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2306.03010
|
2023-06-05T16:25:33Z
|
Interval Load Forecasting for Individual Households in the Presence of
Electric Vehicle Charging
|
[
"Raiden Skala",
"Mohamed Ahmed T. A. Elgalhud",
"Katarina Grolinger",
"Syed Mir"
] |
The transition to Electric Vehicles (EV) in place of traditional internal
combustion engines is increasing societal demand for electricity. The ability
to integrate the additional demand from EV charging into forecasting
electricity demand is critical for maintaining the reliability of electricity
generation and distribution. Load forecasting studies typically exclude
households with home EV charging, focusing on offices, schools, and public
charging stations. Moreover, they provide point forecasts which do not offer
information about prediction uncertainty. Consequently, this paper proposes the
Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load
forecasting in presence of EV charging. The approach takes advantage of the
LSTM model to capture the time dependencies and uses the dropout layer with
Bayesian inference to generate prediction intervals. Results show that the
proposed LSTM-BNNs achieve accuracy similar to point forecasts with the
advantage of prediction intervals. Moreover, the impact of lockdowns related to
the COVID-19 pandemic on the load forecasting model is examined, and the
analysis shows that there is no major change in the model performance as, for
the considered households, the randomness of the EV charging outweighs the
change due to pandemic.
|
[
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY"
] | false |
2306.03014
|
2023-06-05T16:30:17Z
|
On the Behavior of Intrusive and Non-intrusive Speech Enhancement
Metrics in Predictive and Generative Settings
|
[
"Danilo de Oliveira",
"Julius Richter",
"Jean-Marie Lemercier",
"Tal Peer",
"Timo Gerkmann"
] |
Since its inception, the field of deep speech enhancement has been dominated
by predictive (discriminative) approaches, such as spectral mapping or masking.
Recently, however, novel generative approaches have been applied to speech
enhancement, attaining good denoising performance with high subjective quality
scores. At the same time, advances in deep learning also allowed for the
creation of neural network-based metrics, which have desirable traits such as
being able to work without a reference (non-intrusively). Since generatively
enhanced speech tends to exhibit radically different residual distortions, its
evaluation using instrumental speech metrics may behave differently compared to
predictively enhanced speech. In this paper, we evaluate the performance of the
same speech enhancement backbone trained under predictive and generative
paradigms on a variety of metrics and show that intrusive and non-intrusive
measures correlate differently for each paradigm. This analysis motivates the
search for metrics that can together paint a complete and unbiased picture of
speech enhancement performance, irrespective of the model's training process.
|
[
"eess.AS",
"cs.LG",
"cs.SD"
] | false |
2306.03040
|
2023-06-05T17:03:10Z
|
Learning Similarity among Users for Personalized Session-Based
Recommendation from hierarchical structure of User-Session-Item
|
[
"Jisoo Cha",
"Haemin Jeong",
"Wooju Kim"
] |
The task of the session-based recommendation is to predict the next
interaction of the user based on the anonymized user's behavior pattern. And
personalized version of this system is a promising research field due to its
availability to deal with user information. However, there's a problem that the
user's preferences and historical sessions were not considered in the typical
session-based recommendation since it concentrates only on user-item
interaction. In addition, the existing personalized session-based
recommendation model has a limited capability in that it only considers the
preference of the current user without considering those of similar users. It
means there can be the loss of information included within the hierarchical
data structure of the user-session-item. To tackle with this problem, we
propose USP-SBR(abbr. of User Similarity Powered - Session Based Recommender).
To model global historical sessions of users, we propose UserGraph that has two
types of nodes - ItemNode and UserNode. We then connect the nodes with three
types of edges. The first type of edges connects ItemNode as chronological
order, and the second connects ItemNode to UserNode, and the last connects
UserNode to ItemNode. With these user embeddings, we propose additional
contrastive loss, that makes users with similar intention be close to each
other in the vector space. we apply graph neural network on these UserGraph and
update nodes. Experimental results on two real-world datasets demonstrate that
our method outperforms some state-of-the-art approaches.
