths requires rich domain knowledge which is extremely
difficult to obtain for complex and semantic rich HINs. Moreover, strictly
defined meta-paths will limit the HGRL's access to more comprehensive
information in HINs. To fully unleash the power of HGRL, we present a
Reinforcement Learning enhanced Heterogeneous Graph Neural Network (RL-HGNN),
to design different meta-paths for the nodes in a HIN. Specifically, RL-HGNN
models the meta-path design process as a Markov Decision Process and uses a
policy network to adaptively design a meta-path for each node to learn its
effective representations. The policy network is trained with deep
reinforcement learning by exploiting the performance of the model on a
downstream task. We further propose an extension, RL-HGNN++, to ameliorate the
meta-path design procedure and accelerate the training process. Experimental
results demonstrate the effectiveness of RL-HGNN, and reveals that it can
identify meaningful meta-paths that would have been ignored by human knowledge. | [
"cs.LG"
] |
Panoptic segmentation unifies semantic segmentation and instance segmentation
which has been attracting increasing attention in recent years. However, most
existing research was conducted under a supervised learning setup whereas
unsupervised domain adaptive panoptic segmentation which is critical in
different tasks and applications is largely neglected. We design a domain
adaptive panoptic segmentation network that exploits inter-style consistency
and inter-task regularization for optimal domain adaptive panoptic
segmentation. The inter-style consistency leverages geometric invariance across
the same image of the different styles which fabricates certain
self-supervisions to guide the network to learn domain-invariant features. The
inter-task regularization exploits the complementary nature of instance
segmentation and semantic segmentation and uses it as a constraint for better
feature alignment across domains. Extensive experiments over multiple domain
adaptive panoptic segmentation tasks (e.g., synthetic-to-real and real-to-real)
show that our proposed network achieves superior segmentation performance as
compared with the state-of-the-art. | [
"cs.CV"
] |
Query expansion is a technique widely used in image search consisting in
combining highly ranked images from an original query into an expanded query
that is then reissued, generally leading to increased recall and precision. An
important aspect of query expansion is choosing an appropriate way to combine
the images into a new query. Interestingly, despite the undeniable empirical
success of query expansion, ad-hoc methods with different caveats have
dominated the landscape, and not a lot of research has been done on learning
how to do query expansion. In this paper we propose a more principled framework
to query expansion, where one trains, in a discriminative manner, a model that
learns how images should be aggregated to form the expanded query. Within this
framework, we propose a model that leverages a self-attention mechanism to
effectively learn how to transfer information between the different images
before aggregating them. Our approach obtains higher accuracy than existing
approaches on standard benchmarks. More importantly, our approach is the only
one that consistently shows high accuracy under different regimes, overcoming
caveats of existing methods. | [
"cs.CV",
"cs.LG"
] |
Retinal vessel segmentation is an indispensable step for automatic detection
of retinal diseases with fundoscopic images. Though many approaches have been
proposed, existing methods tend to miss fine vessels or allow false positives
at terminal branches. Let alone under-segmentation, over-segmentation is also
problematic when quantitative studies need to measure the precise width of
vessels. In this paper, we present a method that generates the precise map of
retinal vessels using generative adversarial training. Our methods achieve dice
coefficient of 0.829 on DRIVE dataset and 0.834 on STARE dataset which is the
state-of-the-art performance on both datasets. | [
"cs.CV",
"cs.LG"
] |
Learning generative models for graph-structured data is challenging because
graphs are discrete, combinatorial, and the underlying data distribution is
invariant to the ordering of nodes. However, most of the existing generative
models for graphs are not invariant to the chosen ordering, which might lead to
an undesirable bias in the learned distribution. To address this difficulty, we
propose a permutation invariant approach to modeling graphs, using the recent
framework of score-based generative modeling. In particular, we design a
permutation equivariant, multi-channel graph neural network to model the
gradient of the data distribution at the input graph (a.k.a., the score
function). This permutation equivariant model of gradients implicitly defines a
permutation invariant distribution for graphs. We train this graph neural
network with score matching and sample from it with annealed Langevin dynamics.
In our experiments, we first demonstrate the capacity of this new architecture
in learning discrete graph algorithms. For graph generation, we find that our
learning approach achieves better or comparable results to existing models on
benchmark datasets. | [
"cs.LG",
"stat.ML"
] |
Rank minimization (RM) is a wildly investigated task of finding solutions by
exploiting low-rank structure of parameter matrices. Recently, solving RM
problem by leveraging non-convex relaxations has received significant
attention. It has been demonstrated by some theoretical and experimental work
that non-convex relaxation, e.g. Truncated Nuclear Norm Regularization (TNNR)
and Reweighted Nuclear Norm Regularization (RNNR), can provide a better
approximation of original problems than convex relaxations. However, designing
an efficient algorithm with theoretical guarantee remains a challenging
problem. In this paper, we propose a simple but efficient proximal-type method,
namely Iterative Shrinkage-Thresholding Algorithm(ISTA), with concrete analysis
to solve rank minimization problems with both non-convex weighted and
reweighted nuclear norm as low-rank regularizers. Theoretically, the proposed
method could converge to the critical point under very mild assumptions with
the rate in the order of $O(1/T)$. Moreover, the experimental results on both
synthetic data and real world data sets show that proposed algorithm
outperforms state-of-arts in both efficiency and accuracy. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
The task of image generation started to receive some attention from artists
and designers to inspire them in new creations. However, exploiting the results
of deep generative models such as Generative Adversarial Networks can be long
and tedious given the lack of existing tools. In this work, we propose a simple
strategy to inspire creators with new generations learned from a dataset of
their choice, while providing some control on them. We design a simple
optimization method to find the optimal latent parameters corresponding to the
closest generation to any input inspirational image. Specifically, we allow the
generation given an inspirational image of the user choice by performing
several optimization steps to recover optimal parameters from the model's
latent space. We tested several exploration methods starting with classic
gradient descents to gradient-free optimizers. Many gradient-free optimizers
just need comparisons (better/worse than another image), so that they can even
be used without numerical criterion, without inspirational image, but with only
with human preference. Thus, by iterating on one's preferences we could make
robust Facial Composite or Fashion Generation algorithms. High resolution of
the produced design generations are obtained using progressive growing of GANs.
Our results on four datasets of faces, fashion images, and textures show that
satisfactory images are effectively retrieved in most cases. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
With the explosion in the availability of spatio-temporal tracking data in
modern sports, there is an enormous opportunity to better analyse, learn and
predict important events in adversarial group environments. In this paper, we
propose a deep decision tree architecture for discriminative dictionary
learning from adversarial multi-agent trajectories. We first build up a
hierarchy for the tree structure by adding each layer and performing feature
weight based clustering in the forward pass. We then fine tune the player role
weights using back propagation. The hierarchical architecture ensures the
interpretability and the integrity of the group representation. The resulting
architecture is a decision tree, with leaf-nodes capturing a dictionary of
multi-agent group interactions. Due to the ample volume of data available, we
focus on soccer tracking data, although our approach can be used in any
adversarial multi-agent domain. We present applications of proposed method for
simulating soccer games as well as evaluating and quantifying team strategies. | [
"cs.CV"
] |
Time series data in the retail world are particularly rich in terms of
dimensionality, and these dimensions can be aggregated in groups or
hierarchies. Valuable information is nested in these complex structures, which
helps to predict the aggregated time series data. From a portfolio of brands
under HUUB's monitoring, we selected two to explore their sales behaviour,
leveraging the grouping properties of their product structure. Using
statistical models, namely SARIMA, to forecast each level of the hierarchy, an
optimal combination approach was used to generate more consistent forecasts in
the higher levels. Our results show that the proposed methods can indeed
capture nested information in the more granular series, helping to improve the
forecast accuracy of the aggregated series. The Weighted Least Squares (WLS)
method surpasses all other methods proposed in the study, including the Minimum
Trace (MinT) reconciliation. | [
"stat.ML",
"cs.LG",
"stat.AP",
"stat.ME"
] |
Arguably one of the top success stories of deep learning is transfer
learning. The finding that pre-training a network on a rich source set (eg.,
ImageNet) can help boost performance once fine-tuned on a usually much smaller
target set, has been instrumental to many applications in language and vision.
Yet, very little is known about its usefulness in 3D point cloud understanding.
We see this as an opportunity considering the effort required for annotating
data in 3D. In this work, we aim at facilitating research on 3D representation
learning. Different from previous works, we focus on high-level scene
understanding tasks. To this end, we select a suite of diverse datasets and
tasks to measure the effect of unsupervised pre-training on a large source set
of 3D scenes. Our findings are extremely encouraging: using a unified triplet
of architecture, source dataset, and contrastive loss for pre-training, we
achieve improvement over recent best results in segmentation and detection
across 6 different benchmarks for indoor and outdoor, real and synthetic
datasets -- demonstrating that the learned representation can generalize across
domains. Furthermore, the improvement was similar to supervised pre-training,
suggesting that future efforts should favor scaling data collection over more
detailed annotation. We hope these findings will encourage more research on
unsupervised pretext task design for 3D deep learning. | [
"cs.CV"
] |
With the explosive growth of online products and content, recommendation
techniques have been considered as an effective tool to overcome information
overload, improve user experience, and boost business revenue. In recent years,
we have observed a new desideratum of considering long-term rewards of multiple
related recommendation tasks simultaneously. The consideration of long-term
rewards is strongly tied to business revenue and growth. Learning multiple
tasks simultaneously could generally improve the performance of individual task
due to knowledge sharing in multi-task learning. While a few existing works
have studied long-term rewards in recommendations, they mainly focus on a
single recommendation task. In this paper, we propose {\it PoDiRe}: a
\underline{po}licy \underline{di}stilled \underline{re}commender that can
address long-term rewards of recommendations and simultaneously handle multiple
recommendation tasks. This novel recommendation solution is based on a marriage
of deep reinforcement learning and knowledge distillation techniques, which is
able to establish knowledge sharing among different tasks and reduce the size
of a learning model. The resulting model is expected to attain better
performance and lower response latency for real-time recommendation services.
