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To combine explicit and implicit generative models, we introduce
semi-implicit generator (SIG) as a flexible hierarchical model that can be
trained in the maximum likelihood framework. Both theoretically and
experimentally, we demonstrate that SIG can generate high quality samples
especially when dealing with multi-modality. By introducing SIG as an unbiased
regularizer to the generative adversarial network (GAN), we show the interplay
between maximum likelihood and adversarial learning can stabilize the
adversarial training, resist the notorious mode collapsing problem of GANs, and
improve the diversity of generated random samples. | [
"stat.ML",
"cs.LG"
] |
Spectral clustering (SC) is a popular clustering technique to find strongly
connected communities on a graph. SC can be used in Graph Neural Networks
(GNNs) to implement pooling operations that aggregate nodes belonging to the
same cluster. However, the eigendecomposition of the Laplacian is expensive
and, since clustering results are graph-specific, pooling methods based on SC
must perform a new optimization for each new sample. In this paper, we propose
a graph clustering approach that addresses these limitations of SC. We
formulate a continuous relaxation of the normalized minCUT problem and train a
GNN to compute cluster assignments that minimize this objective. Our GNN-based
implementation is differentiable, does not require to compute the spectral
decomposition, and learns a clustering function that can be quickly evaluated
on out-of-sample graphs. From the proposed clustering method, we design a graph
pooling operator that overcomes some important limitations of state-of-the-art
graph pooling techniques and achieves the best performance in several
supervised and unsupervised tasks. | [
"cs.LG",
"stat.ML"
] |
Recent research on the application of remote sensing and deep learning-based
analysis in precision agriculture demonstrated a potential for improved crop
management and reduced environmental impacts of agricultural production.
Despite the promising results, the practical relevance of these technologies
for actual field deployment requires novel algorithms that are customized for
analysis of agricultural images and robust to implementation on natural field
imagery. The paper presents an approach for analyzing aerial images of a potato
crop using deep neural networks. The main objective is to demonstrate automated
spatial recognition of a healthy versus stressed crop at a plant level.
Specifically, we examine premature plant senescence resulting in drought stress
on Russet Burbank potato plants. The proposed deep learning model, named
Retina-UNet-Ag, is a variant of Retina-UNet (Jaeger et al., 2018) and includes
connections from low-level semantic dense representation maps to the feature
pyramid network. The paper also introduces a dataset of field images acquired
with a Parrot Sequoia camera carried by a Solo unmanned aerial vehicle.
Experimental validation demonstrated the ability for distinguishing healthy and
stressed plants in field images, achieving an average Dice score coefficient of
0.74. A comparison to related state-of-the-art deep learning models for object
detection revealed that the presented approach is effective for the task at
hand. The method applied here is conducive toward the assessment and
recognition of potato crop stress (early plant senescence resulting from
drought stress in this case) in natural aerial field images collected under
real conditions. | [
"cs.CV",
"cs.LG"
] |
Image segmentation is a primary task in many medical applications. Recently,
many deep networks derived from U-Net have been extensively used in various
medical image segmentation tasks. However, in most of the cases, networks
similar to U-net produce coarse and non-smooth segmentations with lots of
discontinuities. To improve and refine the performance of U-Net like networks,
we propose the use of parallel decoders which along with performing the mask
predictions also perform contour prediction and distance map estimation. The
contour and distance map aid in ensuring smoothness in the segmentation
predictions. To facilitate joint training of three tasks, we propose a novel
architecture called Psi-Net with a single encoder and three parallel decoders
(thus having a shape of $\Psi$), one decoder to learns the segmentation mask
prediction and other two decoders to learn the auxiliary tasks of contour
detection and distance map estimation. The learning of these auxiliary tasks
helps in capturing the shape and the boundary information. We also propose a
new joint loss function for the proposed architecture. The loss function
consists of a weighted combination of Negative Log likelihood and Mean Square
Error loss. We have used two publicly available datasets: 1) Origa dataset for
the task of optic cup and disc segmentation and 2) Endovis segment dataset for
the task of polyp segmentation to evaluate our model. We have conducted
extensive experiments using our network to show our model gives better results
in terms of segmentation, boundary and shape metrics. | [
"cs.CV"
] |
Recent studies have exposed that many graph neural networks (GNNs) are
sensitive to adversarial attacks, and can suffer from performance loss if the
graph structure is intentionally perturbed. A different line of research has
shown that many GNN architectures implicitly assume that the underlying graph
displays homophily, i.e., connected nodes are more likely to have similar
features and class labels, and perform poorly if this assumption is not
fulfilled. In this work, we formalize the relation between these two seemingly
different issues. We theoretically show that in the standard scenario in which
node features exhibit homophily, impactful structural attacks always lead to
increased levels of heterophily. Then, inspired by GNN architectures that
target heterophily, we present two designs -- (i) separate aggregators for ego-
and neighbor-embeddings, and (ii) a reduced scope of aggregation -- that can
significantly improve the robustness of GNNs. Our extensive empirical
evaluations show that GNNs featuring merely these two designs can achieve
significantly improved robustness compared to the best-performing unvaccinated
model with 24.99% gain in average performance under targeted attacks, while
having smaller computational overhead than existing defense mechanisms.
Furthermore, these designs can be readily combined with explicit defense
mechanisms to yield state-of-the-art robustness with up to 18.33% increase in
performance under attacks compared to the best-performing vaccinated model. | [
"cs.LG",
"stat.ML"
] |
Learning to solve complex sequences of tasks--while both leveraging transfer
and avoiding catastrophic forgetting--remains a key obstacle to achieving
human-level intelligence. The progressive networks approach represents a step
forward in this direction: they are immune to forgetting and can leverage prior
knowledge via lateral connections to previously learned features. We evaluate
this architecture extensively on a wide variety of reinforcement learning tasks
(Atari and 3D maze games), and show that it outperforms common baselines based
on pretraining and finetuning. Using a novel sensitivity measure, we
demonstrate that transfer occurs at both low-level sensory and high-level
control layers of the learned policy. | [
"cs.LG"
] |
In this paper, we propose a model-driven method that reconstructs LoD-2
building models following a "decomposition-optimization-fitting" paradigm. The
proposed method starts building detection results through a deep learning-based
detector and vectorizes individual segments into polygons using a "three-step"
polygon extraction method, followed by a novel grid-based decomposition method
that decomposes the complex and irregularly shaped building polygons to tightly
combined elementary building rectangles ready to fit elementary building
models. We have optionally introduced OpenStreetMap (OSM) and Graph-Cut (GC)
labeling to further refine the orientation of 2D building rectangle. The 3D
modeling step takes building-specific parameters such as hip lines, as well as
non-rigid and regularized transformations to optimize the flexibility for using
a minimal set of elementary models. Finally, roof type of building models s
refined and adjacent building models in one building segment are merged into
the complex polygonal model. Our proposed method has addressed a few technical
caveats over existing methods, resulting in practically high-quality results,
based on our evaluation and comparative study on a diverse set of experimental
datasets of cities with different urban patterns. | [
"cs.CV"
] |
We study the unsupervised learning of CNNs for optical flow estimation using
proxy ground truth data. Supervised CNNs, due to their immense learning
capacity, have shown superior performance on a range of computer vision
problems including optical flow prediction. They however require the ground
truth flow which is usually not accessible except on limited synthetic data.
Without the guidance of ground truth optical flow, unsupervised CNNs often
perform worse as they are naturally ill-conditioned. We therefore propose a
novel framework in which proxy ground truth data generated from classical
approaches is used to guide the CNN learning. The models are further refined in
an unsupervised fashion using an image reconstruction loss. Our guided learning
approach is competitive with or superior to state-of-the-art approaches on
three standard benchmark datasets yet is completely unsupervised and can run in
real time. | [
"cs.CV"
] |
Graph neural networks (GNNs) have achieved great success in recent years.
Three most common applications include node classification, link prediction,
and graph classification. While there is rich literature on node classification
and graph classification, GNN for link prediction is relatively less studied
and less understood. One common practice in previous works is to first compute
node representations through a GNN, and then directly aggregate two node
representations as a link representation. In this paper, we show the
limitations of such an approach, and propose a labeling trick to make GNNs
learn better link representations. Labeling trick assigns labels to nodes as
their additional features according to nodes' relationships with the target
link. We show theoretically that GNNs applied to such labeled graphs can learn
most expressive link representations. We also show that one state-of-the-art
link prediction model, SEAL, exactly uses a labeling trick. Labeling trick
brings up to 195% performance gains over plain GNNs, achieving 3 first places
on the OGB link prediction leaderboard. | [
"cs.LG"
] |
The recent successful deep neural networks are largely trained in a
supervised manner. It {\it associates} complex patterns of input samples with
neurons in the last layer, which form representations of {\it concepts}. In
spite of their successes, the properties of complex patterns associated a
learned concept remain elusive. In this work, by analyzing how neurons are
associated with concepts in supervised networks, we hypothesize that with
proper priors to regulate learning, neural networks can automatically associate
neurons in the intermediate layers with concepts that are aligned with real
world concepts, when trained only with labels that associate concepts with top
level neurons, which is a plausible way for unsupervised learning. We develop a
prior to verify the hypothesis and experimentally find the proposed prior help
neural networks automatically learn both basic physical concepts at the lower
layers, e.g., rotation of filters, and highly semantic concepts at the higher
layers, e.g., fine-grained categories of an entry-level category. | [
"cs.LG"
] |
This paper develops an explainable deep learning model that estimates the
remaining useful lives of rotating machinery. The model extracts high-level
features from Fourier transform using an autoencoder. The features are used as
input to a feedforward neural network to estimate the remaining useful lives.
The paper explains the model's behavior by analyzing the composition of the
features and the relationships between the features and the estimation results.
In order to make the model explainable, the paper introduces octave-band
filtering. The filtering reduces the input size of the autoencoder and
simplifies the model. A case study demonstrates the methods to explain the
model. The study also shows that the octave band-filtering in the model
imitates the functionality of low-level convolutional layers. This result
supports the validity of using the filtering to reduce the depth of the model. | [
"cs.LG",
"eess.SP"
] |
The recently developed vision transformer (ViT) has achieved promising
results on image classification compared to convolutional neural networks.
Inspired by this, in this paper, we study how to learn multi-scale feature
representations in transformer models for image classification. To this end, we
propose a dual-branch transformer to combine image patches (i.e., tokens in a
transformer) of different sizes to produce stronger image features. Our
approach processes small-patch and large-patch tokens with two separate
branches of different computational complexity and these tokens are then fused
purely by attention multiple times to complement each other. Furthermore, to
reduce computation, we develop a simple yet effective token fusion module based
on cross attention, which uses a single token for each branch as a query to
exchange information with other branches. Our proposed cross-attention only
requires linear time for both computational and memory complexity instead of
quadratic time otherwise. Extensive experiments demonstrate that our approach
performs better than or on par with several concurrent works on vision
transformer, in addition to efficient CNN models. For example, on the
ImageNet1K dataset, with some architectural changes, our approach outperforms
the recent DeiT by a large margin of 2\% with a small to moderate increase in
FLOPs and model parameters. Our source codes and models are available at
\url{https://github.com/IBM/CrossViT}. | [
"cs.CV"
] |
In this paper we focus our attention on the exploitation of the information
contained in financial news to enhance the performance of a classifier of bank
distress. Such information should be analyzed and inserted into the predictive
model in the most efficient way and this task deals with all the issues related
to text analysis and specifically analysis of news media. Among the different
models proposed for such purpose, we investigate one of the possible deep
learning approaches, based on a doc2vec representation of the textual data, a
kind of neural network able to map the sequential and symbolic text input onto
a reduced latent semantic space. Afterwards, a second supervised neural network
is trained combining news data with standard financial figures to classify
banks whether in distressed or tranquil states, based on a small set of known
distress events. Then the final aim is not only the improvement of the
predictive performance of the classifier but also to assess the importance of
news data in the classification process. Does news data really bring more
useful information not contained in standard financial variables? Our results
seem to confirm such hypothesis. | [
"stat.ML",
"cs.LG"
] |
Sample inefficiency is a long-lasting problem in reinforcement learning (RL).