|
[
"cs.IR",
"cs.AI",
"cs.LG",
"68P20"
] | false |
2306.03065
|
2023-06-05T17:43:46Z
|
LibAUC: A Deep Learning Library for X-Risk Optimization
|
[
"Zhuoning Yuan",
"Dixian Zhu",
"Zi-Hao Qiu",
"Gang Li",
"Xuanhui Wang",
"Tianbao Yang"
] |
This paper introduces the award-winning deep learning (DL) library called
LibAUC for implementing state-of-the-art algorithms towards optimizing a family
of risk functions named X-risks. X-risks refer to a family of compositional
functions in which the loss function of each data point is defined in a way
that contrasts the data point with a large number of others. They have broad
applications in AI for solving classical and emerging problems, including but
not limited to classification for imbalanced data (CID), learning to rank
(LTR), and contrastive learning of representations (CLR). The motivation of
developing LibAUC is to address the convergence issues of existing libraries
for solving these problems. In particular, existing libraries may not converge
or require very large mini-batch sizes in order to attain good performance for
these problems, due to the usage of the standard mini-batch technique in the
empirical risk minimization (ERM) framework. Our library is for deep X-risk
optimization (DXO) that has achieved great success in solving a variety of
tasks for CID, LTR and CLR. The contributions of this paper include: (1) It
introduces a new mini-batch based pipeline for implementing DXO algorithms,
which differs from existing DL pipeline in the design of controlled data
samplers and dynamic mini-batch losses; (2) It provides extensive benchmarking
experiments for ablation studies and comparison with existing libraries. The
LibAUC library features scalable performance for millions of items to be
contrasted, faster and better convergence than existing libraries for
optimizing X-risks, seamless PyTorch deployment and versatile APIs for various
loss optimization. Our library is available to the open source community at
https://github.com/Optimization-AI/LibAUC, to facilitate further academic
research and industrial applications.
|
[
"cs.LG",
"cs.AI",
"math.OC",
"stat.ML"
] | false |
2306.03109
|
2023-06-05T04:34:54Z
|
Machine Learning Force Fields with Data Cost Aware Training
|
[
"Alexander Bukharin",
"Tianyi Liu",
"Shengjie Wang",
"Simiao Zuo",
"Weihao Gao",
"Wen Yan",
"Tuo Zhao"
] |
Machine learning force fields (MLFF) have been proposed to accelerate
molecular dynamics (MD) simulation, which finds widespread applications in
chemistry and biomedical research. Even for the most data-efficient MLFFs,
reaching chemical accuracy can require hundreds of frames of force and energy
labels generated by expensive quantum mechanical algorithms, which may scale as
$O(n^3)$ to $O(n^7)$, with $n$ proportional to the number of basis functions.
To address this issue, we propose a multi-stage computational framework --
ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of
cheap inaccurate data and expensive accurate data. The motivation behind
ASTEROID is that inaccurate data, though incurring large bias, can help capture
the sophisticated structures of the underlying force field. Therefore, we first
train a MLFF model on a large amount of inaccurate training data, employing a
bias-aware loss function to prevent the model from overfitting tahe potential
bias of this data. We then fine-tune the obtained model using a small amount of
accurate training data, which preserves the knowledge learned from the
inaccurate training data while significantly improving the model's accuracy.
Moreover, we propose a variant of ASTEROID based on score matching for the
setting where the inaccurate training data are unlabeled. Extensive experiments
on MD datasets and downstream tasks validate the efficacy of ASTEROID. Our code
and data are available at https://github.com/abukharin3/asteroid.
|
[
"q-bio.QM",
"cs.LG",
"physics.chem-ph"
] | false |
2306.03112
|
2023-06-05T08:38:30Z
|
Synthesizing Affective Neurophysiological Signals Using Generative
Models: A Review Paper
|
[
"Alireza F. Nia",
"Vanessa Tang",
"Gonzalo Maso Talou",
"Mark Billinghurst"
] |
The integration of emotional intelligence in machines is an important step in
advancing human-computer interaction. This demands the development of reliable
end-to-end emotion recognition systems. However, the scarcity of public
affective datasets presents a challenge. In this literature review, we
emphasize the use of generative models to address this issue in
neurophysiological signals, particularly Electroencephalogram (EEG) and
Functional Near-Infrared Spectroscopy (fNIRS). We provide a comprehensive
analysis of different generative models used in the field, examining their
input formulation, deployment strategies, and methodologies for evaluating the
quality of synthesized data. This review serves as a comprehensive overview,
offering insights into the advantages, challenges, and promising future
directions in the application of generative models in emotion recognition
systems. Through this review, we aim to facilitate the progression of
neurophysiological data augmentation, thereby supporting the development of
more efficient and reliable emotion recognition systems.