In collaboration with Samsung Game Launcher, one of the world's largest
commercial mobile game platforms, we conduct a comprehensive experimental study
on large-scale real data with hundreds of millions of events and show that our
solution outperforms many state-of-the-art methods in terms of several standard
evaluation metrics. | [
"cs.LG",
"cs.IR",
"stat.ML"
] |
Virtual try-on under arbitrary poses has attracted lots of research attention
due to its huge potential applications. However, existing methods can hardly
preserve the details in clothing texture and facial identity (face, hair) while
fitting novel clothes and poses onto a person. In this paper, we propose a
novel multi-stage framework to synthesize person images, where rich details in
salient regions can be well preserved. Specifically, a multi-stage framework is
proposed to decompose the generation into spatial alignment followed by a
coarse-to-fine generation. To better preserve the details in salient areas such
as clothing and facial areas, we propose a Tree-Block (tree dilated fusion
block) to harness multi-scale features in the generator networks. With
end-to-end training of multiple stages, the whole framework can be jointly
optimized for results with significantly better visual fidelity and richer
details. Extensive experiments on standard datasets demonstrate that our
proposed framework achieves the state-of-the-art performance, especially in
preserving the visual details in clothing texture and facial identity. Our
implementation will be publicly available soon. | [
"cs.CV"
] |
Image-only and pseudo-LiDAR representations are commonly used for monocular
3D object detection. However, methods based on them have shortcomings of either
not well capturing the spatial relationships in neighbored image pixels or
being hard to handle the noisy nature of the monocular pseudo-LiDAR point
cloud. To overcome these issues, in this paper we propose a novel
object-centric voxel representation tailored for monocular 3D object detection.
Specifically, voxels are built on each object proposal, and their sizes are
adaptively determined by the 3D spatial distribution of the points, allowing
the noisy point cloud to be organized effectively within a voxel grid. This
representation is proved to be able to locate the object in 3D space
accurately. Furthermore, prior works would like to estimate the orientation via
deep features extracted from an entire image or a noisy point cloud. By
contrast, we argue that the local RoI information from the object image patch
alone with a proper resizing scheme is a better input as it provides complete
semantic clues meanwhile excludes irrelevant interferences. Besides, we
decompose the confidence mechanism in monocular 3D object detection by
considering the relationship between 3D objects and the associated 2D boxes.
Evaluated on KITTI, our method outperforms state-of-the-art methods by a large
margin. The code will be made publicly available soon. | [
"cs.CV"
] |
Autoregressive models recently achieved comparable results versus
state-of-the-art Generative Adversarial Networks (GANs) with the help of Vector
Quantized Variational AutoEncoders (VQ-VAE). However, autoregressive models
have several limitations such as exposure bias and their training objective
does not guarantee visual fidelity. To address these limitations, we propose to
use Reinforced Adversarial Learning (RAL) based on policy gradient optimization
for autoregressive models. By applying RAL, we enable a similar process for
training and testing to address the exposure bias issue. In addition, visual
fidelity has been further optimized with adversarial loss inspired by their
strong counterparts: GANs. Due to the slow sampling speed of autoregressive
models, we propose to use partial generation for faster training. RAL also
empowers the collaboration between different modules of the VQ-VAE framework.
To our best knowledge, the proposed method is first to enable adversarial
learning in autoregressive models for image generation. Experiments on
synthetic and real-world datasets show improvements over the MLE trained
models. The proposed method improves both negative log-likelihood (NLL) and
Fr\'echet Inception Distance (FID), which indicates improvements in terms of
visual quality and diversity. The proposed method achieves state-of-the-art
results on Celeba for 64 $\times$ 64 image resolution, showing promise for
large scale image generation. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Video super-resolution (VSR) aims to restore a photo-realistic
high-resolution (HR) video frame from both its corresponding low-resolution
(LR) frame (reference frame) and multiple neighboring frames (supporting
frames). Due to varying motion of cameras or objects, the reference frame and
each support frame are not aligned. Therefore, temporal alignment is a
challenging yet important problem for VSR. Previous VSR methods usually utilize
optical flow between the reference frame and each supporting frame to wrap the
supporting frame for temporal alignment. Therefore, the performance of these
image-level wrapping-based models will highly depend on the prediction accuracy
of optical flow, and inaccurate optical flow will lead to artifacts in the
wrapped supporting frames, which also will be propagated into the reconstructed
HR video frame. To overcome the limitation, in this paper, we propose a
temporal deformable alignment network (TDAN) to adaptively align the reference
frame and each supporting frame at the feature level without computing optical
flow. The TDAN uses features from both the reference frame and each supporting
frame to dynamically predict offsets of sampling convolution kernels. By using
the corresponding kernels, TDAN transforms supporting frames to align with the
reference frame. To predict the HR video frame, a reconstruction network taking
aligned frames and the reference frame is utilized. Experimental results
demonstrate the effectiveness of the proposed TDAN-based VSR model. | [
"cs.CV"
] |
Reliable and accurate 3D object detection is a necessity for safe autonomous
driving. Although LiDAR sensors can provide accurate 3D point cloud estimates
of the environment, they are also prohibitively expensive for many settings.
Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction
in the accuracy gap between methods based on LiDAR sensors and those based on
cheap stereo cameras. PL combines state-of-the-art deep neural networks for 3D
depth estimation with those for 3D object detection by converting 2D depth map
outputs to 3D point cloud inputs. However, so far these two networks have to be
trained separately. In this paper, we introduce a new framework based on
differentiable Change of Representation (CoR) modules that allow the entire PL
pipeline to be trained end-to-end. The resulting framework is compatible with
most state-of-the-art networks for both tasks and in combination with PointRCNN
improves over PL consistently across all benchmarks -- yielding the highest
entry on the KITTI image-based 3D object detection leaderboard at the time of
submission. Our code will be made available at
https://github.com/mileyan/pseudo-LiDAR_e2e. | [
"cs.CV",
"eess.IV"
] |
Visual SLAM shows significant progress in recent years due to high attention
from vision community but still, challenges remain for low-textured
environments. Feature based visual SLAMs do not produce reliable camera and
structure estimates due to insufficient features in a low-textured environment.
Moreover, existing visual SLAMs produce partial reconstruction when the number
of 3D-2D correspondences is insufficient for incremental camera estimation
using bundle adjustment. This paper presents Edge SLAM, a feature based
monocular visual SLAM which mitigates the above mentioned problems. Our
proposed Edge SLAM pipeline detects edge points from images and tracks those
using optical flow for point correspondence. We further refine these point
correspondences using geometrical relationship among three views. Owing to our
edge-point tracking, we use a robust method for two-view initialization for
bundle adjustment. Our proposed SLAM also identifies the potential situations
where estimating a new camera into the existing reconstruction is becoming
unreliable and we adopt a novel method to estimate the new camera reliably
using a local optimization technique. We present an extensive evaluation of our
proposed SLAM pipeline with most popular open datasets and compare with the
state-of-the art. Experimental result indicates that our Edge SLAM is robust
and works reliably well for both textured and less-textured environment in
comparison to existing state-of-the-art SLAMs. | [
"cs.CV"
] |
Recent advances in attention-based networks have shown that Vision
Transformers can achieve state-of-the-art or near state-of-the-art results on
many image classification tasks. This puts transformers in the unique position
of being a promising alternative to traditional convolutional neural networks
(CNNs). While CNNs have been carefully studied with respect to adversarial
attacks, the same cannot be said of Vision Transformers. In this paper, we
study the robustness of Vision Transformers to adversarial examples. Our
analyses of transformer security is divided into three parts. First, we test
the transformer under standard white-box and black-box attacks. Second, we
study the transferability of adversarial examples between CNNs and
transformers. We show that adversarial examples do not readily transfer between
CNNs and transformers. Based on this finding, we analyze the security of a
simple ensemble defense of CNNs and transformers. By creating a new attack, the
self-attention blended gradient attack, we show that such an ensemble is not
secure under a white-box adversary. However, under a black-box adversary, we
show that an ensemble can achieve unprecedented robustness without sacrificing
clean accuracy. Our analysis for this work is done using six types of white-box
attacks and two types of black-box attacks. Our study encompasses multiple
Vision Transformers, Big Transfer Models and CNN architectures trained on
CIFAR-10, CIFAR-100 and ImageNet. | [
"cs.CV",
"cs.LG"
] |
Generative adversarial networks (GANs) have shown impressive results in both
unconditional and conditional image generation. In recent literature, it is
shown that pre-trained GANs, on a different dataset, can be transferred to
improve the image generation from a small target data. The same, however, has
not been well-studied in the case of conditional GANs (cGANs), which provides
new opportunities for knowledge transfer compared to unconditional setup. In
particular, the new classes may borrow knowledge from the related old classes,
or share knowledge among themselves to improve the training. This motivates us
to study the problem of efficient conditional GAN transfer with knowledge
propagation across classes. To address this problem, we introduce a new GAN
transfer method to explicitly propagate the knowledge from the old classes to
the new classes. The key idea is to enforce the popularly used conditional
batch normalization (BN) to learn the class-specific information of the new
classes from that of the old classes, with implicit knowledge sharing among the
new ones. This allows for an efficient knowledge propagation from the old
classes to the new ones, with the BN parameters increasing linearly with the
number of new classes. The extensive evaluation demonstrates the clear
superiority of the proposed method over state-of-the-art competitors for
efficient conditional GAN transfer tasks. The code is available at:
https://github.com/mshahbazi72/cGANTransfer | [
"cs.CV"
] |
Feature selection methods have an important role on the readability of data
and the reduction of complexity of learning algorithms. In recent years, a
variety of efforts are investigated on feature selection problems based on
unsupervised viewpoint due to the laborious labeling task on large datasets. In
this paper, we propose a novel approach on unsupervised feature selection
initiated from the subspace clustering to preserve the similarities by
representation learning of low dimensional subspaces among the samples. A
self-expressive model is employed to implicitly learn the cluster similarities
in an adaptive manner. The proposed method not only maintains the sample
similarities through subspace clustering, but it also captures the
discriminative information based on a regularized regression model. In line
with the convergence analysis of the proposed method, the experimental results
on benchmark datasets demonstrate the effectiveness of our approach as compared
with the state of the art methods. | [
"cs.LG",
"stat.ML"
] |
A variety of deep neural networks have been applied in medical image
segmentation and achieve good performance. Unlike natural images, medical
images of the same imaging modality are characterized by the same pattern,
which indicates that same normal organs or tissues locate at similar positions
in the images. Thus, in this paper we try to incorporate the prior knowledge of
medical images into the structure of neural networks such that the prior
knowledge can be utilized for accurate segmentation. Based on this idea, we
propose a novel deep network called knowledge-based fully convolutional network
(KFCN) for medical image segmentation. The segmentation function and
corresponding error is analyzed. We show the existence of an asymptotically
stable region for KFCN which traditional FCN doesn't possess. Experiments
validate our knowledge assumption about the incorporation of prior knowledge
into the convolution kernels of KFCN and show that KFCN can achieve a
reasonable segmentation and a satisfactory accuracy. | [
"cs.CV"
] |
The Transformer architecture is widely used in natural language processing.