The state-of-the-art estimates the optimal action values while it usually
involves an extensive search over the state-action space and unstable
optimization. Towards the sample-efficient RL, we propose ranking policy
gradient (RPG), a policy gradient method that learns the optimal rank of a set
of discrete actions. To accelerate the learning of policy gradient methods, we
establish the equivalence between maximizing the lower bound of return and
imitating a near-optimal policy without accessing any oracles. These results
lead to a general off-policy learning framework, which preserves the
optimality, reduces variance, and improves the sample-efficiency. Furthermore,
the sample complexity of RPG does not depend on the dimension of state space,
which enables RPG for large-scale problems. We conduct extensive experiments
showing that when consolidating with the off-policy learning framework, RPG
substantially reduces the sample complexity, comparing to the state-of-the-art. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
We present a novel method for multi-view depth estimation from a single
video, which is a critical task in various applications, such as perception,
reconstruction and robot navigation. Although previous learning-based methods
have demonstrated compelling results, most works estimate depth maps of
individual video frames independently, without taking into consideration the
strong geometric and temporal coherence among the frames. Moreover, current
state-of-the-art (SOTA) models mostly adopt a fully 3D convolution network for
cost regularization and therefore require high computational cost, thus
limiting their deployment in real-world applications. Our method achieves
temporally coherent depth estimation results by using a novel Epipolar
Spatio-Temporal (EST) transformer to explicitly associate geometric and
temporal correlation with multiple estimated depth maps. Furthermore, to reduce
the computational cost, inspired by recent Mixture-of-Experts models, we design
a compact hybrid network consisting of a 2D context-aware network and a 3D
matching network which learn 2D context information and 3D disparity cues
separately. Extensive experiments demonstrate that our method achieves higher
accuracy in depth estimation and significant speedup than the SOTA methods. | [
"cs.CV"
] |
In this paper, we study the problem of image recognition with
non-differentiable constraints. A lot of real-life recognition applications
require a rich output structure with deterministic constraints that are
discrete or modeled by a non-differentiable function. A prime example is
recognizing digit sequences, which are restricted by such rules (e.g.,
\textit{container code detection}, \textit{social insurance number
recognition}, etc.). We investigate the usefulness of adding non-differentiable
constraints in learning for the task of digit sequence recognition. Toward this
goal, we synthesize six different datasets from MNIST and Cropped SVHN, with
three discrete rules inspired by real-life protocols. To deal with the
non-differentiability of these rules, we propose a reinforcement learning
approach based on the policy gradient method. We find that incorporating this
rule-based reinforcement can effectively increase the accuracy for all datasets
and provide a good inductive bias which improves the model even with limited
data. On one of the datasets, MNIST\_Rule2, models trained with rule-based
reinforcement increase the accuracy by 4.7\% for 2000 samples and 23.6\% for
500 samples. We further test our model against synthesized adversarial
examples, e.g., blocking out digits, and observe that adding our rule-based
reinforcement increases the model robustness with a relatively smaller
performance drop. | [
"cs.CV"
] |
Determining which image regions to concentrate on is critical for
Human-Object Interaction (HOI) detection. Conventional HOI detectors focus on
either detected human and object pairs or pre-defined interaction locations,
which limits learning of the effective features. In this paper, we reformulate
HOI detection as an adaptive set prediction problem, with this novel
formulation, we propose an Adaptive Set-based one-stage framework (AS-Net) with
parallel instances and interaction branches. To attain this, we map a trainable
interaction query set to an interaction prediction set with a transformer. Each
query adaptively aggregates the interaction-relevant features from global
contexts through multi-head co-attention. Besides, the training process is
supervised adaptively by matching each ground truth with the interaction
prediction. Furthermore, we design an effective instance-aware attention module
to introduce instructive features from the instance branch into the interaction
branch. Our method outperforms previous state-of-the-art methods without any
extra human pose and language features on three challenging HOI detection
datasets. Especially, we achieve over $31\%$ relative improvement on a
large-scale HICO-DET dataset. Code is available at
https://github.com/yoyomimi/AS-Net. | [
"cs.CV"
] |
The goal of meta-reinforcement learning (meta-RL) is to build agents that can
quickly learn new tasks by leveraging prior experience on related tasks.
Learning a new task often requires both exploring to gather task-relevant
information and exploiting this information to solve the task. In principle,
optimal exploration and exploitation can be learned end-to-end by simply
maximizing task performance. However, such meta-RL approaches struggle with
local optima due to a chicken-and-egg problem: learning to explore requires
good exploitation to gauge the exploration's utility, but learning to exploit
requires information gathered via exploration. Optimizing separate objectives
for exploration and exploitation can avoid this problem, but prior meta-RL
exploration objectives yield suboptimal policies that gather information
irrelevant to the task. We alleviate both concerns by constructing an
exploitation objective that automatically identifies task-relevant information
and an exploration objective to recover only this information. This avoids
local optima in end-to-end training, without sacrificing optimal exploration.
Empirically, DREAM substantially outperforms existing approaches on complex
meta-RL problems, such as sparse-reward 3D visual navigation. Videos of DREAM:
https://ezliu.github.io/dream/ | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Data augmentation is a widely adopted technique for avoiding overfitting when
training deep neural networks. However, this approach requires domain-specific
knowledge and is often limited to a fixed set of hard-coded transformations.
Recently, several works proposed to use generative models for generating
semantically meaningful perturbations to train a classifier. However, because
accurate encoding and decoding are critical, these methods, which use
architectures that approximate the latent-variable inference, remained limited
to pilot studies on small datasets.
Exploiting the exactly reversible encoder-decoder structure of normalizing
flows, we perform on-manifold perturbations in the latent space to define fully
unsupervised data augmentations. We demonstrate that such perturbations match
the performance of advanced data augmentation techniques -- reaching 96.6% test
accuracy for CIFAR-10 using ResNet-18 and outperform existing methods,
particularly in low data regimes -- yielding 10--25% relative improvement of
test accuracy from classical training. We find that our latent adversarial
perturbations adaptive to the classifier throughout its training are most
effective, yielding the first test accuracy improvement results on real-world
datasets -- CIFAR-10/100 -- via latent-space perturbations. | [
"stat.ML",
"cs.LG"
] |
Humans can robustly localize themselves without a map after they get lost
following prominent visual cues or landmarks. In this work, we aim at endowing
autonomous agents the same ability. Such ability is important in robotics
applications yet very challenging when an agent is exposed to partially
calibrated environments, where camera images with accurate 6 Degree-of-Freedom
pose labels only cover part of the scene. To address the above challenge, we
explore using Reinforcement Learning to search for a policy to generate
intelligent motions so as to actively localize the agent given visual
information in partially calibrated environments. Our core contribution is to
formulate the active visual localization problem as a Partially Observable
Markov Decision Process and propose an algorithmic framework based on Deep
Reinforcement Learning to solve it. We further propose an indoor scene dataset
ACR-6, which consists of both synthetic and real data and simulates challenging
scenarios for active visual localization. We benchmark our algorithm against
handcrafted baselines for localization and demonstrate that our approach
significantly outperforms them on localization success rate. | [
"cs.CV"
] |
Variational Autoencoders (VAE) and their variants have been widely used in a
variety of applications, such as dialog generation, image generation and
disentangled representation learning. However, the existing VAE models have
some limitations in different applications. For example, a VAE easily suffers
from KL vanishing in language modeling and low reconstruction quality for
disentangling. To address these issues, we propose a novel controllable
variational autoencoder framework, ControlVAE, that combines a controller,
inspired by automatic control theory, with the basic VAE to improve the
performance of resulting generative models. Specifically, we design a new
non-linear PI controller, a variant of the proportional-integral-derivative
(PID) control, to automatically tune the hyperparameter (weight) added in the
VAE objective using the output KL-divergence as feedback during model training.
The framework is evaluated using three applications; namely, language modeling,
disentangled representation learning, and image generation. The results show
that ControlVAE can achieve better disentangling and reconstruction quality
than the existing methods. For language modelling, it not only averts the
KL-vanishing, but also improves the diversity of generated text. Finally, we
also demonstrate that ControlVAE improves the reconstruction quality of
generated images compared to the original VAE. | [
"cs.LG",
"stat.ML"
] |
This paper addresses the problem of media retrieval using a multimodal query
(a query which combines visual input with additional semantic information in
natural language feedback). We propose a SynthTriplet GAN framework which
resolves this task by expanding the multimodal query with a synthetically
generated image that captures semantic information from both image and text
input. We introduce a novel triplet mining method that uses a synthetic image
as an anchor to directly optimize for embedding distances of generated and
target images. We demonstrate that apart from the added value of retrieval
illustration with synthetic image with the focus on customization and user
feedback, the proposed method greatly surpasses other multimodal generation
methods and achieves state of the art results in the multimodal retrieval task.
We also show that in contrast to other retrieval methods, our method provides
explainable embeddings. | [
"cs.CV",
"cs.AI"
] |
We present 3D-MPA, a method for instance segmentation on 3D point clouds.
Given an input point cloud, we propose an object-centric approach where each
point votes for its object center. We sample object proposals from the
predicted object centers. Then, we learn proposal features from grouped point
features that voted for the same object center. A graph convolutional network
introduces inter-proposal relations, providing higher-level feature learning in
addition to the lower-level point features. Each proposal comprises a semantic
label, a set of associated points over which we define a foreground-background
mask, an objectness score and aggregation features. Previous works usually
perform non-maximum-suppression (NMS) over proposals to obtain the final object
detections or semantic instances. However, NMS can discard potentially correct
predictions. Instead, our approach keeps all proposals and groups them together
based on the learned aggregation features. We show that grouping proposals
improves over NMS and outperforms previous state-of-the-art methods on the
tasks of 3D object detection and semantic instance segmentation on the
ScanNetV2 benchmark and the S3DIS dataset. | [
"cs.CV"
] |
In e-commerce, a growing number of user-generated videos are used for product
promotion. How to generate video descriptions that narrate the user-preferred
product characteristics depicted in the video is vital for successful
promoting. Traditional video captioning methods, which focus on routinely
describing what exists and happens in a video, are not amenable for
product-oriented video captioning. To address this problem, we propose a
product-oriented video captioner framework, abbreviated as Poet. Poet firstly
represents the videos as product-oriented spatial-temporal graphs. Then, based
on the aspects of the video-associated product, we perform knowledge-enhanced
spatial-temporal inference on those graphs for capturing the dynamic change of
fine-grained product-part characteristics. The knowledge leveraging module in
Poet differs from the traditional design by performing knowledge filtering and
dynamic memory modeling. We show that Poet achieves consistent performance
improvement over previous methods concerning generation quality, product
aspects capturing, and lexical diversity. Experiments are performed on two
product-oriented video captioning datasets, buyer-generated fashion video
dataset (BFVD) and fan-generated fashion video dataset (FFVD), collected from
Mobile Taobao. We will release the desensitized datasets to promote further
investigations on both video captioning and general video analysis problems. | [
"cs.CV"
] |
We introduce a novel self-supervised learning approach to learn
representations of videos that are responsive to changes in the motion
dynamics. Our representations can be learned from data without human annotation
and provide a substantial boost to the training of neural networks on small
labeled data sets for tasks such as action recognition, which require to
accurately distinguish the motion of objects. We promote an accurate learning
of motion without human annotation by training a neural network to discriminate
a video sequence from its temporally transformed versions. To learn to
distinguish non-trivial motions, the design of the transformations is based on
two principles: 1) To define clusters of motions based on time warps of
different magnitude; 2) To ensure that the discrimination is feasible only by
observing and analyzing as many image frames as possible. Thus, we introduce
the following transformations: forward-backward playback, random frame
skipping, and uniform frame skipping. Our experiments show that networks
trained with the proposed method yield representations with improved transfer
performance for action recognition on UCF101 and HMDB51. | [
"cs.CV"
] |
How to effectively fuse cross-modal information is the key problem for RGB-D
salient object detection. Early fusion and the result fusion schemes fuse RGB
and depth information at the input and output stages, respectively, hence incur
the problem of distribution gap or information loss. Many models use the
feature fusion strategy but are limited by the low-order point-to-point fusion
methods. In this paper, we propose a novel mutual attention model by fusing
attention and contexts from different modalities. We use the non-local
attention of one modality to propagate long-range contextual dependencies for
the other modality, thus leveraging complementary attention cues to perform
high-order and trilinear cross-modal interaction. We also propose to induce
contrast inference from the mutual attention and obtain a unified model.