|
[
"cs.HC",
"cs.AI",
"cs.LG",
"q-bio.NC"
] | false |
2306.03115
|
2023-06-05T13:13:19Z
|
AutoExp: A multidisciplinary, multi-sensor framework to evaluate human
activities in self-driving cars
|
[
"Carlos Crispim-Junior",
"Romain Guesdon",
"Christophe Jallais",
"Florent Laroche",
"Stephanie Souche-Le Corvec",
"Laure Tougne Rodet"
] |
The adoption of self-driving cars will certainly revolutionize our lives,
even though they may take more time to become fully autonomous than initially
predicted. The first vehicles are already present in certain cities of the
world, as part of experimental robot-taxi services. However, most existing
studies focus on the navigation part of such vehicles. We currently miss
methods, datasets, and studies to assess the in-cabin human component of the
adoption of such technology in real-world conditions. This paper proposes an
experimental framework to study the activities of occupants of self-driving
cars using a multidisciplinary approach (computer vision associated with human
and social sciences), particularly non-driving related activities. The
framework is composed of an experimentation scenario, and a data acquisition
module. We seek firstly to capture real-world data about the usage of the
vehicle in the nearest possible, real-world conditions, and secondly to create
a dataset containing in-cabin human activities to foster the development and
evaluation of computer vision algorithms. The acquisition module records
multiple views of the front seats of the vehicle (Intel RGB-D and GoPro
cameras); in addition to survey data about the internal states and attitudes of
participants towards this type of vehicle before, during, and after the
experimentation. We evaluated the proposed framework with the realization of
real-world experimentation with 30 participants (1 hour each) to study the
acceptance of SDCs of SAE level 4.
|
[
"cs.HC",
"cs.AI",
"cs.LG"
] | false |
2306.03175
|
2023-06-05T18:32:53Z
|
Infusing Lattice Symmetry Priors in Attention Mechanisms for
Sample-Efficient Abstract Geometric Reasoning
|
[
"Mattia Atzeni",
"Mrinmaya Sachan",
"Andreas Loukas"
] |
The Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) and its most
recent language-complete instantiation (LARC) has been postulated as an
important step towards general AI. Yet, even state-of-the-art machine learning
models struggle to achieve meaningful performance on these problems, falling
behind non-learning based approaches. We argue that solving these tasks
requires extreme generalization that can only be achieved by proper accounting
for core knowledge priors. As a step towards this goal, we focus on geometry
priors and introduce LatFormer, a model that incorporates lattice symmetry
priors in attention masks. We show that, for any transformation of the
hypercubic lattice, there exists a binary attention mask that implements that
group action. Hence, our study motivates a modification to the standard
attention mechanism, where attention weights are scaled using soft masks
generated by a convolutional network. Experiments on synthetic geometric
reasoning show that LatFormer requires 2 orders of magnitude fewer data than
standard attention and transformers. Moreover, our results on ARC and LARC
tasks that incorporate geometric priors provide preliminary evidence that these
complex datasets do not lie out of the reach of deep learning models.
|
[
"cs.AI",
"cs.LG",
"stat.ML"
] | false |
2306.03179
|
2023-06-05T18:40:35Z
|
Fair Patient Model: Mitigating Bias in the Patient Representation
Learned from the Electronic Health Records
|
[
"Sonish Sivarajkumar",
"Yufei Huang",
"Yanshan Wang"
] |
Objective: To pre-train fair and unbiased patient representations from
Electronic Health Records (EHRs) using a novel weighted loss function that
reduces bias and improves fairness in deep representation learning models.
Methods: We defined a new loss function, called weighted loss function, in
the deep representation learning model to balance the importance of different
groups of patients and features. We applied the proposed model, called Fair
Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset
and learned patient representations for four clinical outcome prediction tasks.
Results: FPM outperformed the baseline models in terms of three fairness
metrics: demographic parity, equality of opportunity difference, and equalized
odds ratio. FPM also achieved comparable predictive performance with the
baselines, with an average accuracy of 0.7912. Feature analysis revealed that
FPM captured more information from clinical features than the baselines.