Despite its success, the design principle of the Transformer remains elusive.
In this paper, we provide a novel perspective towards understanding the
architecture: we show that the Transformer can be mathematically interpreted as
a numerical Ordinary Differential Equation (ODE) solver for a
convection-diffusion equation in a multi-particle dynamic system. In
particular, how words in a sentence are abstracted into contexts by passing
through the layers of the Transformer can be interpreted as approximating
multiple particles' movement in the space using the Lie-Trotter splitting
scheme and the Euler's method. Given this ODE's perspective, the rich
literature of numerical analysis can be brought to guide us in designing
effective structures beyond the Transformer. As an example, we propose to
replace the Lie-Trotter splitting scheme by the Strang-Marchuk splitting
scheme, a scheme that is more commonly used and with much lower local
truncation errors. The Strang-Marchuk splitting scheme suggests that the
self-attention and position-wise feed-forward network (FFN) sub-layers should
not be treated equally. Instead, in each layer, two position-wise FFN
sub-layers should be used, and the self-attention sub-layer is placed in
between. This leads to a brand new architecture. Such an FFN-attention-FFN
layer is "Macaron-like", and thus we call the network with this new
architecture the Macaron Net. Through extensive experiments, we show that the
Macaron Net is superior to the Transformer on both supervised and unsupervised
learning tasks. The reproducible codes and pretrained models can be found at
https://github.com/zhuohan123/macaron-net | [
"cs.LG",
"cs.CL",
"stat.ML"
] |
Measuring and analyzing the flow of customers in retail stores is essential
for a retailer to better comprehend customers' behavior and support
decision-making. Nevertheless, not much attention has been given to the
development of novel technologies for automatic people counting. We introduce
LRCN-RetailNet: a recurrent neural network architecture capable of learning a
non-linear regression model and accurately predicting the people count from
videos captured by low-cost surveillance cameras. The input video format
follows the recently proposed RGBP image format, which is comprised of color
and people (foreground) information. Our architecture is capable of considering
two relevant aspects: spatial features extracted through convolutional layers
from the RGBP images; and the temporal coherence of the problem, which is
exploited by recurrent layers. We show that, through a supervised learning
approach, the trained models are capable of predicting the people count with
high accuracy. Additionally, we present and demonstrate that a straightforward
modification of the methodology is effective to exclude salespeople from the
people count. Comprehensive experiments were conducted to validate, evaluate
and compare the proposed architecture. Results corroborated that LRCN-RetailNet
remarkably outperforms both the previous RetailNet architecture, which was
limited to evaluating a single image per iteration; and a state-of-the-art
neural network for object detection. Finally, computational performance
experiments confirmed that the entire methodology is effective to estimate
people count in real-time. | [
"cs.CV"
] |
Convolutional networks are ubiquitous in deep learning. They are particularly
useful for images, as they reduce the number of parameters, reduce training
time, and increase accuracy. However, as a model of the brain they are
seriously problematic, since they require weight sharing - something real
neurons simply cannot do. Consequently, while neurons in the brain can be
locally connected (one of the features of convolutional networks), they cannot
be convolutional. Locally connected but non-convolutional networks, however,
significantly underperform convolutional ones. This is troublesome for studies
that use convolutional networks to explain activity in the visual system. Here
we study plausible alternatives to weight sharing that aim at the same
regularization principle, which is to make each neuron within a pool react
similarly to identical inputs. The most natural way to do that is by showing
the network multiple translations of the same image, akin to saccades in animal
vision. However, this approach requires many translations, and doesn't remove
the performance gap. We propose instead to add lateral connectivity to a
locally connected network, and allow learning via Hebbian plasticity. This
requires the network to pause occasionally for a sleep-like phase of "weight
sharing". This method enables locally connected networks to achieve nearly
convolutional performance on ImageNet, thus supporting convolutional networks
as a model of the visual stream. | [
"cs.LG",
"cs.NE",
"q-bio.NC"
] |
Existing RGB-D salient object detection (SOD) models usually treat RGB and
depth as independent information and design separate networks for feature
extraction from each. Such schemes can easily be constrained by a limited
amount of training data or over-reliance on an elaborately designed training
process. Inspired by the observation that RGB and depth modalities actually
present certain commonality in distinguishing salient objects, a novel joint
learning and densely cooperative fusion (JL-DCF) architecture is designed to
learn from both RGB and depth inputs through a shared network backbone, known
as the Siamese architecture. In this paper, we propose two effective
components: joint learning (JL), and densely cooperative fusion (DCF). The JL
module provides robust saliency feature learning by exploiting cross-modal
commonality via a Siamese network, while the DCF module is introduced for
complementary feature discovery. Comprehensive experiments using five popular
metrics show that the designed framework yields a robust RGB-D saliency
detector with good generalization. As a result, JL-DCF significantly advances
the state-of-the-art models by an average of ~2.0% (max F-measure) across seven
challenging datasets. In addition, we show that JL-DCF is readily applicable to
other related multi-modal detection tasks, including RGB-T (thermal infrared)
SOD and video SOD, achieving comparable or even better performance against
state-of-the-art methods. We also link JL-DCF to the RGB-D semantic
segmentation field, showing its capability of outperforming several semantic
segmentation models on the task of RGB-D SOD. These facts further confirm that
the proposed framework could offer a potential solution for various
applications and provide more insight into the cross-modal complementarity
task. | [
"cs.CV"
] |
Most modern deep learning-based multi-view 3D reconstruction techniques use
RNNs or fusion modules to combine information from multiple images after
encoding them. These two separate steps have loose connections and do not
consider all available information while encoding each view. We propose
LegoFormer, a transformer-based model that unifies object reconstruction under
a single framework and parametrizes the reconstructed occupancy grid by its
decomposition factors. This reformulation allows the prediction of an object as
a set of independent structures then aggregated to obtain the final
reconstruction. Experiments conducted on ShapeNet display the competitive
performance of our network with respect to the state-of-the-art methods. We
also demonstrate how the use of self-attention leads to increased
interpretability of the model output. | [
"cs.CV"
] |
Deep learning-based methods for video pedestrian detection and tracking
require large volumes of training data to achieve good performance. However,
data acquisition in crowded public environments raises data privacy concerns --
we are not allowed to simply record and store data without the explicit consent
of all participants. Furthermore, the annotation of such data for computer
vision applications usually requires a substantial amount of manual effort,
especially in the video domain. Labeling instances of pedestrians in highly
crowded scenarios can be challenging even for human annotators and may
introduce errors in the training data. In this paper, we study how we can
advance different aspects of multi-person tracking using solely synthetic data.
To this end, we generate MOTSynth, a large, highly diverse synthetic dataset
for object detection and tracking using a rendering game engine. Our
experiments show that MOTSynth can be used as a replacement for real data on
tasks such as pedestrian detection, re-identification, segmentation, and
tracking. | [
"cs.CV"
] |
The rapidly emerging field of deep learning-based computational pathology has
demonstrated promise in developing objective prognostic models from histology
whole slide images. However, most prognostic models are either based on
histology or genomics alone and do not address how histology and genomics can
be integrated to develop joint image-omic prognostic models. Additionally
identifying explainable morphological and molecular descriptors from these
models that govern such prognosis is of interest. We used multimodal deep
learning to integrate gigapixel whole slide pathology images, RNA-seq
abundance, copy number variation, and mutation data from 5,720 patients across
14 major cancer types. Our interpretable, weakly-supervised, multimodal deep
learning algorithm is able to fuse these heterogeneous modalities for
predicting outcomes and discover prognostic features from these modalities that
corroborate with poor and favorable outcomes via multimodal interpretability.