Considering low-quality depth data may detriment the model performance, we
further propose selective attention to reweight the added depth cues. We embed
the proposed modules in a two-stream CNN for RGB-D SOD. Experimental results
have demonstrated the effectiveness of our proposed model. Moreover, we also
construct a new challenging large-scale RGB-D SOD dataset with high-quality,
thus can both promote the training and evaluation of deep models. | [
"cs.CV"
] |
Most popular optimizers for deep learning can be broadly categorized as
adaptive methods (e.g. Adam) and accelerated schemes (e.g. stochastic gradient
descent (SGD) with momentum). For many models such as convolutional neural
networks (CNNs), adaptive methods typically converge faster but generalize
worse compared to SGD; for complex settings such as generative adversarial
networks (GANs), adaptive methods are typically the default because of their
stability.We propose AdaBelief to simultaneously achieve three goals: fast
convergence as in adaptive methods, good generalization as in SGD, and training
stability. The intuition for AdaBelief is to adapt the stepsize according to
the "belief" in the current gradient direction. Viewing the exponential moving
average (EMA) of the noisy gradient as the prediction of the gradient at the
next time step, if the observed gradient greatly deviates from the prediction,
we distrust the current observation and take a small step; if the observed
gradient is close to the prediction, we trust it and take a large step. We
validate AdaBelief in extensive experiments, showing that it outperforms other
methods with fast convergence and high accuracy on image classification and
language modeling. Specifically, on ImageNet, AdaBelief achieves comparable
accuracy to SGD. Furthermore, in the training of a GAN on Cifar10, AdaBelief
demonstrates high stability and improves the quality of generated samples
compared to a well-tuned Adam optimizer. Code is available at
https://github.com/juntang-zhuang/Adabelief-Optimizer | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Event classification can add valuable information for semantic search and the
increasingly important topic of fact validation in news. So far, only few
approaches address image classification for newsworthy event types such as
natural disasters, sports events, or elections. Previous work distinguishes
only between a limited number of event types and relies on rather small
datasets for training. In this paper, we present a novel ontology-driven
approach for the classification of event types in images. We leverage a large
number of real-world news events to pursue two objectives: First, we create an
ontology based on Wikidata comprising the majority of event types. Second, we
introduce a novel large-scale dataset that was acquired through Web crawling.
Several baselines are proposed including an ontology-driven learning approach
that aims to exploit structured information of a knowledge graph to learn
relevant event relations using deep neural networks. Experimental results on
existing as well as novel benchmark datasets demonstrate the superiority of the
proposed ontology-driven approach. | [
"cs.CV"
] |
We consider the problem of finitely parameterized multi-armed bandits where
the model of the underlying stochastic environment can be characterized based
on a common unknown parameter. The true parameter is unknown to the learning
agent. However, the set of possible parameters, which is finite, is known a
priori. We propose an algorithm that is simple and easy to implement, which we
call Finitely Parameterized Upper Confidence Bound (FP-UCB) algorithm, which
uses the information about the underlying parameter set for faster learning. In
particular, we show that the FP-UCB algorithm achieves a bounded regret under
some structural condition on the underlying parameter set. We also show that,
if the underlying parameter set does not satisfy the necessary structural
condition, the FP-UCB algorithm achieves a logarithmic regret, but with a
smaller preceding constant compared to the standard UCB algorithm. We also
validate the superior performance of the FP-UCB algorithm through extensive
numerical simulations. | [
"cs.LG",
"stat.ML"
] |
Attributing the pixels of an input image to a certain category is an
important and well-studied problem in computer vision, with applications
ranging from weakly supervised localisation to understanding hidden effects in
the data. In recent years, approaches based on interpreting a previously
trained neural network classifier have become the de facto state-of-the-art and
are commonly used on medical as well as natural image datasets. In this paper,
we discuss a limitation of these approaches which may lead to only a subset of
the category specific features being detected. To address this problem we
develop a novel feature attribution technique based on Wasserstein Generative
Adversarial Networks (WGAN), which does not suffer from this limitation. We
show that our proposed method performs substantially better than the
state-of-the-art for visual attribution on a synthetic dataset and on real 3D
neuroimaging data from patients with mild cognitive impairment (MCI) and
Alzheimer's disease (AD). For AD patients the method produces compellingly
realistic disease effect maps which are very close to the observed effects. | [
"cs.CV"
] |
Lack of annotated samples greatly restrains the direct application of deep
learning in remote sensing image scene classification. Although researches have
been done to tackle this issue by data augmentation with various image
transformation operations, they are still limited in quantity and diversity.
Recently, the advent of the unsupervised learning based generative adversarial
networks (GANs) bring us a new way to generate augmented samples. However, such
GAN-generated samples are currently only served for training GANs model itself
and for improving the performance of the discriminator in GANs internally (in
vivo). It becomes a question of serious doubt whether the GAN-generated samples
can help better improve the scene classification performance of other deep
learning networks (in vitro), compared with the widely used transformed
samples. To answer this question, this paper proposes a SiftingGAN approach to
generate more numerous, more diverse and more authentic labeled samples for
data augmentation. SiftingGAN extends traditional GAN framework with an
Online-Output method for sample generation, a Generative-Model-Sifting method
for model sifting, and a Labeled-Sample-Discriminating method for sample
sifting. Experiments on the well-known AID dataset demonstrate that the
proposed SiftingGAN method can not only effectively improve the performance of
the scene classification baseline that is achieved without data augmentation,
but also significantly excels the comparison methods based on traditional
geometric/radiometric transformation operations. | [
"cs.CV"
] |
Self-supervised or weakly supervised models trained on large-scale datasets
have shown sample-efficient transfer to diverse datasets in few-shot settings.
We consider how upstream pretrained models can be leveraged for downstream
few-shot, multilabel, and continual learning tasks. Our model CLIPPER (CLIP
PERsonalized) uses image representations from CLIP, a large-scale image
representation learning model trained using weak natural language supervision.
We developed a technique, called Multi-label Weight Imprinting (MWI), for
multi-label, continual, and few-shot learning, and CLIPPER uses MWI with image
representations from CLIP. We evaluated CLIPPER on 10 single-label and 5
multi-label datasets. Our model shows robust and competitive performance, and
we set new benchmarks for few-shot, multi-label, and continual learning. Our
lightweight technique is also compute-efficient and enables privacy-preserving
applications as the data is not sent to the upstream model for fine-tuning. | [
"cs.CV"
] |
Mutual information maximization has emerged as a powerful learning objective
for unsupervised representation learning obtaining state-of-the-art performance
in applications such as object recognition, speech recognition, and
reinforcement learning. However, such approaches are fundamentally limited
since a tight lower bound of mutual information requires sample size
exponential in the mutual information. This limits the applicability of these
approaches for prediction tasks with high mutual information, such as in video
understanding or reinforcement learning. In these settings, such techniques are
prone to overfit, both in theory and in practice, and capture only a few of the
relevant factors of variation. This leads to incomplete representations that
are not optimal for downstream tasks. In this work, we empirically demonstrate
that mutual information-based representation learning approaches do fail to
learn complete representations on a number of designed and real-world tasks. To
mitigate these problems we introduce the Wasserstein dependency measure, which
learns more complete representations by using the Wasserstein distance instead
of the KL divergence in the mutual information estimator. We show that a
practical approximation to this theoretically motivated solution, constructed
using Lipschitz constraint techniques from the GAN literature, achieves
substantially improved results on tasks where incomplete representations are a
major challenge. | [
"cs.LG",
"stat.ML"
] |
We showcase a topological mapping framework for a challenging indoor
warehouse setting. At the most abstract level, the warehouse is represented as
a Topological Graph where the nodes of the graph represent a particular
warehouse topological construct (e.g. rackspace, corridor) and the edges denote
the existence of a path between two neighbouring nodes or topologies. At the
intermediate level, the map is represented as a Manhattan Graph where the nodes
and edges are characterized by Manhattan properties and as a Pose Graph at the
lower-most level of detail. The topological constructs are learned via a Deep
Convolutional Network while the relational properties between topological
instances are learnt via a Siamese-style Neural Network. In the paper, we show
that maintaining abstractions such as Topological Graph and Manhattan Graph
help in recovering an accurate Pose Graph starting from a highly erroneous and
unoptimized Pose Graph. We show how this is achieved by embedding topological
and Manhattan relations as well as Manhattan Graph aided loop closure relations
as constraints in the backend Pose Graph optimization framework. The recovery
of near ground-truth Pose Graph on real-world indoor warehouse scenes vindicate
the efficacy of the proposed framework. | [
"cs.CV",
"cs.RO"
] |
We present a caricature generation framework based on shape and style
manipulation using StyleGAN. Our framework, dubbed StyleCariGAN, automatically
creates a realistic and detailed caricature from an input photo with optional
controls on shape exaggeration degree and color stylization type. The key
component of our method is shape exaggeration blocks that are used for
modulating coarse layer feature maps of StyleGAN to produce desirable
caricature shape exaggerations. We first build a layer-mixed StyleGAN for
photo-to-caricature style conversion by swapping fine layers of the StyleGAN
for photos to the corresponding layers of the StyleGAN trained to generate
caricatures. Given an input photo, the layer-mixed model produces detailed
color stylization for a caricature but without shape exaggerations. We then
append shape exaggeration blocks to the coarse layers of the layer-mixed model
and train the blocks to create shape exaggerations while preserving the
characteristic appearances of the input. Experimental results show that our
StyleCariGAN generates realistic and detailed caricatures compared to the
current state-of-the-art methods. We demonstrate StyleCariGAN also supports
other StyleGAN-based image manipulations, such as facial expression control. | [
"cs.CV",
"cs.GR",
"I.4.0"
] |
We propose a new approach for synthesizing fully detailed art-stylized images
from sketches. Given a sketch, with no semantic tagging, and a reference image
of a specific style, the model can synthesize meaningful details with colors
and textures. The model consists of three modules designed explicitly for
better artistic style capturing and generation. Based on a GAN framework, a
dual-masked mechanism is introduced to enforce the content constraints (from
the sketch), and a feature-map transformation technique is developed to
strengthen the style consistency (to the reference image). Finally, an inverse
procedure of instance-normalization is proposed to disentangle the style and
content information, therefore yields better synthesis performance. Experiments
demonstrate a significant qualitative and quantitative boost over baselines
based on previous state-of-the-art techniques, adopted for the proposed
process. | [
"cs.CV",
"eess.IV"
] |
Standard registration algorithms need to be independently applied to each
surface to register, following careful pre-processing and hand-tuning.
Recently, learning-based approaches have emerged that reduce the registration
of new scans to running inference with a previously-trained model. In this
paper, we cast the registration task as a surface-to-surface translation
problem, and design a model to reliably capture the latent geometric
information directly from raw 3D face scans. We introduce Shape-My-Face (SMF),
a powerful encoder-decoder architecture based on an improved point cloud
encoder, a novel visual attention mechanism, graph convolutional decoders with
skip connections, and a specialized mouth model that we smoothly integrate with
the mesh convolutions. Compared to the previous state-of-the-art learning
algorithms for non-rigid registration of face scans, SMF only requires the raw
data to be rigidly aligned (with scaling) with a pre-defined face template.