Conclusion: FPM is a novel method to pre-train fair and unbiased patient
representations from EHR data using a weighted loss function. The learned
representations can be used for various downstream tasks in healthcare and can
be extended to other domains where bias and fairness are important.
|
[
"cs.LG",
"cs.AI",
"cs.CY"
] | false |
2306.03195
|
2023-06-05T19:13:44Z
|
Lumos in the Night Sky: AI-enabled Visual Tool for Exploring Night-Time
Light Patterns
|
[
"Jakob Hederich",
"Shreya Ghosh",
"Zeyu He",
"Prasenjit Mitra"
] |
We introduce NightPulse, an interactive tool for Night-time light (NTL) data
visualization and analytics, which enables researchers and stakeholders to
explore and analyze NTL data with a user-friendly platform. Powered by
efficient system architecture, NightPulse supports image segmentation,
clustering, and change pattern detection to identify urban development and
sprawl patterns. It captures temporal trends of NTL and semantics of cities,
answering questions about demographic factors, city boundaries, and unusual
differences.
|
[
"cs.HC",
"cs.AI",
"cs.IR",
"cs.LG"
] | false |
2306.03202
|
2023-06-05T19:22:02Z
|
Nonlinear Distributionally Robust Optimization
|
[
"Mohammed Rayyan Sheriff",
"Peyman Mohajerin Esfahani"
] |
This article focuses on a class of distributionally robust optimization (DRO)
problems where, unlike the growing body of the literature, the objective
function is potentially non-linear in the distribution. Existing methods to
optimize nonlinear functions in probability space use the Frechet derivatives,
which present both theoretical and computational challenges. Motivated by this,
we propose an alternative notion for the derivative and corresponding
smoothness based on Gateaux (G)-derivative for generic risk measures. These
concepts are explained via three running risk measure examples of variance,
entropic risk, and risk on finite support sets. We then propose a G-derivative
based Frank-Wolfe~(FW) algorithm for generic non-linear optimization problems
in probability spaces and establish its convergence under the proposed notion
of smoothness in a completely norm-independent manner. We use the set-up of the
FW algorithm to devise a methodology to compute a saddle point of the
non-linear DRO problem. Finally, for the minimum variance portfolio selection
problem we analyze the regularity conditions and compute the FW-oracle in
various settings, and validate the theoretical results numerically.
|
[
"stat.ML",
"cs.LG",
"math.OC"
] | false |
2306.03221
|
2023-06-05T20:11:30Z
|
Structural Re-weighting Improves Graph Domain Adaptation
|
[
"Shikun Liu",
"Tianchun Li",
"Yongbin Feng",
"Nhan Tran",
"Han Zhao",
"Qiu Qiang",
"Pan Li"
] |
In many real-world applications, graph-structured data used for training and
testing have differences in distribution, such as in high energy physics (HEP)
where simulation data used for training may not match real experiments. Graph
domain adaptation (GDA) is a method used to address these differences. However,
current GDA primarily works by aligning the distributions of node
representations output by a single graph neural network encoder shared across
the training and testing domains, which may often yield sub-optimal solutions.
This work examines different impacts of distribution shifts caused by either
graph structure or node attributes and identifies a new type of shift, named
conditional structure shift (CSS), which current GDA approaches are provably
sub-optimal to deal with. A novel approach, called structural reweighting
(StruRW), is proposed to address this issue and is tested on synthetic graphs,
four benchmark datasets, and a new application in HEP. StruRW has shown
significant performance improvement over the baselines in the settings with
large graph structure shifts, and reasonable performance improvement when node
attribute shift dominates.
|
[
"cs.LG",
"cs.AI",
"cs.SI"
] | false |
2306.03249
|
2023-06-05T21:08:34Z
|
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood
Estimation for Latent Gaussian Models
|
[
"Alexander Lin",
"Bahareh Tolooshams",
"Yves Atchadé",
"Demba Ba"
] |
Latent Gaussian models have a rich history in statistics and machine
learning, with applications ranging from factor analysis to compressed sensing
to time series analysis. The classical method for maximizing the likelihood of
these models is the expectation-maximization (EM) algorithm. For problems with
high-dimensional latent variables and large datasets, EM scales poorly because
it needs to invert as many large covariance matrices as the number of data
points. We introduce probabilistic unrolling, a method that combines Monte
Carlo sampling with iterative linear solvers to circumvent matrix inversion.