We compared our model with unimodal deep learning models trained on histology
slides and molecular profiles alone, and demonstrate performance increase in
risk stratification on 9 out of 14 cancers. In addition, we analyze morphologic
and molecular markers responsible for prognostic predictions across all cancer
types. All analyzed data, including morphological and molecular correlates of
patient prognosis across the 14 cancer types at a disease and patient level are
presented in an interactive open-access database
(http://pancancer.mahmoodlab.org) to allow for further exploration and
prognostic biomarker discovery. To validate that these model explanations are
prognostic, we further analyzed high attention morphological regions in WSIs,
which indicates that tumor-infiltrating lymphocyte presence corroborates with
favorable cancer prognosis on 9 out of 14 cancer types studied. | [
"cs.CV",
"cs.AI",
"q-bio.GN",
"q-bio.QM",
"q-bio.TO"
] |
This paper presents a novel approach for segmenting moving objects in
unconstrained environments using guided convolutional neural networks. This
guiding process relies on foreground masks from independent algorithms (i.e.
state-of-the-art algorithms) to implement an attention mechanism that
incorporates the spatial location of foreground and background to compute their
separated representations. Our approach initially extracts two kinds of
features for each frame using colour and optical flow information. Such
features are combined following a multiplicative scheme to benefit from their
complementarity. These unified colour and motion features are later processed
to obtain the separated foreground and background representations. Then, both
independent representations are concatenated and decoded to perform foreground
segmentation. Experiments conducted on the challenging DAVIS 2016 dataset
demonstrate that our guided representations not only outperform non-guided, but
also recent and top-performing video object segmentation algorithms. | [
"cs.CV"
] |
The data imbalance problem is a frequent bottleneck in the classification
performance of neural networks. In this paper, we propose a novel supervised
discriminative feature generation (DFG) method for a minority class dataset.
DFG is based on the modified structure of a generative adversarial network
consisting of four independent networks: generator, discriminator, feature
extractor, and classifier. To augment the selected discriminative features of
the minority class data by adopting an attention mechanism, the generator for
the class-imbalanced target task is trained, and the feature extractor and
classifier are regularized using the pre-trained features from a large source
data. The experimental results show that the DFG generator enhances the
augmentation of the label-preserved and diverse features, and the
classification results are significantly improved on the target task. The
feature generation model can contribute greatly to the development of data
augmentation methods through discriminative feature generation and supervised
attention methods. | [
"cs.CV",
"cs.LG"
] |
The vanilla GAN (Goodfellow et al. 2014) suffers from mode collapse deeply,
which usually manifests as that the images generated by generators tend to have
a high similarity amongst them, even though their corresponding latent vectors
have been very different. In this paper, we introduce a pluggable diversity
penalty module (DPM) to alleviate mode collapse of GANs. It reduces the
similarity of image pairs in feature space, i.e., if two latent vectors are
different, then we enforce the generator to generate two images with different
features. The normalized Gram matrix is used to measure the similarity. We
compare the proposed method with Unrolled GAN (Metz et al. 2016), BourGAN
(Xiao, Zhong, and Zheng 2018), PacGAN (Lin et al. 2018), VEEGAN (Srivastava et
al. 2017) and ALI (Dumoulin et al. 2016) on 2D synthetic dataset, and results
show that the diversity penalty module can help GAN capture much more modes of
the data distribution. Further, in classification tasks, we apply this method
as image data augmentation on MNIST, Fashion- MNIST and CIFAR-10, and the
classification testing accuracy is improved by 0.24%, 1.34% and 0.52% compared
with WGAN GP (Gulrajani et al. 2017), respectively. In domain translation,
diversity penalty module can help StarGAN (Choi et al. 2018) generate more
accurate attention masks and accelarate the convergence process. Finally, we
quantitatively evaluate the proposed method with IS and FID on CelebA,
CIFAR-10, MNIST and Fashion-MNIST, and the results suggest GAN with diversity
penalty module gets much higher IS and lower FID compared with some SOTA GAN
architectures. | [
"cs.CV"
] |
Joint image-text embedding is the bedrock for most Vision-and-Language (V+L)
tasks, where multimodality inputs are simultaneously processed for joint visual
and textual understanding. In this paper, we introduce UNITER, a UNiversal
Image-TExt Representation, learned through large-scale pre-training over four
image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU
Captions), which can power heterogeneous downstream V+L tasks with joint
multimodal embeddings. We design four pre-training tasks: Masked Language
Modeling (MLM), Masked Region Modeling (MRM, with three variants), Image-Text
Matching (ITM), and Word-Region Alignment (WRA). Different from previous work
that applies joint random masking to both modalities, we use conditional
masking on pre-training tasks (i.e., masked language/region modeling is
conditioned on full observation of image/text). In addition to ITM for global
image-text alignment, we also propose WRA via the use of Optimal Transport (OT)
to explicitly encourage fine-grained alignment between words and image regions
during pre-training. Comprehensive analysis shows that both conditional masking
and OT-based WRA contribute to better pre-training. We also conduct a thorough
ablation study to find an optimal combination of pre-training tasks. Extensive
experiments show that UNITER achieves new state of the art across six V+L tasks
(over nine datasets), including Visual Question Answering, Image-Text
Retrieval, Referring Expression Comprehension, Visual Commonsense Reasoning,
Visual Entailment, and NLVR$^2$. Code is available at
https://github.com/ChenRocks/UNITER. | [
"cs.CV",
"cs.CL",
"cs.LG"
] |
Deep generative models for graphs have shown great promise in the area of
drug design, but have so far found little application beyond generating
graph-structured molecules. In this work, we demonstrate a proof of concept for
the challenging task of road network extraction from image data. This task can
be framed as image-conditioned graph generation, for which we develop the
Generative Graph Transformer (GGT), a deep autoregressive model that makes use
of attention mechanisms for image conditioning and the recurrent generation of
graphs. We benchmark GGT on the application of road network extraction from
semantic segmentation data. For this, we introduce the Toulouse Road Network
dataset, based on real-world publicly-available data. We further propose the
StreetMover distance: a metric based on the Sinkhorn distance for effectively
evaluating the quality of road network generation. The code and dataset are
publicly available. | [
"cs.LG",
"stat.ML"
] |
There is a recent surge of interest in cross-modal representation learning
corresponding to images and text. The main challenge lies in mapping images and
text to a shared latent space where the embeddings corresponding to a similar
semantic concept lie closer to each other than the embeddings corresponding to
different semantic concepts, irrespective of the modality. Ranking losses are
commonly used to create such shared latent space -- however, they do not impose
any constraints on inter-class relationships resulting in neighboring clusters
to be completely unrelated. The works in the domain of visual semantic
embeddings address this problem by first constructing a semantic embedding
space based on some external knowledge and projecting image embeddings onto
this fixed semantic embedding space. These works are confined only to image
domain and constraining the embeddings to a fixed space adds additional burden
on learning. This paper proposes a novel method, HUSE, to learn cross-modal
representation with semantic information. HUSE learns a shared latent space
where the distance between any two universal embeddings is similar to the
distance between their corresponding class embeddings in the semantic embedding
space. HUSE also uses a classification objective with a shared classification
layer to make sure that the image and text embeddings are in the same shared
latent space. Experiments on UPMC Food-101 show our method outperforms previous
state-of-the-art on retrieval, hierarchical precision and classification
results. | [
"cs.CV",
"cs.CL",
"cs.LG"
] |
A common shortfall of supervised learning for medical imaging is the greedy
need for human annotations, which is often expensive and time-consuming to
obtain. This paper proposes a semi-supervised classification method for three
kinds of apicomplexan parasites and non-infected host cells microscopic images,
which uses a small number of labeled data and a large number of unlabeled data
for training. There are two challenges in microscopic image recognition. The
first is that salient structures of the microscopic images are more fuzzy and
intricate than natural images' on a real-world scale. The second is that
insignificant textures, like background staining, lightness, and contrast
level, vary a lot in samples from different clinical scenarios. To address
these challenges, we aim to learn a distinguishable and appearance-invariant
representation by contrastive learning strategy. On one hand, macroscopic
images, which share similar shape characteristics in morphology, are introduced
to contrast for structure enhancement. On the other hand, different appearance
transformations, including color distortion and flittering, are utilized to
contrast for texture elimination. In the case where only 1% of microscopic
images are labeled, the proposed method reaches an accuracy of 94.90% in a
generalized testing set. | [
"cs.CV",
"cs.AI"
] |
Hyperspectral (HS) images are characterized by approximately contiguous
spectral information, enabling the fine identification of materials by
capturing subtle spectral discrepancies. Owing to their excellent locally
contextual modeling ability, convolutional neural networks (CNNs) have been
proven to be a powerful feature extractor in HS image classification. However,
CNNs fail to mine and represent the sequence attributes of spectral signatures
well due to the limitations of their inherent network backbone. To solve this
issue, we rethink HS image classification from a sequential perspective with
transformers, and propose a novel backbone network called \ul{SpectralFormer}.