Additionally, our model provides topologically-sound meshes with minimal
supervision, offers faster training time, has orders of magnitude fewer
trainable parameters, is more robust to noise, and can generalize to previously
unseen datasets. We extensively evaluate the quality of our registrations on
diverse data. We demonstrate the robustness and generalizability of our model
with in-the-wild face scans across different modalities, sensor types, and
resolutions. Finally, we show that, by learning to register scans, SMF produces
a hybrid linear and non-linear morphable model. Manipulation of the latent
space of SMF allows for shape generation, and morphing applications such as
expression transfer in-the-wild. We train SMF on a dataset of human faces
comprising 9 large-scale databases on commodity hardware. | [
"cs.CV",
"cs.GR",
"cs.LG"
] |
A fundamental problem in computational chemistry is to find a set of
reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction.
Existing state-of-the-art methods rely on matching the target molecule with a
large set of reaction templates, which are very computationally expensive and
also suffer from the problem of coverage. In this paper, we propose a novel
template-free approach called G2Gs by transforming a target molecular graph
into a set of reactant molecular graphs. G2Gs first splits the target molecular
graph into a set of synthons by identifying the reaction centers, and then
translates the synthons to the final reactant graphs via a variational graph
translation framework. Experimental results show that G2Gs significantly
outperforms existing template-free approaches by up to 63% in terms of the
top-1 accuracy and achieves a performance close to that of state-of-the-art
template based approaches, but does not require domain knowledge and is much
more scalable. | [
"cs.LG",
"stat.ML"
] |
The multiresolution Gaussian process (GP) has gained increasing attention as
a viable approach towards improving the quality of approximations in GPs that
scale well to large-scale data. Most of the current constructions assume full
independence across resolutions. This assumption simplifies the inference, but
it underestimates the uncertainties in transitioning from one resolution to
another. This in turn results in models which are prone to overfitting in the
sense of excessive sensitivity to the chosen resolution, and predictions which
are non-smooth at the boundaries. Our contribution is a new construction which
instead assumes conditional independence among GPs across resolutions. We show
that relaxing the full independence assumption enables robustness against
overfitting, and that it delivers predictions that are smooth at the
boundaries. Our new model is compared against current state of the art on 2
synthetic and 9 real-world datasets. In most cases, our new conditionally
independent construction performed favorably when compared against models based
on the full independence assumption. In particular, it exhibits little to no
signs of overfitting. | [
"stat.ML"
] |
Multi-agent reinforcement learning (MARL) has been increasingly used in a
wide range of safety-critical applications, which require guaranteed safety
(e.g., no unsafe states are ever visited) during the learning
process.Unfortunately, current MARL methods do not have safety guarantees.
Therefore, we present two shielding approaches for safe MARL. In centralized
shielding, we synthesize a single shield to monitor all agents' joint actions
and correct any unsafe action if necessary. In factored shielding, we
synthesize multiple shields based on a factorization of the joint state space
observed by all agents; the set of shields monitors agents concurrently and
each shield is only responsible for a subset of agents at each
step.Experimental results show that both approaches can guarantee the safety of
agents during learning without compromising the quality of learned policies;
moreover, factored shielding is more scalable in the number of agents than
centralized shielding. | [
"cs.LG",
"cs.FL",
"I.2.6; I.2.4"
] |
Deep neural networks (DNN) excel at extracting patterns. Through
representation learning and automated feature engineering on large datasets,
such models have been highly successful in computer vision and natural language
applications. Designing optimal network architectures from a principled or
rational approach however has been less than successful, with the best
successful approaches utilizing an additional machine learning algorithm to
tune the network hyperparameters. However, in many technical fields, there
exist established domain knowledge and understanding about the subject matter.
In this work, we develop a novel furcated neural network architecture that
utilizes domain knowledge as high-level design principles of the network. We
demonstrate proof-of-concept by developing IL-Net, a furcated network for
predicting the properties of ionic liquids, which is a class of complex
multi-chemicals entities. Compared to existing state-of-the-art approaches, we
show that furcated networks can improve model accuracy by approximately 20-35%,
without using additional labeled data. Lastly, we distill two key design
principles for furcated networks that can be adapted to other domains. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Deep convolutional neural networks (CNNs) have been widely applied for
low-level vision over the past five years. According to nature of different
applications, designing appropriate CNN architectures is developed. However,
customized architectures gather different features via treating all pixel
points as equal to improve the performance of given application, which ignores
the effects of local power pixel points and results in low training efficiency.
In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric
block (AB), a memory enhancement block (MEB) and a high-frequency feature
enhancement block (HFFEB) for image super-resolution. The AB utilizes
one-dimensional asymmetric convolutions to intensify the square convolution
kernels in horizontal and vertical directions for promoting the influences of
local salient features for SISR. The MEB fuses all hierarchical low-frequency
features from the AB via residual learning (RL) technique to resolve the
long-term dependency problem and transforms obtained low-frequency features
into high-frequency features. The HFFEB exploits low- and high-frequency
features to obtain more robust super-resolution features and address excessive
feature enhancement problem. Addditionally, it also takes charge of
reconstructing a high-resolution (HR) image. Extensive experiments show that
our ACNet can effectively address single image super-resolution (SISR), blind
SISR and blind SISR of blind noise problems. The code of the ACNet is shown at
https://github.com/hellloxiaotian/ACNet. | [
"cs.CV"
] |
Recently, the task of image generation has attracted much attention. In
particular, the recent empirical successes of the Markov Chain Monte Carlo
(MCMC) technique of Langevin Dynamics have prompted a number of theoretical
advances; despite this, several outstanding problems remain. First, the
Langevin Dynamics is run in very high dimension on a nonconvex landscape; in
the worst case, due to the NP-hardness of nonconvex optimization, it is thought
that Langevin Dynamics mixes only in time exponential in the dimension. In this
work, we demonstrate how the manifold hypothesis allows for the considerable
reduction of mixing time, from exponential in the ambient dimension to
depending only on the (much smaller) intrinsic dimension of the data. Second,
the high dimension of the sampling space significantly hurts the performance of
Langevin Dynamics; we leverage a multi-scale approach to help ameliorate this
issue and observe that this multi-resolution algorithm allows for a trade-off
between image quality and computational expense in generation. | [
"stat.ML",
"cs.LG"
] |
Event cameras, inspired by biological vision systems, provide a natural and
data efficient representation of visual information. Visual information is
acquired in the form of events that are triggered by local brightness changes.
Each pixel location of the camera's sensor records events asynchronously and
independently with very high temporal resolution. However, because most
brightness changes are triggered by relative motion of the camera and the
scene, the events recorded at a single sensor location seldom correspond to the
same world point. To extract meaningful information from event cameras, it is
helpful to register events that were triggered by the same underlying world
point. In this work we propose a new model of event data that captures its
natural spatio-temporal structure. We start by developing a model for aligned
event data. That is, we develop a model for the data as though it has been
perfectly registered already. In particular, we model the aligned data as a
spatio-temporal Poisson point process. Based on this model, we develop a
maximum likelihood approach to registering events that are not yet aligned.
That is, we find transformations of the observed events that make them as
likely as possible under our model. In particular we extract the camera
rotation that leads to the best event alignment. We show new state of the art
accuracy for rotational velocity estimation on the DAVIS 240C dataset. In
addition, our method is also faster and has lower computational complexity than
several competing methods. | [
"cs.CV"
] |
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"
] |
Zeroth-order (ZO, also known as derivative-free) methods, which estimate the
gradient only by two function evaluations, have attracted much attention
recently because of its broad applications in machine learning community. The
two function evaluations are normally generated with random perturbations from
standard Gaussian distribution. To speed up ZO methods, many methods, such as
variance reduced stochastic ZO gradients and learning an adaptive Gaussian
distribution, have recently been proposed to reduce the variances of ZO
gradients. However, it is still an open problem whether there is a space to
further improve the convergence of ZO methods. To explore this problem, in this
paper, we propose a new reinforcement learning based ZO algorithm (ZO-RL) with
learning the sampling policy for generating the perturbations in ZO
optimization instead of using random sampling. To find the optimal policy, an
actor-critic RL algorithm called deep deterministic policy gradient (DDPG) with
two neural network function approximators is adopted. The learned sampling
policy guides the perturbed points in the parameter space to estimate a more
accurate ZO gradient. To the best of our knowledge, our ZO-RL is the first
algorithm to learn the sampling policy using reinforcement learning for ZO
optimization which is parallel to the existing methods. Especially, our ZO-RL
can be combined with existing ZO algorithms that could further accelerate the
algorithms. Experimental results for different ZO optimization problems show
that our ZO-RL algorithm can effectively reduce the variances of ZO gradient by
learning a sampling policy, and converge faster than existing ZO algorithms in
different scenarios. | [
"cs.LG"
] |
To assist researchers to identify Environmental Microorganisms (EMs)
effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image
segmentation is proposed in this paper. There are two parts in this framework:
The first is a novel pixel-level segmentation approach, using a newly
introduced Convolutional Neural Network (CNN), namely, "mU-Net-B3", with a
dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16
based patch-level segmentation method with a novel "buffer" strategy, which
further improves the segmentation quality of the details of the EMs. In the
experiment, compared with the state-of-the-art methods on 420 EM images, the
proposed MSCC method reduces the memory requirement from 355 MB to 103 MB,
improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from
85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%,
respectively, and reduces the volume overlap error from 22.58% to 20.26%.
Therefore, the MSCC method shows great potential in the EM segmentation field. | [
"cs.CV"
] |
Learning with limited data is a key challenge for visual recognition. Many
few-shot learning methods address this challenge by learning an instance
embedding function from seen classes and apply the function to instances from
unseen classes with limited labels. This style of transfer learning is
task-agnostic: the embedding function is not learned optimally discriminative
with respect to the unseen classes, where discerning among them leads to the
target task. In this paper, we propose a novel approach to adapt the instance
embeddings to the target classification task with a set-to-set function,
yielding embeddings that are task-specific and are discriminative. We
empirically investigated various instantiations of such set-to-set functions
and observed the Transformer is most effective -- as it naturally satisfies key
properties of our desired model. We denote this model as FEAT (few-shot
embedding adaptation w/ Transformer) and validate it on both the standard
few-shot classification benchmark and four extended few-shot learning settings
with essential use cases, i.e., cross-domain, transductive, generalized
few-shot learning, and low-shot learning. It archived consistent improvements
over baseline models as well as previous methods and established the new
state-of-the-art results on two benchmarks. | [
"cs.LG",
"cs.CV"
] |
In recent years, researchers have achieved great success in applying Deep
Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games,
creating strong autonomous agents that could defeat professional players in
StarCraft~II. However, existing approaches to tackle full games have high
computational costs, usually requiring the use of thousands of GPUs and CPUs
for weeks. This paper has two main contributions to address this issue: 1) We
introduce Gym-$\mu$RTS (pronounced "gym-micro-RTS") as a fast-to-run RL
environment for full-game RTS research and 2) we present a collection of
techniques to scale DRL to play full-game $\mu$RTS as well as ablation studies
to demonstrate their empirical importance. Our best-trained bot can defeat
every $\mu$RTS bot we tested from the past $\mu$RTS competitions when working
in a single-map setting, resulting in a state-of-the-art DRL agent while only
taking about 60 hours of training using a single machine (one GPU, three vCPU,
16GB RAM). See the blog post at
https://wandb.ai/vwxyzjn/gym-microrts-paper/reports/Gym-RTS-Toward-Affordable-Deep-Reinforcement-Learning-Research-in-Real-Time-Strategy-Games--Vmlldzo2MDIzMTg
and the source code at https://github.com/vwxyzjn/gym-microrts-paper | [
"cs.LG"
] |
Learning-based approaches for semantic segmentation have two inherent
challenges. First, acquiring pixel-wise labels is expensive and time-consuming.