Our theoretical analyses reveal that unrolling and backpropagation through the
iterations of the solver can accelerate gradient estimation for maximum
likelihood estimation. In experiments on simulated and real data, we
demonstrate that probabilistic unrolling learns latent Gaussian models up to an
order of magnitude faster than gradient EM, with minimal losses in model
performance.
|
[
"cs.LG",
"eess.SP",
"stat.CO"
] | false |
2306.03257
|
2023-06-05T21:19:37Z
|
Generating Private Synthetic Data with Genetic Algorithms
|
[
"Terrance Liu",
"Jingwu Tang",
"Giuseppe Vietri",
"Zhiwei Steven Wu"
] |
We study the problem of efficiently generating differentially private
synthetic data that approximate the statistical properties of an underlying
sensitive dataset. In recent years, there has been a growing line of work that
approaches this problem using first-order optimization techniques. However,
such techniques are restricted to optimizing differentiable objectives only,
severely limiting the types of analyses that can be conducted. For example,
first-order mechanisms have been primarily successful in approximating
statistical queries only in the form of marginals for discrete data domains. In
some cases, one can circumvent such issues by relaxing the task's objective to
maintain differentiability. However, even when possible, these approaches
impose a fundamental limitation in which modifications to the minimization
problem become additional sources of error. Therefore, we propose Private-GSD,
a private genetic algorithm based on zeroth-order optimization heuristics that
do not require modifying the original objective. As a result, it avoids the
aforementioned limitations of first-order optimization. We empirically evaluate
Private-GSD against baseline algorithms on data derived from the American
Community Survey across a variety of statistics--otherwise known as statistical
queries--both for discrete and real-valued attributes. We show that Private-GSD
outperforms the state-of-the-art methods on non-differential queries while
matching accuracy in approximating differentiable ones.
|
[
"cs.NE",
"cs.CR",
"cs.LG"
] | false |
2306.03288
|
2023-06-05T22:21:26Z
|
Deep Learning From Crowdsourced Labels: Coupled Cross-entropy
Minimization, Identifiability, and Regularization
|
[
"Shahana Ibrahim",
"Tri Nguyen",
"Xiao Fu"
] |
Using noisy crowdsourced labels from multiple annotators, a deep
learning-based end-to-end (E2E) system aims to learn the label correction
mechanism and the neural classifier simultaneously. To this end, many E2E
systems concatenate the neural classifier with multiple annotator-specific
``label confusion'' layers and co-train the two parts in a parameter-coupled
manner. The formulated coupled cross-entropy minimization (CCEM)-type criteria
are intuitive and work well in practice. Nonetheless, theoretical understanding
of the CCEM criterion has been limited. The contribution of this work is
twofold: First, performance guarantees of the CCEM criterion are presented. Our
analysis reveals for the first time that the CCEM can indeed correctly identify
the annotators' confusion characteristics and the desired ``ground-truth''
neural classifier under realistic conditions, e.g., when only incomplete
annotator labeling and finite samples are available. Second, based on the
insights learned from our analysis, two regularized variants of the CCEM are
proposed. The regularization terms provably enhance the identifiability of the
target model parameters in various more challenging cases. A series of
synthetic and real data experiments are presented to showcase the effectiveness
of our approach.
|
[
"cs.LG",
"eess.SP",
"stat.ML"
] | false |
2306.03314
|
2023-06-05T23:55:37Z
|
Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM
Agents
|
[
"Yashar Talebirad",
"Amirhossein Nadiri"
] |
In this paper, we present a novel framework for enhancing the capabilities of
large language models (LLMs) by leveraging the power of multi-agent systems.
Our framework introduces a collaborative environment where multiple intelligent
agent components, each with distinctive attributes and roles, work together to
handle complex tasks more efficiently and effectively. We demonstrate the
practicality and versatility of our framework through case studies in
artificial general intelligence (AGI), specifically focusing on the Auto-GPT
and BabyAGI models. We also examine the "Gorilla" model, which integrates
external APIs into the LLM. Our framework addresses limitations and challenges
such as looping issues, security risks, scalability, system evaluation, and
ethical considerations. By modeling various domains such as courtroom
simulations and software development scenarios, we showcase the potential
applications and benefits of our proposed multi-agent system. Our framework
provides an avenue for advancing the capabilities and performance of LLMs
through collaboration and knowledge exchange among intelligent agents.
|
[
"cs.AI",
"cs.LG",
"cs.MA"
] | false |
2306.04648
|
2023-06-05T13:57:23Z
|
On training locally adaptive CP
|
[
"Nicolo Colombo"
] |
We address the problem of making Conformal Prediction (CP) intervals locally
adaptive. Most existing methods focus on approximating the object-conditional
validity of the intervals by partitioning or re-weighting the calibration set.
Our strategy is new and conceptually different. Instead of re-weighting the
calibration data, we redefine the conformity measure through a trainable change
of variables, $A \to \phi_X(A)$, that depends explicitly on the object
attributes, $X$. Under certain conditions and if $\phi_X$ is monotonic in $A$
for any $X$, the transformations produce prediction intervals that are
guaranteed to be marginally valid and have $X$-dependent sizes. We describe how
to parameterize and train $\phi_X$ to maximize the interval efficiency.