Beyond band-wise representations in classic transformers, SpectralFormer is
capable of learning spectrally local sequence information from neighboring
bands of HS images, yielding group-wise spectral embeddings. More
significantly, to reduce the possibility of losing valuable information in the
layer-wise propagation process, we devise a cross-layer skip connection to
convey memory-like components from shallow to deep layers by adaptively
learning to fuse "soft" residuals across layers. It is worth noting that the
proposed SpectralFormer is a highly flexible backbone network, which can be
applicable to both pixel- and patch-wise inputs. We evaluate the classification
performance of the proposed SpectralFormer on three HS datasets by conducting
extensive experiments, showing the superiority over classic transformers and
achieving a significant improvement in comparison with state-of-the-art
backbone networks. The codes of this work will be available at
\url{https://sites.google.com/view/danfeng-hong} for the sake of
reproducibility. | [
"cs.CV",
"cs.AI"
] |
Recent developments in gradient-based attention modeling have seen attention
maps emerge as a powerful tool for interpreting convolutional neural networks.
Despite good localization for an individual class of interest, these techniques
produce attention maps with substantially overlapping responses among different
classes, leading to the problem of visual confusion and the need for
discriminative attention. In this paper, we address this problem by means of a
new framework that makes class-discriminative attention a principled part of
the learning process. Our key innovations include new learning objectives for
attention separability and cross-layer consistency, which result in improved
attention discriminability and reduced visual confusion. Extensive experiments
on image classification benchmarks show the effectiveness of our approach in
terms of improved classification accuracy, including CIFAR-100 (+3.33%),
Caltech-256 (+1.64%), ILSVRC2012 (+0.92%), CUB-200-2011 (+4.8%) and PASCAL
VOC2012 (+5.73%). | [
"cs.CV",
"cs.LG"
] |
Recent exploration methods have proven to be a recipe for improving
sample-efficiency in deep reinforcement learning (RL). However, efficient
exploration in high-dimensional observation spaces still remains a challenge.
This paper presents Random Encoders for Efficient Exploration (RE3), an
exploration method that utilizes state entropy as an intrinsic reward. In order
to estimate state entropy in environments with high-dimensional observations,
we utilize a k-nearest neighbor entropy estimator in the low-dimensional
representation space of a convolutional encoder. In particular, we find that
the state entropy can be estimated in a stable and compute-efficient manner by
utilizing a randomly initialized encoder, which is fixed throughout training.
Our experiments show that RE3 significantly improves the sample-efficiency of
both model-free and model-based RL methods on locomotion and navigation tasks
from DeepMind Control Suite and MiniGrid benchmarks. We also show that RE3
allows learning diverse behaviors without extrinsic rewards, effectively
improving sample-efficiency in downstream tasks. Source code and videos are
available at https://sites.google.com/view/re3-rl. | [
"cs.LG"
] |
This paper presents Multi-view Labelling Object Detector (MLOD). The detector
takes an RGB image and a LIDAR point cloud as input and follows the two-stage
object detection framework. A Region Proposal Network (RPN) generates 3D
proposals in a Bird's Eye View (BEV) projection of the point cloud. The second
stage projects the 3D proposal bounding boxes to the image and BEV feature maps
and sends the corresponding map crops to a detection header for classification
and bounding-box regression. Unlike other multi-view based methods, the cropped
image features are not directly fed to the detection header, but masked by the
depth information to filter out parts outside 3D bounding boxes. The fusion of
image and BEV features is challenging, as they are derived from different
perspectives. We introduce a novel detection header, which provides detection
results not just from fusion layer, but also from each sensor channel. Hence
the object detector can be trained on data labelled in different views to avoid
the degeneration of feature extractors. MLOD achieves state-of-the-art
performance on the KITTI 3D object detection benchmark. Most importantly, the
evaluation shows that the new header architecture is effective in preventing
image feature extractor degeneration. | [
"cs.CV",
"cs.LG",
"cs.RO"
] |
Nowadays, plenty of deep learning technologies are being applied to all
aspects of autonomous driving with promising results. Among them, object
detection is the key to improve the ability of an autonomous agent to perceive
its environment so that it can (re)act. However, previous vision-based object
detectors cannot achieve satisfactory performance under real-time driving
scenarios. To remedy this, we present the real-time steaming perception system
in this paper, which is also the 2nd Place solution of Streaming Perception
Challenge (Workshop on Autonomous Driving at CVPR 2021) for the detection-only
track. Unlike traditional object detection challenges, which focus mainly on
the absolute performance, streaming perception task requires achieving a
balance of accuracy and latency, which is crucial for real-time autonomous
driving. We adopt YOLOv5 as our basic framework, data augmentation,
Bag-of-Freebies, and Transformer are adopted to improve streaming object
detection performance with negligible extra inference cost. On the Argoverse-HD
test set, our method achieves 33.2 streaming AP (34.6 streaming AP verified by
the organizer) under the required hardware. Its performance significantly
surpasses the fixed baseline of 13.6 (host team), demonstrating the
potentiality of application. | [
"cs.CV"
] |
Clustering is a fundamental task in data analysis. Recently, deep clustering,
which derives inspiration primarily from deep learning approaches, achieves
state-of-the-art performance and has attracted considerable attention. Current
deep clustering methods usually boost the clustering results by means of the
powerful representation ability of deep learning, e.g., autoencoder, suggesting
that learning an effective representation for clustering is a crucial
requirement. The strength of deep clustering methods is to extract the useful
representations from the data itself, rather than the structure of data, which
receives scarce attention in representation learning. Motivated by the great
success of Graph Convolutional Network (GCN) in encoding the graph structure,
we propose a Structural Deep Clustering Network (SDCN) to integrate the
structural information into deep clustering. Specifically, we design a delivery
operator to transfer the representations learned by autoencoder to the
corresponding GCN layer, and a dual self-supervised mechanism to unify these
two different deep neural architectures and guide the update of the whole
model. In this way, the multiple structures of data, from low-order to
high-order, are naturally combined with the multiple representations learned by
autoencoder. Furthermore, we theoretically analyze the delivery operator, i.e.,
with the delivery operator, GCN improves the autoencoder-specific
representation as a high-order graph regularization constraint and autoencoder
helps alleviate the over-smoothing problem in GCN. Through comprehensive
experiments, we demonstrate that our propose model can consistently perform
better over the state-of-the-art techniques. | [
"cs.LG",
"stat.ML"
] |
We define and study error detection and correction tasks that are useful for
3D reconstruction of neurons from electron microscopic imagery, and for image
segmentation more generally. Both tasks take as input the raw image and a
binary mask representing a candidate object. For the error detection task, the
desired output is a map of split and merge errors in the object. For the error
correction task, the desired output is the true object. We call this object
mask pruning, because the candidate object mask is assumed to be a superset of
the true object. We train multiscale 3D convolutional networks to perform both
tasks. We find that the error-detecting net can achieve high accuracy. The
accuracy of the error-correcting net is enhanced if its input object mask is
"advice" (union of erroneous objects) from the error-detecting net. | [
"cs.CV"
] |
Over the past decade, Deep Convolutional Neural Networks have been widely
adopted for medical image segmentation and shown to achieve adequate
performance. However, due to the inherent inductive biases present in the
convolutional architectures, they lack understanding of long-range dependencies
in the image. Recently proposed Transformer-based architectures that leverage
self-attention mechanism encode long-range dependencies and learn
representations that are highly expressive. This motivates us to explore
Transformer-based solutions and study the feasibility of using
Transformer-based network architectures for medical image segmentation tasks.
Majority of existing Transformer-based network architectures proposed for
vision applications require large-scale datasets to train properly. However,
compared to the datasets for vision applications, for medical imaging the
number of data samples is relatively low, making it difficult to efficiently
train transformers for medical applications. To this end, we propose a Gated
Axial-Attention model which extends the existing architectures by introducing
an additional control mechanism in the self-attention module. Furthermore, to
train the model effectively on medical images, we propose a Local-Global
training strategy (LoGo) which further improves the performance. Specifically,
we operate on the whole image and patches to learn global and local features,
respectively. The proposed Medical Transformer (MedT) is evaluated on three
different medical image segmentation datasets and it is shown that it achieves
better performance than the convolutional and other related transformer-based
architectures. Code: https://github.com/jeya-maria-jose/Medical-Transformer | [
"cs.CV"
] |
With the rapid development of technology, automobiles have become an
essential asset in our day-to-day lives. One of the more important researches
is Traffic Signs Recognition (TSR) systems. This paper describes an approach
for efficiently detecting and recognizing traffic signs in real-time, taking
into account the various weather, illumination and visibility challenges
through the means of transfer learning. We tackle the traffic sign detection
problem using the state-of-the-art of multi-object detection systems such as
Faster Recurrent Convolutional Neural Networks (F-RCNN) and Single Shot Multi-
Box Detector (SSD) combined with various feature extractors such as MobileNet
v1 and Inception v2, and also Tiny-YOLOv2. However, the focus of this paper is
going to be F-RCNN Inception v2 and Tiny YOLO v2 as they achieved the best
results. The aforementioned models were fine-tuned on the German Traffic Signs
Detection Benchmark (GTSDB) dataset. These models were tested on the host PC as
well as Raspberry Pi 3 Model B+ and the TASS PreScan simulation. We will
discuss the results of all the models in the conclusion section. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent
multi-dimensional, structured data such as images. The simplest solution to
solve this computationally hard problem is to decompose it into independent
layer-wise subproblems. However, neuroscientific evidence would suggest
inter-connecting these subproblems as in the Predictive Coding (PC) theory,
which adds top-down connections between consecutive layers. In this study, a
new model called 2-Layers Sparse Predictive Coding (2L-SPC) is introduced to
assess the impact of this inter-layer feedback connection. In particular, the
2L-SPC is compared with a Hierarchical Lasso (Hi-La) network made out of a
sequence of independent Lasso layers. The 2L-SPC and the 2-layers Hi-La
networks are trained on 4 different databases and with different sparsity
parameters on each layer. First, we show that the overall prediction error
generated by 2L-SPC is lower thanks to the feedback mechanism as it transfers
prediction error between layers. Second, we demonstrate that the inference
stage of the 2L-SPC is faster to converge than for the Hi-La model. Third, we
show that the 2L-SPC also accelerates the learning process. Finally, the
qualitative analysis of both models dictionaries, supported by their activation
probability, show that the 2L-SPC features are more generic and informative. | [
"cs.CV"
] |
Recent deep learning approaches for representation learning on graphs follow
a neighborhood aggregation procedure. We analyze some important properties of
these models, and propose a strategy to overcome those. In particular, the
range of "neighboring" nodes that a node's representation draws from strongly
depends on the graph structure, analogous to the spread of a random walk. To
adapt to local neighborhood properties and tasks, we explore an architecture --
jumping knowledge (JK) networks -- that flexibly leverages, for each node,
different neighborhood ranges to enable better structure-aware representation.