Second, realistic segmentation datasets are highly unbalanced: some categories
are much more abundant than others, biasing the performance to the most
represented ones. In this paper, we are interested in focusing human labelling
effort on a small subset of a larger pool of data, minimizing this effort while
maximizing performance of a segmentation model on a hold-out set. We present a
new active learning strategy for semantic segmentation based on deep
reinforcement learning (RL). An agent learns a policy to select a subset of
small informative image regions -- opposed to entire images -- to be labeled,
from a pool of unlabeled data. The region selection decision is made based on
predictions and uncertainties of the segmentation model being trained. Our
method proposes a new modification of the deep Q-network (DQN) formulation for
active learning, adapting it to the large-scale nature of semantic segmentation
problems. We test the proof of concept in CamVid and provide results in the
large-scale dataset Cityscapes. On Cityscapes, our deep RL region-based DQN
approach requires roughly 30% less additional labeled data than our most
competitive baseline to reach the same performance. Moreover, we find that our
method asks for more labels of under-represented categories compared to the
baselines, improving their performance and helping to mitigate class imbalance. | [
"cs.CV"
] |
Network embedding methodologies, which learn a distributed vector
representation for each vertex in a network, have attracted considerable
interest in recent years. Existing works have demonstrated that vertex
representation learned through an embedding method provides superior
performance in many real-world applications, such as node classification, link
prediction, and community detection. However, most of the existing methods for
network embedding only utilize topological information of a vertex, ignoring a
rich set of nodal attributes (such as, user profiles of an online social
network, or textual contents of a citation network), which is abundant in all
real-life networks. A joint network embedding that takes into account both
attributional and relational information entails a complete network information
and could further enrich the learned vector representations. In this work, we
present Neural-Brane, a novel Neural Bayesian Personalized Ranking based
Attributed Network Embedding. For a given network, Neural-Brane extracts latent
feature representation of its vertices using a designed neural network model
that unifies network topological information and nodal attributes; Besides, it
utilizes Bayesian personalized ranking objective, which exploits the proximity
ordering between a similar node-pair and a dissimilar node-pair. We evaluate
the quality of vertex embedding produced by Neural-Brane by solving the node
classification and clustering tasks on four real-world datasets. Experimental
results demonstrate the superiority of our proposed method over the
state-of-the-art existing methods. | [
"cs.LG",
"cs.SI",
"stat.ML"
] |
This paper investigates energy efficiency for two-tier femtocell networks
through combining game theory and stochastic learning. With the Stackelberg
game formulation, a hierarchical reinforcement learning framework is applied to
study the joint average utility maximization of macrocells and femtocells
subject to the minimum signal-to-interference-plus-noise-ratio requirements.
The macrocells behave as the leaders and the femtocells are followers during
the learning procedure. At each time step, the leaders commit to dynamic
strategies based on the best responses of the followers, while the followers
compete against each other with no further information but the leaders'
strategy information. In this paper, we propose two learning algorithms to
schedule each cell's stochastic power levels, leading by the macrocells.
Numerical experiments are presented to validate the proposed studies and show
that the two learning algorithms substantially improve the energy efficiency of
the femtocell networks. | [
"cs.LG"
] |
Video object segmentation aims at accurately segmenting the target object
regions across consecutive frames. It is technically challenging for coping
with complicated factors (e.g., shape deformations, occlusion and out of the
lens). Recent approaches have largely solved them by using backforth
re-identification and bi-directional mask propagation. However, their methods
are extremely slow and only support offline inference, which in principle
cannot be applied in real time. Motivated by this observation, we propose a
efficient detection-based paradigm for video object segmentation. We propose an
unified One-Pass Video Segmentation framework (OVS-Net) for modeling
spatial-temporal representation in a unified pipeline, which seamlessly
integrates object detection, object segmentation, and object re-identification.
The proposed framework lends itself to one-pass inference that effectively and
efficiently performs video object segmentation. Moreover, we propose a
maskguided attention module for modeling the multi-scale object boundary and
multi-level feature fusion. Experiments on the challenging DAVIS 2017
demonstrate the effectiveness of the proposed framework with comparable
performance to the state-of-the-art, and the great efficiency about 11.5 FPS
towards pioneering real-time work to our knowledge, more than 5 times faster
than other state-of-the-art methods. | [
"cs.CV"
] |
Image style transfer aims to manipulate the appearance of a source image, or
"content" image, to share similar texture and colors of a target "style" image.
Ideally, the style transfer manipulation should also preserve the semantic
content of the source image. A commonly used approach to assist in transferring
styles is based on Gram matrix optimization. One problem of Gram matrix-based
optimization is that it does not consider the correlation between colors and
their styles. Specifically, certain textures or structures should be associated
with specific colors. This is particularly challenging when the target style
image exhibits multiple style types. In this work, we propose a color-aware
multi-style transfer method that generates aesthetically pleasing results while
preserving the style-color correlation between style and generated images. We
achieve this desired outcome by introducing a simple but efficient modification
to classic Gram matrix-based style transfer optimization. A nice feature of our
method is that it enables the users to manually select the color associations
between the target style and content image for more transfer flexibility. We
validated our method with several qualitative comparisons, including a user
study conducted with 30 participants. In comparison with prior work, our method
is simple, easy to implement, and achieves visually appealing results when
targeting images that have multiple styles. Source code is available at
https://github.com/mahmoudnafifi/color-aware-style-transfer. | [
"cs.CV"
] |
Tensor Networks (TN) offer a powerful framework to efficiently represent very
high-dimensional objects. TN have recently shown their potential for machine
learning applications and offer a unifying view of common tensor decomposition
models such as Tucker, tensor train (TT) and tensor ring (TR). However,
identifying the best tensor network structure from data for a given task is
challenging. In this work, we leverage the TN formalism to develop a generic
and efficient adaptive algorithm to jointly learn the structure and the
parameters of a TN from data. Our method is based on a simple greedy approach
starting from a rank one tensor and successively identifying the most promising
tensor network edges for small rank increments. Our algorithm can adaptively
identify TN structures with small number of parameters that effectively
optimize any differentiable objective function. Experiments on tensor
decomposition, tensor completion and model compression tasks demonstrate the
effectiveness of the proposed algorithm. In particular, our method outperforms
the state-of-the-art evolutionary topology search [Li and Sun, 2020] for tensor
decomposition of images (while being orders of magnitude faster) and finds
efficient tensor network structures to compress neural networks outperforming
popular TT based approaches [Novikov et al., 2015]. | [
"cs.LG",
"stat.ML"
] |
Graph Attention Networks (GATs) are one of the most popular GNN architectures
and are considered as the state-of-the-art architecture for representation
learning with graphs. In GAT, every node attends to its neighbors given its own
representation as the query. However, in this paper we show that GATs can only
compute a restricted kind of attention where the ranking of attended nodes is
unconditioned on the query node. We formally define this restricted kind of
attention as static attention and distinguish it from a strictly more
expressive dynamic attention. Because GATs use a static attention mechanism,
there are simple graph problems that GAT cannot express: in a controlled
problem, we show that static attention hinders GAT from even fitting the
training data. To remove this limitation, we introduce a simple fix by
modifying the order of operations and propose GATv2: a dynamic graph attention
variant that is strictly more expressive than GAT. We perform an extensive
evaluation and show that GATv2 outperforms GAT across 11 OGB and other
benchmarks while we match their parametric costs. Our code is available at
https://github.com/tech-srl/how_attentive_are_gats . | [
"cs.LG"
] |
We compare a recent dehazing method based on deep learning, Dehazenet, with
traditional state-of-the-art approaches , on benchmark data with reference.
Dehazenet estimates the depth map from transmission factor on a single color
image, which is used to inverse the Koschmieder model of imaging in the
presence of haze. In this sense, the solution is still attached to the
Koschmieder model. We demonstrate that the transmission is very well estimated
by the network, but also that this method exhibits the same limitation than
others due to the use of the same imaging model. | [
"cs.CV",
"cs.MM",
"eess.IV"
] |
Few-shot object detection, which aims at detecting novel objects rapidly from
extremely few annotated examples of previously unseen classes, has attracted
significant research interest in the community. Most existing approaches employ
the Faster R-CNN as basic detection framework, yet, due to the lack of tailored
considerations for data-scarce scenario, their performance is often not
satisfactory. In this paper, we look closely into the conventional Faster R-CNN
and analyze its contradictions from two orthogonal perspectives, namely
multi-stage (RPN vs. RCNN) and multi-task (classification vs. localization). To
resolve these issues, we propose a simple yet effective architecture, named
Decoupled Faster R-CNN (DeFRCN). To be concrete, we extend Faster R-CNN by
introducing Gradient Decoupled Layer for multi-stage decoupling and
Prototypical Calibration Block for multi-task decoupling. The former is a novel
deep layer with redefining the feature-forward operation and gradient-backward
operation for decoupling its subsequent layer and preceding layer, and the
latter is an offline prototype-based classification model with taking the
proposals from detector as input and boosting the original classification
scores with additional pairwise scores for calibration. Extensive experiments
on multiple benchmarks show our framework is remarkably superior to other
existing approaches and establishes a new state-of-the-art in few-shot
literature. | [
"cs.CV"
] |
Classification is an important supervised machine learning method, which is
necessary and challenging issue for ecological research. It offers a way to
classify a dataset into subsets that share common patterns. Notably, there are
many classification algorithms to choose from, each making certain assumptions
about the data and about how classification should be formed. In this paper, we
applied eight machine learning classification algorithms such as Decision
Trees, Random Forest, Artificial Neural Network, Support Vector Machine, Linear
Discriminant Analysis, k-nearest neighbors, Logistic Regression and Naive Bayes
on ecological data. The goal of this study is to compare different machine
learning classification algorithms in ecological dataset. In this analysis we
have checked the accuracy test among the algorithms. In our study we conclude
that Linear Discriminant Analysis and k-nearest neighbors are the best methods
among all other methods | [
"stat.ML",
"cs.LG"
] |
Variational Convertor-Encoder (VCE) converts an image to various styles; we
present this novel architecture for the problem of one-shot generalization and
its transfer to new tasks not seen before without additional training. We also
improve the performance of variational auto-encoder (VAE) to filter those
blurred points using a novel algorithm proposed by us, namely large margin VAE
(LMVAE). Two samples with the same property are input to the encoder, and then
a convertor is required to processes one of them from the noisy outputs of the
encoder; finally, the noise represents a variety of transformation rules and is
used to convert new images. The algorithm that combines and improves the
condition variational auto-encoder (CVAE) and introspective VAE, we propose
this new framework aim to transform graphics instead of generating them; it is
used for the one-shot generative process. No sequential inference algorithmic
is needed in training. Compared to recent Omniglot datasets, the results show
that our model produces more realistic and diverse images. | [
"cs.CV"
] |
Vehicle re-identification is an important problem and becomes desirable with
the rapid expansion of applications in video surveillance and intelligent
transportation. By recalling the identification process of human vision, we are
aware that there exists a native hierarchical dependency when humans identify
different vehicles. Specifically, humans always firstly determine one vehicle's
coarse-grained category, i.e., the car model/type. Then, under the branch of
the predicted car model/type, they are going to identify specific vehicles by
relying on subtle visual cues, e.g., customized paintings and windshield
stickers, at the fine-grained level. Inspired by the coarse-to-fine
hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention
(RNN-HA) classification model for vehicle re-identification. RNN-HA consists of
three mutually coupled modules: the first module generates image
representations for vehicle images, the second hierarchical module models the
aforementioned hierarchical dependent relationship, and the last attention
module focuses on capturing the subtle visual information distinguishing
specific vehicles from each other. By conducting comprehensive experiments on
two vehicle re-identification benchmark datasets VeRi and VehicleID, we
demonstrate that the proposed model achieves superior performance over
state-of-the-art methods. | [
"cs.CV"
] |
Co-occurrent visual pattern makes aggregating contextual information a common
paradigm to enhance the pixel representation for semantic image segmentation.
The existing approaches focus on modeling the context from the perspective of
the whole image, i.e., aggregating the image-level contextual information.
Despite impressive, these methods weaken the significance of the pixel
representations of the same category, i.e., the semantic-level contextual
information. To address this, this paper proposes to augment the pixel
representations by aggregating the image-level and semantic-level contextual
information, respectively. First, an image-level context module is designed to
capture the contextual information for each pixel in the whole image. Second,
we aggregate the representations of the same category for each pixel where the
category regions are learned under the supervision of the ground-truth
segmentation. Third, we compute the similarities between each pixel
representation and the image-level contextual information, the semantic-level
contextual information, respectively. At last, a pixel representation is
augmented by weighted aggregating both the image-level contextual information
and the semantic-level contextual information with the similarities as the
weights. Integrating the image-level and semantic-level context allows this
paper to report state-of-the-art accuracy on four benchmarks, i.e., ADE20K,
LIP, COCOStuff and Cityscapes. | [
"cs.CV"
] |
Forecasting the long-term future motion of road actors is a core challenge to
the deployment of safe autonomous vehicles (AVs). Viable solutions must account
for both the static geometric context, such as road lanes, and dynamic social
interactions arising from multiple actors. While recent deep architectures have
achieved state-of-the-art performance on distance-based forecasting metrics,
these approaches produce forecasts that are predicted without regard to the
AV's intended motion plan. In contrast, we propose a recurrent graph-based
attentional approach with interpretable geometric (actor-lane) and social
(actor-actor) relationships that supports the injection of counterfactual
geometric goals and social contexts. Our model can produce diverse predictions
conditioned on hypothetical or "what-if" road lanes and multi-actor
interactions. We show that such an approach could be used in the planning loop
to reason about unobserved causes or unlikely futures that are directly
relevant to the AV's intended route. | [
"cs.LG",
"stat.ML"
] |
We propose a method for large displacement optical flow in which local
matching costs are learned by a convolutional neural network (CNN) and a
smoothness prior is imposed by a conditional random field (CRF). We tackle the
computation- and memory-intensive operations on the 4D cost volume by a
min-projection which reduces memory complexity from quadratic to linear and
binary descriptors for efficient matching. This enables evaluation of the cost
on the fly and allows to perform learning and CRF inference on high resolution
images without ever storing the 4D cost volume. To address the problem of
learning binary descriptors we propose a new hybrid learning scheme. In
contrast to current state of the art approaches for learning binary CNNs we can
compute the exact non-zero gradient within our model. We compare several
methods for training binary descriptors and show results on public available
benchmarks. | [
"cs.CV"
] |
With various face presentation attacks arising under unseen scenarios, face
anti-spoofing (FAS) based on domain generalization (DG) has drawn growing
attention due to its robustness. Most existing methods utilize DG frameworks to
align the features to seek a compact and generalized feature space. However,
little attention has been paid to the feature extraction process for the FAS
task, especially the influence of normalization, which also has a great impact
on the generalization of the learned representation. To address this issue, we
propose a novel perspective of face anti-spoofing that focuses on the
normalization selection in the feature extraction process. Concretely, an
Adaptive Normalized Representation Learning (ANRL) framework is devised, which
adaptively selects feature normalization methods according to the inputs,
aiming to learn domain-agnostic and discriminative representation. Moreover, to
facilitate the representation learning, Dual Calibration Constraints are
designed, including Inter-Domain Compatible loss and Inter-Class Separable
loss, which provide a better optimization direction for generalizable
representation. Extensive experiments and visualizations are presented to
demonstrate the effectiveness of our method against the SOTA competitors. | [
"cs.CV"
] |
Recently, several methods have been proposed to explain the predictions of
recurrent neural networks (RNNs), in particular of LSTMs. The goal of these
methods is to understand the network's decisions by assigning to each input
variable, e.g., a word, a relevance indicating to which extent it contributed
to a particular prediction. In previous works, some of these methods were not
yet compared to one another, or were evaluated only qualitatively. We close
this gap by systematically and quantitatively comparing these methods in
different settings, namely (1) a toy arithmetic task which we use as a sanity
check, (2) a five-class sentiment prediction of movie reviews, and besides (3)
we explore the usefulness of word relevances to build sentence-level
representations. Lastly, using the method that performed best in our
experiments, we show how specific linguistic phenomena such as the negation in
sentiment analysis reflect in terms of relevance patterns, and how the
relevance visualization can help to understand the misclassification of
individual samples. | [
"cs.LG",
"cs.NE",
"stat.ML"
] |
Training of Generative Adversarial Networks (GANs) is notoriously fragile,
requiring to maintain a careful balance between the generator and the
discriminator in order to perform well. To mitigate this issue we introduce a
new regularization technique - progressive augmentation of GANs (PA-GAN). The
key idea is to gradually increase the task difficulty of the discriminator by
progressively augmenting its input or feature space, thus enabling continuous
learning of the generator. We show that the proposed progressive augmentation
preserves the original GAN objective, does not compromise the discriminator's
optimality and encourages a healthy competition between the generator and
discriminator, leading to the better-performing generator. We experimentally
demonstrate the effectiveness of PA-GAN across different architectures and on
multiple benchmarks for the image synthesis task, on average achieving ~3 point
improvement of the FID score. | [
"cs.CV"
] |
We study the computational tractability of PAC reinforcement learning with
rich observations. We present new provably sample-efficient algorithms for
environments with deterministic hidden state dynamics and stochastic rich
observations. These methods operate in an oracle model of computation --
accessing policy and value function classes exclusively through standard
optimization primitives -- and therefore represent computationally efficient
alternatives to prior algorithms that require enumeration. With stochastic
hidden state dynamics, we prove that the only known sample-efficient algorithm,
OLIVE, cannot be implemented in the oracle model. We also present several
examples that illustrate fundamental challenges of tractable PAC reinforcement
learning in such general settings. | [
"cs.LG",
"stat.ML"
] |
Vision Transformer (ViT) extends the application range of transformers from
language processing to computer vision tasks as being an alternative
architecture against the existing convolutional neural networks (CNN). Since
the transformer-based architecture has been innovative for computer vision
modeling, the design convention towards an effective architecture has been less
studied yet. From the successful design principles of CNN, we investigate the
role of spatial dimension conversion and its effectiveness on transformer-based
architecture. We particularly attend to the dimension reduction principle of
CNNs; as the depth increases, a conventional CNN increases channel dimension
and decreases spatial dimensions. We empirically show that such a spatial
dimension reduction is beneficial to a transformer architecture as well, and
propose a novel Pooling-based Vision Transformer (PiT) upon the original ViT
model. We show that PiT achieves the improved model capability and
generalization performance against ViT. Throughout the extensive experiments,
we further show PiT outperforms the baseline on several tasks such as image
classification, object detection, and robustness evaluation. Source codes and
ImageNet models are available at https://github.com/naver-ai/pit | [
"cs.CV"
] |
The increasing size of neural network models has been critical for
improvements in their accuracy, but device memory is not growing at the same
rate. This creates fundamental challenges for training neural networks within
limited memory environments. In this work, we propose ActNN, a memory-efficient
training framework that stores randomly quantized activations for back
propagation. We prove the convergence of ActNN for general network
architectures, and we characterize the impact of quantization on the
convergence via an exact expression for the gradient variance. Using our
theory, we propose novel mixed-precision quantization strategies that exploit
the activation's heterogeneity across feature dimensions, samples, and layers.
These techniques can be readily applied to existing dynamic graph frameworks,
such as PyTorch, simply by substituting the layers. We evaluate ActNN on
mainstream computer vision models for classification, detection, and
segmentation tasks. On all these tasks, ActNN compresses the activation to 2
bits on average, with negligible accuracy loss. ActNN reduces the memory
footprint of the activation by 12x, and it enables training with a 6.6x to 14x
larger batch size. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Visual object detection has achieved unprecedented ad-vance with the rise of
deep convolutional neural networks.However, detecting tiny objects (for example
tiny per-sons less than 20 pixels) in large-scale images remainsnot well
investigated. The extremely small objects raisea grand challenge about feature
representation while themassive and complex backgrounds aggregate the risk
offalse alarms. In this paper, we introduce a new benchmark,referred to as
TinyPerson, opening up a promising directionfor tiny object detection in a long
distance and with mas-sive backgrounds. We experimentally find that the scale
mis-match between the dataset for network pre-training and thedataset for
detector learning could deteriorate the featurerepresentation and the
detectors. Accordingly, we proposea simple yet effective Scale Match approach
to align theobject scales between the two datasets for favorable tiny-object
representation. Experiments show the significantperformance gain of our
proposed approach over state-of-the-art detectors, and the challenging aspects
of TinyPersonrelated to real-world scenarios. The TinyPerson benchmarkand the
code for our approach will be publicly
available(https://github.com/ucas-vg/TinyBenchmark).(Attention: evaluation
rules of AP have updated in benchmark after this paper accepted, So this paper
use old rules. we will keep old rules of AP in benchmark, but we recommand the
new and we will use the new in latter research.) | [
"cs.CV"
] |
The abundance of data collected by sensors in Internet of Things (IoT)
devices, and the success of deep neural networks in uncovering hidden patterns
in time series data have led to mounting privacy concerns. This is because
private and sensitive information can be potentially learned from sensor data
by applications that have access to this data. In this paper, we aim to examine
the tradeoff between utility and privacy loss by learning low-dimensional
representations that are useful for data obfuscation. We propose deterministic
and probabilistic transformations in the latent space of a variational
autoencoder to synthesize time series data such that intrusive inferences are
prevented while desired inferences can still be made with sufficient accuracy.
In the deterministic case, we use a linear transformation to move the
representation of input data in the latent space such that the reconstructed
data is likely to have the same public attribute but a different private
attribute than the original input data. In the probabilistic case, we apply the
linear transformation to the latent representation of input data with some
probability. We compare our technique with autoencoder-based anonymization
techniques and additionally show that it can anonymize data in real time on
resource-constrained edge devices. | [
"cs.LG",
"cs.AI",
"cs.CR"
] |
The majority of online display ads are served through real-time bidding (RTB)
--- each ad display impression is auctioned off in real-time when it is just
being generated from a user visit. To place an ad automatically and optimally,
it is critical for advertisers to devise a learning algorithm to cleverly bid
an ad impression in real-time. Most previous works consider the bid decision as
a static optimization problem of either treating the value of each impression
independently or setting a bid price to each segment of ad volume. However, the
bidding for a given ad campaign would repeatedly happen during its life span
before the budget runs out. As such, each bid is strategically correlated by
the constrained budget and the overall effectiveness of the campaign (e.g., the
rewards from generated clicks), which is only observed after the campaign has
completed. Thus, it is of great interest to devise an optimal bidding strategy
sequentially so that the campaign budget can be dynamically allocated across
all the available impressions on the basis of both the immediate and future
rewards. In this paper, we formulate the bid decision process as a
reinforcement learning problem, where the state space is represented by the
auction information and the campaign's real-time parameters, while an action is
the bid price to set. By modeling the state transition via auction competition,
we build a Markov Decision Process framework for learning the optimal bidding
policy to optimize the advertising performance in the dynamic real-time bidding
environment. Furthermore, the scalability problem from the large real-world
auction volume and campaign budget is well handled by state value approximation
using neural networks. | [
"cs.LG",
"cs.AI",
"cs.GT"
] |
Structural learning, a method to estimate the parameters for discrete energy
minimization, has been proven to be effective in solving computer vision
problems, especially in 3D scene parsing. As the complexity of the models
increases, structural learning algorithms turn to approximate inference to
retain tractability. Unfortunately, such methods often fail because the
approximation can be arbitrarily poor. In this work, we propose a method to
overcome this limitation through exploiting the properties of the joint problem
of training time inference and learning. With the help of the learning
framework, we transform the inapproximable inference problem into a polynomial
time solvable one, thereby enabling tractable exact inference while still
allowing an arbitrary graph structure and full potential interactions. Our
learning algorithm is guaranteed to return a solution with a bounded error to
the global optimal within the feasible parameter space. We demonstrate the
effectiveness of this method on two point cloud scene parsing datasets. Our
approach runs much faster and solves a problem that is intractable for
previous, well-known approaches. | [
"cs.CV"
] |
Recent implicit neural rendering methods have demonstrated that it is
possible to learn accurate view synthesis for complex scenes by predicting
their volumetric density and color supervised solely by a set of RGB images.