In a number of experiments on social, bioinformatics and citation networks, we
demonstrate that our model achieves state-of-the-art performance. Furthermore,
combining the JK framework with models like Graph Convolutional Networks,
GraphSAGE and Graph Attention Networks consistently improves those models'
performance. | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] |
Deep Graph Neural Networks (GNNs) show promising performance on a range of
graph tasks, yet at present are costly to run and lack many of the
optimisations applied to DNNs. We show, for the first time, how to
systematically quantise GNNs with minimal or no loss in performance using
Network Architecture Search (NAS). We define the possible quantisation search
space of GNNs. The proposed novel NAS mechanism, named Low Precision Graph NAS
(LPGNAS), constrains both architecture and quantisation choices to be
differentiable. LPGNAS learns the optimal architecture coupled with the best
quantisation strategy for different components in the GNN automatically using
back-propagation in a single search round. On eight different datasets, solving
the task of classifying unseen nodes in a graph, LPGNAS generates quantised
models with significant reductions in both model and buffer sizes but with
similar accuracy to manually designed networks and other NAS results. In
particular, on the Pubmed dataset, LPGNAS shows a better size-accuracy Pareto
frontier compared to seven other manual and searched baselines, offering a 2.3
times reduction in model size but a 0.4% increase in accuracy when compared to
the best NAS competitor. Finally, from our collected quantisation statistics on
a wide range of datasets, we suggest a W4A8 (4-bit weights, 8-bit activations)
quantisation strategy might be the bottleneck for naive GNN quantisations. | [
"cs.LG",
"cs.NE"
] |
Modern CNN-based object detectors focus on feature configuration during
training but often ignore feature optimization during inference. In this paper,
we propose a new feature optimization approach to enhance features and suppress
background noise in both the training and inference stages. We introduce a
generic Inference-aware Feature Filtering (IFF) module that can easily be
combined with modern detectors, resulting in our iffDetector. Unlike
conventional open-loop feature calculation approaches without feedback, the IFF
module performs closed-loop optimization by leveraging high-level semantics to
enhance the convolutional features. By applying Fourier transform analysis, we
demonstrate that the IFF module acts as a negative feedback that theoretically
guarantees the stability of feature learning. IFF can be fused with CNN-based
object detectors in a plug-and-play manner with negligible computational cost
overhead. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that
our iffDetector consistently outperforms state-of-the-art methods by
significant margins\footnote{The test code and model are anonymously available
in https://github.com/anonymous2020new/iffDetector }. | [
"cs.CV"
] |
Many machine learning tasks such as multiple instance learning, 3D shape
recognition, and few-shot image classification are defined on sets of
instances. Since solutions to such problems do not depend on the order of
elements of the set, models used to address them should be permutation
invariant. We present an attention-based neural network module, the Set
Transformer, specifically designed to model interactions among elements in the
input set. The model consists of an encoder and a decoder, both of which rely
on attention mechanisms. In an effort to reduce computational complexity, we
introduce an attention scheme inspired by inducing point methods from sparse
Gaussian process literature. It reduces the computation time of self-attention
from quadratic to linear in the number of elements in the set. We show that our
model is theoretically attractive and we evaluate it on a range of tasks,
demonstrating the state-of-the-art performance compared to recent methods for
set-structured data. | [
"cs.LG",
"stat.ML"
] |
Recently the field of inverse problems has seen a growing usage of
mathematically only partially understood learned and non-learned priors. Based
on first principles, we develop a projectional approach to inverse problems
that addresses the incorporation of these priors, while still guaranteeing data
consistency. We implement this projectional method (PM) on the one hand via
very general Plug-and-Play priors and on the other hand, via an end-to-end
training approach. To this end, we introduce a novel alternating neural
architecture, allowing for the incorporation of highly customized priors from
data in a principled manner. We also show how the recent success of
Regularization by Denoising (RED) can, at least to some extent, be explained as
an approximation of the PM. Furthermore, we demonstrate how the idea can be
applied to stop the degradation of Deep Image Prior (DIP) reconstructions over
time. | [
"cs.LG",
"cs.CV",
"math.FA",
"stat.ML"
] |
As constituent parts of image objects, superpixels can improve several
higher-level operations. However, image segmentation methods might have their
accuracy seriously compromised for reduced numbers of superpixels. We have
investigated a solution based on the Iterative Spanning Forest (ISF) framework.
In this work, we present Dynamic ISF (DISF) -- a method based on the following
steps. (a) It starts from an image graph and a seed set with considerably more
pixels than the desired number of superpixels. (b) The seeds compete among
themselves, and each seed conquers its most closely connected pixels, resulting
in an image partition (spanning forest) with connected superpixels. In step
(c), DISF assigns relevance values to seeds based on superpixel analysis and
removes the most irrelevant ones. Steps (b) and (c) are repeated until the
desired number of superpixels is reached. DISF has the chance to reconstruct
relevant edges after each iteration, when compared to region merging
algorithms. As compared to other seed-based superpixel methods, DISF is more
likely to find relevant seeds. It also introduces dynamic arc-weight estimation
in the ISF framework for more effective superpixel delineation, and we
demonstrate all results on three datasets with distinct object properties. | [
"cs.CV"
] |
AI agents are being developed to support high stakes decision-making
processes from driving cars to prescribing drugs, making it increasingly
important for human users to understand their behavior. Policy summarization
methods aim to convey strengths and weaknesses of such agents by demonstrating
their behavior in a subset of informative states. Some policy summarization
methods extract a summary that optimizes the ability to reconstruct the agent's
policy under the assumption that users will deploy inverse reinforcement
learning. In this paper, we explore the use of different models for extracting
summaries. We introduce an imitation learning-based approach to policy
summarization; we demonstrate through computational simulations that a mismatch
between the model used to extract a summary and the model used to reconstruct
the policy results in worse reconstruction quality; and we demonstrate through
a human-subject study that people use different models to reconstruct policies
in different contexts, and that matching the summary extraction model to these
can improve performance. Together, our results suggest that it is important to
carefully consider user models in policy summarization. | [
"cs.LG",
"stat.ML"
] |
Graph Convolutional Network (GCN) is an emerging technique for information
retrieval (IR) applications. While GCN assumes the homophily property of a
graph, real-world graphs are never perfect: the local structure of a node may
contain discrepancy, e.g., the labels of a node's neighbors could vary. This
pushes us to consider the discrepancy of local structure in GCN modeling.
Existing work approaches this issue by introducing an additional module such as
graph attention, which is expected to learn the contribution of each neighbor.
However, such module may not work reliably as expected, especially when there
lacks supervision signal, e.g., when the labeled data is small. Moreover,
existing methods focus on modeling the nodes in the training data, and never
consider the local structure discrepancy of testing nodes.