However, existing methods are restricted to learning efficient representations
of static scenes that encode all scene objects into a single neural network,
and lack the ability to represent dynamic scenes and decompositions into
individual scene objects. In this work, we present the first neural rendering
method that decomposes dynamic scenes into scene graphs. We propose a learned
scene graph representation, which encodes object transformation and radiance,
to efficiently render novel arrangements and views of the scene. To this end,
we learn implicitly encoded scenes, combined with a jointly learned latent
representation to describe objects with a single implicit function. We assess
the proposed method on synthetic and real automotive data, validating that our
approach learns dynamic scenes -- only by observing a video of this scene --
and allows for rendering novel photo-realistic views of novel scene
compositions with unseen sets of objects at unseen poses. | [
"cs.CV",
"cs.GR"
] |
The design of methods for inference from time sequences has traditionally
relied on statistical models that describe the relation between a latent
desired sequence and the observed one. A broad family of model-based algorithms
have been derived to carry out inference at controllable complexity using
recursive computations over the factor graph representing the underlying
distribution. An alternative model-agnostic approach utilizes machine learning
(ML) methods. Here we propose a framework that combines model-based algorithms
and data-driven ML tools for stationary time sequences. In the proposed
approach, neural networks are developed to separately learn specific components
of a factor graph describing the distribution of the time sequence, rather than
the complete inference task. By exploiting stationary properties of this
distribution, the resulting approach can be applied to sequences of varying
temporal duration. Learned factor graph can be realized using compact neural
networks that are trainable using small training sets, or alternatively, be
used to improve upon existing deep inference systems. We present an inference
algorithm based on learned stationary factor graphs, which learns to implement
the sum-product scheme from labeled data, and can be applied to sequences of
different lengths. Our experimental results demonstrate the ability of the
proposed learned factor graphs to learn to carry out accurate inference from
small training sets for sleep stage detection using the Sleep-EDF dataset, as
well as for symbol detection in digital communications with unknown channels. | [
"cs.LG",
"cs.IT",
"math.IT",
"stat.ML"
] |
Recent advances in deep learning have led to significant progress in the
computer vision field, especially for visual object recognition tasks. The
features useful for object classification are learned by feed-forward deep
convolutional neural networks (CNNs) automatically, and they are shown to be
able to predict and decode neural representations in the ventral visual pathway
of humans and monkeys. However, despite the huge amount of work on optimizing
CNNs, there has not been much research focused on linking CNNs with guiding
principles from the human visual cortex. In this work, we propose a network
optimization strategy inspired by both of the developmental trajectory of
children's visual object recognition capabilities, and Bar (2003), who
hypothesized that basic level information is carried in the fast magnocellular
pathway through the prefrontal cortex (PFC) and then projected back to inferior
temporal cortex (IT), where subordinate level categorization is achieved. We
instantiate this idea by training a deep CNN to perform basic level object
categorization first, and then train it on subordinate level categorization. We
apply this idea to training AlexNet (Krizhevsky et al., 2012) on the ILSVRC
2012 dataset and show that the top-5 accuracy increases from 80.13% to 82.14%,
demonstrating the effectiveness of the method. We also show that subsequent
transfer learning on smaller datasets gives superior results. | [
"cs.CV"
] |
Crowd flow describes the elementary group behavior of crowds. Understanding
the dynamics behind these movements can help to identify various abnormalities
in crowds. However, developing a crowd model describing these flows is a
challenging task. In this paper, a physics-based model is proposed to describe
the movements in dense crowds. The crowd model is based on active Langevin
equation where the motion points are assumed to be similar to active colloidal
particles in fluids. The model is further augmented with computer-vision
techniques to segment both linear and non-linear motion flows in a dense crowd.
The evaluation of the active Langevin equation-based crowd segmentation has
been done on publicly available crowd videos and on our own videos. The
proposed method is able to segment the flow with lesser optical flow error and
better accuracy in comparison to existing state-of-the-art methods. | [
"cs.CV"
] |
Many types of 3D acquisition sensors have emerged in recent years and point
cloud has been widely used in many areas. Accurate and fast registration of
cross-source 3D point clouds from different sensors is an emerged research
problem in computer vision. This problem is extremely challenging because
cross-source point clouds contain a mixture of various variances, such as
density, partial overlap, large noise and outliers, viewpoint changing. In this
paper, an algorithm is proposed to align cross-source point clouds with both
high accuracy and high efficiency. There are two main contributions: firstly,
two components, the weak region affinity and pixel-wise refinement, are
proposed to maintain the global and local information of 3D point clouds. Then,
these two components are integrated into an iterative tensor-based registration
algorithm to solve the cross-source point cloud registration problem. We
conduct experiments on synthetic cross-source benchmark dataset and real
cross-source datasets. Comparison with six state-of-the-art methods, the
proposed method obtains both higher efficiency and accuracy. | [
"cs.CV",
"cs.GR"
] |
We introduce the task of directly modeling a visually intelligent agent.
Computer vision typically focuses on solving various subtasks related to visual
intelligence. We depart from this standard approach to computer vision; instead
we directly model a visually intelligent agent. Our model takes visual
information as input and directly predicts the actions of the agent. Toward
this end we introduce DECADE, a large-scale dataset of ego-centric videos from
a dog's perspective as well as her corresponding movements. Using this data we
model how the dog acts and how the dog plans her movements. We show under a
variety of metrics that given just visual input we can successfully model this
intelligent agent in many situations. Moreover, the representation learned by
our model encodes distinct information compared to representations trained on
image classification, and our learned representation can generalize to other
domains. In particular, we show strong results on the task of walkable surface
estimation by using this dog modeling task as representation learning. | [
"cs.CV"
] |
We formalize and study ``programming by rewards'' (PBR), a new approach for
specifying and synthesizing subroutines for optimizing some quantitative metric
such as performance, resource utilization, or correctness over a benchmark. A
PBR specification consists of (1) input features $x$, and (2) a reward function
$r$, modeled as a black-box component (which we can only run), that assigns a
reward for each execution. The goal of the synthesizer is to synthesize a
"decision function" $f$ which transforms the features to a decision value for
the black-box component so as to maximize the expected reward $E[r \circ f
(x)]$ for executing decisions $f(x)$ for various values of $x$. We consider a
space of decision functions in a DSL of loop-free if-then-else programs, which
can branch on linear functions of the input features in a tree-structure and
compute a linear function of the inputs in the leaves of the tree. We find that
this DSL captures decision functions that are manually written in practice by
programmers. Our technical contribution is the use of continuous-optimization
techniques to perform synthesis of such decision functions as if-then-else
programs. We also show that the framework is theoretically-founded ---in cases
when the rewards satisfy nice properties, the synthesized code is optimal in a
precise sense.
We have leveraged PBR to synthesize non-trivial decision functions related to
search and ranking heuristics in the PROSE codebase (an industrial strength
program synthesis framework) and achieve competitive results to manually
written procedures over multiple man years of tuning. We present empirical
evaluation against other baseline techniques over real-world case studies
(including PROSE) as well on simple synthetic benchmarks. | [
"cs.LG",
"cs.AI",
"cs.PL",
"cs.SE",
"stat.ML"
] |
Nighttime satellite imagery has been applied in a wide range of fields.
However, our limited understanding of how observed light intensity is formed
and whether it can be simulated greatly hinders its further application. This
study explores the potential of conditional Generative Adversarial Networks
(cGAN) in translating multispectral imagery to nighttime imagery. A popular
cGAN framework, pix2pix, was adopted and modified to facilitate this
translation using gridded training image pairs derived from Landsat 8 and
Visible Infrared Imaging Radiometer Suite (VIIRS). The results of this study
prove the possibility of multispectral-to-nighttime translation and further
indicate that, with the additional social media data, the generated nighttime
imagery can be very similar to the ground-truth imagery. This study fills the
gap in understanding the composition of satellite observed nighttime light and
provides new paradigms to solve the emerging problems in nighttime remote
sensing fields, including nighttime series construction, light desaturation,
and multi-sensor calibration. | [
"cs.CV",
"eess.IV",
"stat.ML"
] |
Recent hardware developments have dramatically increased the scale of data
parallelism available for neural network training. Among the simplest ways to
harness next-generation hardware is to increase the batch size in standard
mini-batch neural network training algorithms. In this work, we aim to
experimentally characterize the effects of increasing the batch size on
training time, as measured by the number of steps necessary to reach a goal
out-of-sample error. We study how this relationship varies with the training
algorithm, model, and data set, and find extremely large variation between
workloads. Along the way, we show that disagreements in the literature on how
batch size affects model quality can largely be explained by differences in
metaparameter tuning and compute budgets at different batch sizes. We find no
evidence that larger batch sizes degrade out-of-sample performance. Finally, we
discuss the implications of our results on efforts to train neural networks
much faster in the future. Our experimental data is publicly available as a
database of 71,638,836 loss measurements taken over the course of training for
168,160 individual models across 35 workloads. | [
"cs.LG",
"stat.ML"
] |
Wirelessly streaming high quality 360 degree videos is still a challenging
problem. When there are many users watching different 360 degree videos and
competing for the computing and communication resources, the streaming
algorithm at hand should maximize the average quality of experience (QoE) while
guaranteeing a minimum rate for each user. In this paper, we propose a
\emph{cross layer} optimization approach that maximizes the available rate to
each user and efficiently uses it to maximize users' QoE. Particularly, we
consider a tile based 360 degree video streaming, and we optimize a QoE metric
that balances the tradeoff between maximizing each user's QoE and ensuring
fairness among users. We show that the problem can be decoupled into two
interrelated subproblems: (i) a physical layer subproblem whose objective is to
find the download rate for each user, and (ii) an application layer subproblem
whose objective is to use that rate to find a quality decision per tile such
that the user's QoE is maximized. We prove that the physical layer subproblem
can be solved optimally with low complexity and an actor-critic deep
reinforcement learning (DRL) is proposed to leverage the parallel training of
multiple independent agents and solve the application layer subproblem.
Extensive experiments reveal the robustness of our scheme and demonstrate its
significant performance improvement compared to several baseline algorithms. | [
"cs.LG",
"eess.IV"
] |
The sky is a major component of the appearance of a photograph, and its color
and tone can strongly influence the mood of a picture. In nighttime
photography, the sky can also suffer from noise and color artifacts. For this
reason, there is a strong desire to process the sky in isolation from the rest
of the scene to achieve an optimal look. In this work, we propose an automated
method, which can run as a part of a camera pipeline, for creating accurate sky
alpha-masks and using them to improve the appearance of the sky. Our method
performs end-to-end sky optimization in less than half a second per image on a
mobile device. We introduce a method for creating an accurate sky-mask dataset
that is based on partially annotated images that are inpainted and refined by
our modified weighted guided filter. We use this dataset to train a neural
network for semantic sky segmentation. Due to the compute and power constraints
of mobile devices, sky segmentation is performed at a low image resolution. Our
modified weighted guided filter is used for edge-aware upsampling to resize the
alpha-mask to a higher resolution. With this detailed mask we automatically
apply post-processing steps to the sky in isolation, such as automatic
spatially varying white-balance, brightness adjustments, contrast enhancement,
and noise reduction. | [
"cs.CV",
"cs.GR"
] |
While the forward and backward modeling of the process-structure-property
chain has received a lot of attention from the materials community, fewer
efforts have taken into consideration uncertainties. Those arise from a
multitude of sources and their quantification and integration in the inversion
process are essential in meeting the materials design objectives. The first
contribution of this paper is a flexible, fully probabilistic formulation of
such optimization problems that accounts for the uncertainty in the
process-structure and structure-property linkages and enables the
identification of optimal, high-dimensional, process parameters. We employ a
probabilistic, data-driven surrogate for the structure-property link which
expedites computations and enables handling of non-differential objectives. We
couple this with a novel active learning strategy, i.e. a self-supervised
collection of data, which significantly improves accuracy while requiring small
amounts of training data. We demonstrate its efficacy in optimizing the
mechanical and thermal properties of two-phase, random media but envision its
applicability encompasses a wide variety of microstructure-sensitive design
problems. | [
"stat.ML",
"cs.LG"
] |
In this paper, we propose the 3DFeat-Net which learns both 3D feature
detector and descriptor for point cloud matching using weak supervision. Unlike
many existing works, we do not require manual annotation of matching point
clusters. Instead, we leverage on alignment and attention mechanisms to learn
feature correspondences from GPS/INS tagged 3D point clouds without explicitly
specifying them. We create training and benchmark outdoor Lidar datasets, and
experiments show that 3DFeat-Net obtains state-of-the-art performance on these
gravity-aligned datasets. | [
"cs.CV"
] |
Rapid growth in the field of quantitative digital image analysis is paving
the way for researchers to make precise measurements about objects in an image.