This work focuses on the local structure discrepancy issue for testing nodes,
which has received little scrutiny. From a novel perspective of causality, we
investigate whether a GCN should trust the local structure of a testing node
when predicting its label. To this end, we analyze the working mechanism of GCN
with causal graph, estimating the causal effect of a node's local structure for
the prediction. The idea is simple yet effective: given a trained GCN model, we
first intervene the prediction by blocking the graph structure; we then compare
the original prediction with the intervened prediction to assess the causal
effect of the local structure on the prediction. Through this way, we can
eliminate the impact of local structure discrepancy and make more accurate
prediction. Extensive experiments on seven node classification datasets show
that our method effectively enhances the inference stage of GCN. | [
"cs.LG",
"stat.ML"
] |
Several social, medical, engineering and biological challenges rely on
discovering the functionality of networks from their structure and node
metadata, when it is available. For example, in chemoinformatics one might want
to detect whether a molecule is toxic based on structure and atomic types, or
discover the research field of a scientific collaboration network. Existing
techniques rely on counting or measuring structural patterns that are known to
show large variations from network to network, such as the number of triangles,
or the assortativity of node metadata. We introduce the concept of multi-hop
assortativity, that captures the similarity of the nodes situated at the
extremities of a randomly selected path of a given length. We show that
multi-hop assortativity unifies various existing concepts and offers a
versatile family of 'fingerprints' to characterize networks. These fingerprints
allow in turn to recover the functionalities of a network, with the help of the
machine learning toolbox. Our method is evaluated empirically on established
social and chemoinformatic network benchmarks. Results reveal that our
assortativity based features are competitive providing highly accurate results
often outperforming state of the art methods for the network classification
task. | [
"cs.LG",
"cs.SI",
"stat.ML"
] |
The problem of graph learning concerns the construction of an explicit
topological structure revealing the relationship between nodes representing
data entities, which plays an increasingly important role in the success of
many graph-based representations and algorithms in the field of machine
learning and graph signal processing. In this paper, we propose a novel graph
learning framework that incorporates the node-side and observation-side
information, and in particular the covariates that help to explain the
dependency structures in graph signals. To this end, we consider graph signals
as functions in the reproducing kernel Hilbert space associated with a
Kronecker product kernel, and integrate functional learning with
smoothness-promoting graph learning to learn a graph representing the
relationship between nodes. The functional learning increases the robustness of
graph learning against missing and incomplete information in the graph signals.
In addition, we develop a novel graph-based regularisation method which, when
combined with the Kronecker product kernel, enables our model to capture both
the dependency explained by the graph and the dependency due to graph signals
observed under different but related circumstances, e.g. different points in
time. The latter means the graph signals are free from the i.i.d. assumptions
required by the classical graph learning models. Experiments on both synthetic
and real-world data show that our methods outperform the state-of-the-art
models in learning a meaningful graph topology from graph signals, in
particular under heavy noise, missing values, and multiple dependency. | [
"stat.ML",
"cs.LG",
"cs.SI",
"eess.SP"
] |
We present a new stage-wise learning paradigm for training generative
adversarial networks (GANs). The goal of our work is to progressively
strengthen the discriminator and thus, the generators, with each subsequent
stage without changing the network architecture. We call this proposed method
the RankGAN. We first propose a margin-based loss for the GAN discriminator. We
then extend it to a margin-based ranking loss to train the multiple stages of
RankGAN. We focus on face images from the CelebA dataset in our work and show
visual as well as quantitative improvements in face generation and completion
tasks over other GAN approaches, including WGAN and LSGAN. | [
"cs.CV"
] |
Video Question Answering (VideoQA) is a challenging video understanding task
since it requires a deep understanding of both question and video. Previous
studies mainly focus on extracting sophisticated visual and language
embeddings, fusing them by delicate hand-crafted networks. However, the
relevance of different frames, objects, and modalities to the question are
varied along with the time, which is ignored in most of existing methods.
Lacking understanding of the the dynamic relationships and interactions among
objects brings a great challenge to VideoQA task. To address this problem, we
propose a novel Relation-aware Hierarchical Attention (RHA) framework to learn
both the static and dynamic relations of the objects in videos. In particular,
videos and questions are embedded by pre-trained models firstly to obtain the
visual and textual features. Then a graph-based relation encoder is utilized to
extract the static relationship between visual objects. To capture the dynamic
changes of multimodal objects in different video frames, we consider the
temporal, spatial, and semantic relations, and fuse the multimodal features by
hierarchical attention mechanism to predict the answer. We conduct extensive
experiments on a large scale VideoQA dataset, and the experimental results
demonstrate that our RHA outperforms the state-of-the-art methods. | [
"cs.CV",
"cs.AI"
] |
Time series forecasting is essential for agents to make decisions in many
domains. Existing models rely on classical statistical methods to predict
future values based on previously observed numerical information. Yet,
practitioners often rely on visualizations such as charts and plots to reason
about their predictions. Inspired by the end-users, we re-imagine the topic by
creating a framework to produce visual forecasts, similar to the way humans
intuitively do. In this work, we take a novel approach by leveraging advances
in deep learning to extend the field of time series forecasting to a visual
setting. We do this by transforming the numerical analysis problem into the
computer vision domain. Using visualizations of time series data as input, we
train a convolutional autoencoder to produce corresponding visual forecasts. We
examine various synthetic and real datasets with diverse degrees of complexity.
Our experiments show that visual forecasting is effective for cyclic data but
somewhat less for irregular data such as stock price. Importantly, we find the
proposed visual forecasting method to outperform numerical baselines. We
attribute the success of the visual forecasting approach to the fact that we
convert the continuous numerical regression problem into a discrete domain with
quantization of the continuous target signal into pixel space. | [
"cs.CV",
"cs.LG",
"econ.EM"
] |
Learning causal effects from observational data greatly benefits a variety of
domains such as health care, education and sociology. For instance, one could
estimate the impact of a new drug on specific individuals to assist the clinic
plan and improve the survival rate. In this paper, we focus on studying the
problem of estimating Conditional Average Treatment Effect (CATE) from
observational data. The challenges for this problem are two-fold: on the one
hand, we have to derive a causal estimator to estimate the causal quantity from
observational data, where there exists confounding bias; on the other hand, we
have to deal with the identification of CATE when the distribution of
covariates in treatment and control groups are imbalanced. To overcome these
challenges, we propose a neural network framework called Adversarial
Balancing-based representation learning for Causal Effect Inference (ABCEI),
based on the recent advances in representation learning. To ensure the
identification of CATE, ABCEI uses adversarial learning to balance the
distributions of covariates in treatment and control groups in the latent
representation space, without any assumption on the form of the treatment
selection/assignment function. In addition, during the representation learning
and balancing process, highly predictive information from the original
covariate space might be lost. ABCEI can tackle this information loss problem
by preserving useful information for predicting causal effects under the
regularization of a mutual information estimator. The experimental results show
that ABCEI is robust against treatment selection bias, and matches/outperforms
the state-of-the-art approaches. Our experiments show promising results on
several datasets, representing different health care domains among others. | [
"cs.LG",
"stat.ML"
] |
Substantial increase in the use of Electronic Health Records (EHRs) has
opened new frontiers for predictive healthcare. However, while EHR systems are
nearly ubiquitous, they lack a unified code system for representing medical
concepts. Heterogeneous formats of EHR present a substantial barrier for the
training and deployment of state-of-the-art deep learning models at scale. To
overcome this problem, we introduce Description-based Embedding, DescEmb, a
code-agnostic description-based representation learning framework for
predictive modeling on EHR. DescEmb takes advantage of the flexibility of
neural language understanding models while maintaining a neutral approach that
can be combined with prior frameworks for task-specific representation learning
or predictive modeling. We tested our model's capacity on various experiments
including prediction tasks, transfer learning and pooled learning. DescEmb
shows higher performance in overall experiments compared to code-based
approach, opening the door to a text-based approach in predictive healthcare
research that is not constrained by EHR structure nor special domain knowledge. | [
"cs.LG",
"cs.NE"
] |
Deep reinforcement learning has emerged as a promising and powerful technique
for automatically acquiring control policies that can process raw sensory
inputs, such as images, and perform complex behaviors. However, extending deep
RL to real-world robotic tasks has proven challenging, particularly in
safety-critical domains such as autonomous flight, where a trial-and-error
learning process is often impractical. In this paper, we explore the following
question: can we train vision-based navigation policies entirely in simulation,
and then transfer them into the real world to achieve real-world flight without
a single real training image? We propose a learning method that we call
CAD$^2$RL, which can be used to perform collision-free indoor flight in the
real world while being trained entirely on 3D CAD models. Our method uses
single RGB images from a monocular camera, without needing to explicitly
reconstruct the 3D geometry of the environment or perform explicit motion
planning. Our learned collision avoidance policy is represented by a deep
convolutional neural network that directly processes raw monocular images and
outputs velocity commands. This policy is trained entirely on simulated images,
with a Monte Carlo policy evaluation algorithm that directly optimizes the
network's ability to produce collision-free flight. By highly randomizing the
rendering settings for our simulated training set, we show that we can train a
policy that generalizes to the real world, without requiring the simulator to
be particularly realistic or high-fidelity. We evaluate our method by flying a
real quadrotor through indoor environments, and further evaluate the design
choices in our simulator through a series of ablation studies on depth
prediction. For supplementary video see: https://youtu.be/nXBWmzFrj5s | [
"cs.LG",
"cs.CV",
"cs.RO"
] |
Face reenactment is a challenging task, as it is difficult to maintain
accurate expression, pose and identity simultaneously. Most existing methods
directly apply driving facial landmarks to reenact source faces and ignore the
intrinsic gap between two identities, resulting in the identity mismatch issue.
Besides, they neglect the entanglement of expression and pose features when
encoding driving faces, leading to inaccurate expressions and visual artifacts
on large-pose reenacted faces. To address these problems, we propose a
Large-pose Identity-preserving face reenactment network, LI-Net. Specifically,
the Landmark Transformer is adopted to adjust driving landmark images, which
aims to narrow the identity gap between driving and source landmark images.