To compute quantities from the image such as the density of compressed
materials or the velocity of a shockwave, we must determine object boundaries.
Images containing regions that each have a spatial trend in intensity are of
particular interest. We present a supervised image segmentation method that
incorporates spatial information to locate boundaries between regions with
overlapping intensity histograms. The segmentation of a pixel is determined by
comparing its intensity to distributions from local, nearby pixel intensities.
Because of the statistical nature of the algorithm, we use maximum likelihood
estimation theory to quantify uncertainty about each boundary. We demonstrate
the success of this algorithm on a radiograph of a multicomponent cylinder and
on an optical image of a laser-induced shockwave, and we provide final boundary
locations with associated bands of uncertainty. | [
"cs.CV"
] |
Knowledge Transfer (KT) techniques tackle the problem of transferring the
knowledge from a large and complex neural network into a smaller and faster
one. However, existing KT methods are tailored towards classification tasks and
they cannot be used efficiently for other representation learning tasks. In
this paper a novel knowledge transfer technique, that is capable of training a
student model that maintains the same amount of mutual information between the
learned representation and a set of (possible unknown) labels as the teacher
model, is proposed. Apart from outperforming existing KT techniques, the
proposed method allows for overcoming several limitations of existing methods
providing new insight into KT as well as novel KT applications, ranging from
knowledge transfer from handcrafted feature extractors to {cross-modal} KT from
the textual modality into the representation extracted from the visual modality
of the data. | [
"cs.LG",
"cs.NE",
"stat.ML"
] |
Feature interactions are essential for achieving high accuracy in recommender
systems. Many studies take into account the interaction between every pair of
features. However, this is suboptimal because some feature interactions may not
be that relevant to the recommendation result, and taking them into account may
introduce noise and decrease recommendation accuracy. To make the best out of
feature interactions, we propose a graph neural network approach to effectively
model them, together with a novel technique to automatically detect those
feature interactions that are beneficial in terms of recommendation accuracy.
The automatic feature interaction detection is achieved via edge prediction
with an L0 activation regularization. Our proposed model is proved to be
effective through the information bottleneck principle and statistical
interaction theory. Experimental results show that our model (i) outperforms
existing baselines in terms of accuracy, and (ii) automatically identifies
beneficial feature interactions. | [
"cs.LG",
"cs.IR",
"stat.ML"
] |
The quantitative analysis of 3D confocal microscopy images of the shoot
apical meristem helps understanding the growth process of some plants. Cell
segmentation in these images is crucial for computational plant analysis and
many automated methods have been proposed. However, variations in signal
intensity across the image mitigate the effectiveness of those approaches with
no easy way for user correction. We propose a web-based collaborative 3D image
segmentation application, SEGMENT3D, to leverage automatic segmentation
results. The image is divided into 3D tiles that can be either segmented
interactively from scratch or corrected from a pre-existing segmentation.
Individual segmentation results per tile are then automatically merged via
consensus analysis and then stitched to complete the segmentation for the
entire image stack. SEGMENT3D is a comprehensive application that can be
applied to other 3D imaging modalities and general objects. It also provides an
easy way to create supervised data to advance segmentation using machine
learning models. | [
"cs.CV"
] |
The paper proposes a novel Kernelized image segmentation scheme for noisy
images that utilizes the concept of Smallest Univalue Segment Assimilating
Nucleus (SUSAN) and incorporates spatial constraints by computing circular
colour map induced weights. Fuzzy damping coefficients are obtained for each
nucleus or center pixel on the basis of the corresponding weighted SUSAN area
values, the weights being equal to the inverse of the number of horizontal and
vertical moves required to reach a neighborhood pixel from the center pixel.
These weights are used to vary the contributions of the different nuclei in the
Kernel based framework. The paper also presents an edge quality metric obtained
by fuzzy decision based edge candidate selection and final computation of the
blurriness of the edges after their selection. The inability of existing
algorithms to preserve edge information and structural details in their
segmented maps necessitates the computation of the edge quality factor (EQF)
for all the competing algorithms. Qualitative and quantitative analysis have
been rendered with respect to state-of-the-art algorithms and for images ridden
with varying types of noises. Speckle noise ridden SAR images and Rician noise
ridden Magnetic Resonance Images have also been considered for evaluating the
effectiveness of the proposed algorithm in extracting important segmentation
information. | [
"cs.CV",
"stat.ML"
] |
Computer vision applications such as visual relationship detection and human
object interaction can be formulated as a composite (structured) set detection
problem in which both the parts (subject, object, and predicate) and the sum
(triplet as a whole) are to be detected in a hierarchical fashion. In this
paper, we present a new approach, denoted Part-and-Sum detection Transformer
(PST), to perform end-to-end visual composite set detection. Different from
existing Transformers in which queries are at a single level, we simultaneously
model the joint part and sum hypotheses/interactions with composite queries and
attention modules. We explicitly incorporate sum queries to enable better
modeling of the part-and-sum relations that are absent in the standard
Transformers. Our approach also uses novel tensor-based part queries and
vector-based sum queries, and models their joint interaction. We report
experiments on two vision tasks, visual relationship detection and human object
interaction and demonstrate that PST achieves state of the art results among
single-stage models, while nearly matching the results of custom designed
two-stage models. | [
"cs.CV"
] |
We present a novel image editing system that generates images as the user
provides free-form mask, sketch and color as an input. Our system consist of a
end-to-end trainable convolutional network. Contrary to the existing methods,
our system wholly utilizes free-form user input with color and shape. This
allows the system to respond to the user's sketch and color input, using it as
a guideline to generate an image. In our particular work, we trained network
with additional style loss which made it possible to generate realistic
results, despite large portions of the image being removed. Our proposed
network architecture SC-FEGAN is well suited to generate high quality synthetic
image using intuitive user inputs. | [
"cs.CV"
] |
Zero-shot action recognition can recognize samples of unseen classes that are
unavailable in training by exploring common latent semantic representation in
samples. However, most methods neglected the connotative relation and
extensional relation between the action classes, which leads to the poor
generalization ability of the zero-shot learning. Furthermore, the learned
classifier incline to predict the samples of seen class, which leads to poor
classification performance. To solve the above problems, we propose a two-stage
deep neural network for zero-shot action recognition, which consists of a
feature generation sub-network serving as the sampling stage and a graph
attention sub-network serving as the classification stage. In the sampling
stage, we utilize a generative adversarial networks (GAN) trained by action
features and word vectors of seen classes to synthesize the action features of
unseen classes, which can balance the training sample data of seen classes and
unseen classes. In the classification stage, we construct a knowledge graph
(KG) based on the relationship between word vectors of action classes and
related objects, and propose a graph convolution network (GCN) based on
attention mechanism, which dynamically updates the relationship between action
classes and objects, and enhances the generalization ability of zero-shot
learning. In both stages, we all use word vectors as bridges for feature
generation and classifier generalization from seen classes to unseen classes.
We compare our method with state-of-the-art methods on UCF101 and HMDB51
datasets. Experimental results show that our proposed method improves the
classification performance of the trained classifier and achieves higher
accuracy. | [
"cs.CV"
] |
We explore building generative neural network models of popular reinforcement
learning environments. Our world model can be trained quickly in an
unsupervised manner to learn a compressed spatial and temporal representation
of the environment. By using features extracted from the world model as inputs
to an agent, we can train a very compact and simple policy that can solve the
required task. We can even train our agent entirely inside of its own
hallucinated dream generated by its world model, and transfer this policy back
into the actual environment.
An interactive version of this paper is available at
https://worldmodels.github.io/ | [
"cs.LG",
"stat.ML"
] |
Recently, convolutional neural network (CNN) has demonstrated significant
success for image restoration (IR) tasks (e.g., image super-resolution, image
deblurring, rain streak removal, and dehazing). However, existing CNN based
models are commonly implemented as a single-path stream to enrich feature
representations from low-quality (LQ) input space for final predictions, which
fail to fully incorporate preceding low-level contexts into later high-level
features within networks, thereby producing inferior results. In this paper, we
present a deep interleaved network (DIN) that learns how information at
different states should be combined for high-quality (HQ) images
reconstruction. The proposed DIN follows a multi-path and multi-branch pattern
allowing multiple interconnected branches to interleave and fuse at different
states. In this way, the shallow information can guide deep representative
features prediction to enhance the feature expression ability. Furthermore, we
propose asymmetric co-attention (AsyCA) which is attached at each interleaved
node to model the feature dependencies. Such AsyCA can not only adaptively
emphasize the informative features from different states, but also improves the
discriminative ability of networks. Our presented DIN can be trained end-to-end
and applied to various IR tasks. Comprehensive evaluations on public benchmarks
and real-world datasets demonstrate that the proposed DIN perform favorably
against the state-of-the-art methods quantitatively and qualitatively. | [
"cs.CV"
] |
Transfer learning from huge natural image datasets, fine-tuning of deep
neural networks and the use of the corresponding pre-trained networks have
become de facto the core of art analysis applications. Nevertheless, the
effects of transfer learning are still poorly understood. In this paper, we
first use techniques for visualizing the network internal representations in
order to provide clues to the understanding of what the network has learned on
artistic images. Then, we provide a quantitative analysis of the changes
introduced by the learning process thanks to metrics in both the feature and
parameter spaces, as well as metrics computed on the set of maximal activation
images. These analyses are performed on several variations of the transfer
learning procedure. In particular, we observed that the network could
specialize some pre-trained filters to the new image modality and also that
higher layers tend to concentrate classes. Finally, we have shown that a double
fine-tuning involving a medium-size artistic dataset can improve the
classification on smaller datasets, even when the task changes. | [
"cs.CV"
] |
In recent years graph neural network (GNN)-based approaches have become a
popular strategy for processing point cloud data, regularly achieving
state-of-the-art performance on a variety of tasks. To date, the research
community has primarily focused on improving model expressiveness, with
secondary thought given to how to design models that can run efficiently on
resource constrained mobile devices including smartphones or mixed reality
headsets. In this work we make a step towards improving the efficiency of these
models by making the observation that these GNN models are heavily limited by
the representational power of their first, feature extracting, layer. We find
that it is possible to radically simplify these models so long as the feature
extraction layer is retained with minimal degradation to model performance;
further, we discover that it is possible to improve performance overall on
ModelNet40 and S3DIS by improving the design of the feature extractor. Our
approach reduces memory consumption by 20$\times$ and latency by up to
9.9$\times$ for graph layers in models such as DGCNN; overall, we achieve
speed-ups of up to 4.5$\times$ and peak memory reductions of 72.5%. | [
"cs.CV",
"cs.LG"
] |
Over the past decades, state-of-the-art medical image segmentation has
heavily rested on signal processing paradigms, most notably registration-based
label propagation and pair-wise patch comparison, which are generally slow
despite a high segmentation accuracy. In recent years, deep learning has
revolutionalized computer vision with many practices outperforming prior art,
in particular the convolutional neural network (CNN) studies on image
classification. Deep CNN has also started being applied to medical image
segmentation lately, but generally involves long training and demanding memory
requirements, achieving limited success. We propose a patch-based deep learning
framework based on a revisit to the classic neural network model with
substantial modernization, including the use of Rectified Linear Unit (ReLU)
activation, dropout layers, 2.5D tri-planar patch multi-pathway settings. In a
test application to hippocampus segmentation using 100 brain MR images from the
ADNI database, our approach significantly outperformed prior art in terms of
both segmentation accuracy and speed: scoring a median Dice score up to 90.98%
on a near real-time performance (<1s). | [
"cs.CV"
] |
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