Then the Face Rotation Module and the Expression Enhancing Generator decouple
the transformed landmark image into pose and expression features, and reenact
those attributes separately to generate identity-preserving faces with accurate
expressions and poses. Both qualitative and quantitative experimental results
demonstrate the superiority of our method. | [
"cs.CV",
"cs.AI",
"cs.MM"
] |
The interpretation of feature importance in machine learning models is
challenging when features are dependent. Permutation feature importance (PFI)
ignores such dependencies, which can cause misleading interpretations due to
extrapolation. A possible remedy is more advanced conditional PFI approaches
that enable the assessment of feature importance conditional on all other
features. Due to this shift in perspective and in order to enable correct
interpretations, it is therefore important that the conditioning is transparent
and humanly comprehensible. In this paper, we propose a new sampling mechanism
for the conditional distribution based on permutations in conditional
subgroups. As these subgroups are constructed using decision trees
(transformation trees), the conditioning becomes inherently interpretable. This
not only provides a simple and effective estimator of conditional PFI, but also
local PFI estimates within the subgroups. In addition, we apply the conditional
subgroups approach to partial dependence plots (PDP), a popular method for
describing feature effects that can also suffer from extrapolation when
features are dependent and interactions are present in the model. We show that
PFI and PDP based on conditional subgroups often outperform methods such as
conditional PFI based on knockoffs, or accumulated local effect plots.
Furthermore, our approach allows for a more fine-grained interpretation of
feature effects and importance within the conditional subgroups. | [
"stat.ML",
"cs.LG"
] |
Clustering is an unsupervised machine learning method grouping data samples
into clusters of similar objects. In practice, clustering has been used in
numerous applications such as banking customers profiling, document retrieval,
image segmentation, and e-commerce recommendation engines. However, the
existing clustering techniques present significant limitations, from which is
the dependability of their stability on the initialization parameters (e.g.
number of clusters, centroids). Different solutions were presented in the
literature to overcome this limitation (i.e. internal and external validation
metrics). However, these solutions require high computational complexity and
memory consumption, especially when dealing with big data. In this paper, we
apply the recent object detection Deep Learning (DL) model, named YOLO-v5, to
detect the initial clustering parameters such as the number of clusters with
their sizes and centroids. Mainly, the proposed solution consists of adding a
DL-based initialization phase making the clustering algorithms free of
initialization. Two model solutions are provided in this work, one for isolated
clusters and the other one for overlapping clusters. The features of the
incoming dataset determine which model to use. Moreover, The results show that
the proposed solution can provide near-optimal clusters initialization
parameters with low computational and resources overhead compared to existing
solutions. | [
"cs.CV"
] |
Shapley values are one of the main tools used to explain predictions of tree
ensemble models. The main alternative to Shapley values are Banzhaf values that
have not been understood equally well. In this paper we make a step towards
filling this gap, providing both experimental and theoretical comparison of
these model explanation methods. Surprisingly, we show that Banzhaf values
offer several advantages over Shapley values while providing essentially the
same explanations. We verify that Banzhaf values: (1) have a more intuitive
interpretation, (2) allow for more efficient algorithms, and (3) are much more
numerically robust. We provide an experimental evaluation of these theses. In
particular, we show that on real world instances.
Additionally, from a theoretical perspective we provide new and improved
algorithm computing the same Shapley value based explanations as the algorithm
of Lundberg et al. [Nat. Mach. Intell. 2020]. Our algorithm runs in $O(TLD+n)$
time, whereas the previous algorithm had $O(TLD^2+n)$ running time bound. Here,
$T$ is the number of trees, $L$ is the maximum number of leaves in a tree, and
$D$ denotes the maximum depth of a tree in the ensemble. Using the
computational techniques developed for Shapley values we deliver an optimal
$O(TL+n)$ time algorithm for computing Banzhaf values based explanations. In
our experiments these algorithms give running times smaller even by an order of
magnitude. | [
"cs.LG"
] |
Pedestrian detection is an important component for safety of autonomous
vehicles, as well as for traffic and street surveillance. There are extensive
benchmarks on this topic and it has been shown to be a challenging problem when
applied on real use-case scenarios. In purely image-based pedestrian detection
approaches, the state-of-the-art results have been achieved with convolutional
neural networks (CNN) and surprisingly few detection frameworks have been built
upon multi-cue approaches. In this work, we develop a new pedestrian detector
for autonomous vehicles that exploits LiDAR data, in addition to visual
information. In the proposed approach, LiDAR data is utilized to generate
region proposals by processing the three dimensional point cloud that it
provides. These candidate regions are then further processed by a
state-of-the-art CNN classifier that we have fine-tuned for pedestrian
detection. We have extensively evaluated the proposed detection process on the
KITTI dataset. The experimental results show that the proposed LiDAR space
clustering approach provides a very efficient way of generating region
proposals leading to higher recall rates and fewer misses for pedestrian
detection. This indicates that LiDAR data can provide auxiliary information for
CNN-based approaches. | [
"cs.CV"
] |
In e-commerce industry, user behavior sequence data has been widely used in
many business units such as search and merchandising to improve their products.
However, it is rarely used in financial services not only due to its 3V
characteristics - i.e. Volume, Velocity and Variety - but also due to its
unstructured nature. In this paper, we propose a Financial Service scenario
Deep learning based Behavior data representation method for Clustering
(FinDeepBehaviorCluster) to detect fraudulent transactions. To utilize the
behavior sequence data, we treat click stream data as event sequence, use time
attention based Bi-LSTM to learn the sequence embedding in an unsupervised
fashion, and combine them with intuitive features generated by risk experts to
form a hybrid feature representation. We also propose a GPU powered HDBSCAN
(pHDBSCAN) algorithm, which is an engineering optimization for the original
HDBSCAN algorithm based on FAISS project, so that clustering can be carried out
on hundreds of millions of transactions within a few minutes. The computation
efficiency of the algorithm has increased 500 times compared with the original
implementation, which makes flash fraud pattern detection feasible. Our
experimental results show that the proposed FinDeepBehaviorCluster framework is
able to catch missed fraudulent transactions with considerable business values.
In addition, rule extraction method is applied to extract patterns from risky
clusters using intuitive features, so that narrative descriptions can be
attached to the risky clusters for case investigation, and unknown risk
patterns can be mined for real-time fraud detection. In summary,
FinDeepBehaviorCluster as a complementary risk management strategy to the
existing real-time fraud detection engine, can further increase our fraud
detection and proactive risk defense capabilities. | [
"cs.LG"
] |
Convolutional neural networks (CNNs) have been the de facto standard for
nowadays 3D medical image segmentation. The convolutional operations used in
these networks, however, inevitably have limitations in modeling the long-range
dependency due to their inductive bias of locality and weight sharing. Although
Transformer was born to address this issue, it suffers from extreme
computational and spatial complexities in processing high-resolution 3D feature
maps. In this paper, we propose a novel framework that efficiently bridges a
{\bf Co}nvolutional neural network and a {\bf Tr}ansformer {\bf (CoTr)} for
accurate 3D medical image segmentation. Under this framework, the CNN is
constructed to extract feature representations and an efficient deformable
Transformer (DeTrans) is built to model the long-range dependency on the
extracted feature maps. Different from the vanilla Transformer which treats all
image positions equally, our DeTrans pays attention only to a small set of key
positions by introducing the deformable self-attention mechanism. Thus, the
computational and spatial complexities of DeTrans have been greatly reduced,
making it possible to process the multi-scale and high-resolution feature maps,
which are usually of paramount importance for image segmentation. We conduct an
extensive evaluation on the Multi-Atlas Labeling Beyond the Cranial Vault (BCV)
dataset that covers 11 major human organs. The results indicate that our CoTr
leads to a substantial performance improvement over other CNN-based,
transformer-based, and hybrid methods on the 3D multi-organ segmentation task.
Code is available at \def\UrlFont{\rm\small\ttfamily}
\url{https://github.com/YtongXie/CoTr} | [
"cs.CV"
] |
Generative Adversarial Networks have been shown to be powerful in generating
content. To this end, they have been studied intensively in the last few years.
Nonetheless, training these networks requires solving a saddle point problem
that is difficult to solve and slowly converging. Motivated from techniques in
the registration of point clouds and by the fluid flow formulation of mass
transport, we investigate a new formulation that is based on strict
minimization, without the need for the maximization. The formulation views the
problem as a matching problem rather than an adversarial one and thus allows us
to quickly converge and obtain meaningful metrics in the optimization path. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
The scanning electron microscope (SEM) produces an image of a sample by
scanning it with a focused beam of electrons. The electrons interact with the
atoms in the sample, which emit secondary electrons that contain information
about the surface topography and composition. The sample is scanned by the
electron beam point by point, until an image of the surface is formed. Since
its invention in 1942, SEMs have become paramount in the discovery and
understanding of the nanometer world, and today it is extensively used for both
research and in industry. In principle, SEMs can achieve resolution better than
one nanometer. However, for many applications, working at sub-nanometer
resolution implies an exceedingly large number of scanning points. For exactly
this reason, the SEM diagnostics of microelectronic chips is performed either
at high resolution (HR) over a small area or at low resolution (LR) while
capturing a larger portion of the chip. Here, we employ sparse coding and
dictionary learning to algorithmically enhance LR SEM images of microelectronic
chips up to the level of the HR images acquired by slow SEM scans, while
considerably reducing the noise. Our methodology consists of two steps: an
offline stage of learning a joint dictionary from a sequence of LR and HR
images of the same region in the chip, followed by a fast-online
super-resolution step where the resolution of a new LR image is enhanced. We
provide several examples with typical chips used in the microelectronics
industry, as well as a statistical study on arbitrary images with
characteristic structural features. Conceptually, our method works well when
the images have similar characteristics. This work demonstrates that employing
sparsity concepts can greatly improve the performance of SEM, thereby
considerably increasing the scanning throughput without compromising on
analysis quality and resolution. | [
"cs.CV"
] |
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