Contrary to other CP-aware training methods, the objective function is smooth
and can be minimized through standard gradient methods without approximations.
|
[
"cs.LG",
"cs.AI",
"stat.ML"
] | false |
2306.02826
|
2023-06-05T12:22:46Z
|
Near-Optimal Quantum Coreset Construction Algorithms for Clustering
|
[
"Yecheng Xue",
"Xiaoyu Chen",
"Tongyang Li",
"Shaofeng H. -C. Jiang"
] |
$k$-Clustering in $\mathbb{R}^d$ (e.g., $k$-median and $k$-means) is a
fundamental machine learning problem. While near-linear time approximation
algorithms were known in the classical setting for a dataset with cardinality
$n$, it remains open to find sublinear-time quantum algorithms. We give quantum
algorithms that find coresets for $k$-clustering in $\mathbb{R}^d$ with
$\tilde{O}(\sqrt{nk}d^{3/2})$ query complexity. Our coreset reduces the input
size from $n$ to $\mathrm{poly}(k\epsilon^{-1}d)$, so that existing
$\alpha$-approximation algorithms for clustering can run on top of it and yield
$(1 + \epsilon)\alpha$-approximation. This eventually yields a quadratic
speedup for various $k$-clustering approximation algorithms. We complement our
algorithm with a nearly matching lower bound, that any quantum algorithm must
make $\Omega(\sqrt{nk})$ queries in order to achieve even $O(1)$-approximation
for $k$-clustering.
|
[
"quant-ph",
"cs.AI",
"cs.DS",
"cs.LG",
"stat.ML"
] | false |
2306.02971
|
2023-06-05T15:35:00Z
|
Online Learning with Feedback Graphs: The True Shape of Regret
|
[
"Tomáš Kocák",
"Alexandra Carpentier"
] |
Sequential learning with feedback graphs is a natural extension of the
multi-armed bandit problem where the problem is equipped with an underlying
graph structure that provides additional information - playing an action
reveals the losses of all the neighbors of the action. This problem was
introduced by \citet{mannor2011} and received considerable attention in recent
years. It is generally stated in the literature that the minimax regret rate
for this problem is of order $\sqrt{\alpha T}$, where $\alpha$ is the
independence number of the graph, and $T$ is the time horizon. However, this is
proven only when the number of rounds $T$ is larger than $\alpha^3$, which
poses a significant restriction for the usability of this result in large
graphs. In this paper, we define a new quantity $R^*$, called the \emph{problem
complexity}, and prove that the minimax regret is proportional to $R^*$ for any
graph and time horizon $T$. Introducing an intricate exploration strategy, we
define the \mainAlgorithm algorithm that achieves the minimax optimal regret
bound and becomes the first provably optimal algorithm for this setting, even
if $T$ is smaller than $\alpha^3$.
|
[
"cs.LG",
"cs.IT",
"math.IT",
"math.ST",
"stat.TH"
] | false |
2307.00004
|
2023-06-05T23:23:04Z
|
PV Fleet Modeling via Smooth Periodic Gaussian Copula
|
[
"Mehmet G. Ogut",
"Bennet Meyers",
"Stephen P. Boyd"
] |
We present a method for jointly modeling power generation from a fleet of
photovoltaic (PV) systems. We propose a white-box method that finds a function
that invertibly maps vector time-series data to independent and identically
distributed standard normal variables. The proposed method, based on a novel
approach for fitting a smooth, periodic copula transform to data, captures many
aspects of the data such as diurnal variation in the distribution of power
output, dependencies among different PV systems, and dependencies across time.
It consists of interpretable steps and is scalable to many systems. The
resulting joint probability model of PV fleet output across systems and time
can be used to generate synthetic data, impute missing data, perform anomaly
detection, and make forecasts. In this paper, we explain the method and
demonstrate these applications.
|
[
"stat.AP",
"cs.AI",
"cs.LG",
"eess.SP",
"physics.data-an"
] | false |
2306.03318
|
2023-06-06T00:01:40Z
|
Student Classroom Behavior Detection based on Improved YOLOv7
|
[
"Fan Yang"
] |
Accurately detecting student behavior in classroom videos can aid in
analyzing their classroom performance and improving teaching effectiveness.
However, the current accuracy rate in behavior detection is low. To address
this challenge, we propose the Student Classroom Behavior Detection method,
based on improved YOLOv7. First, we created the Student Classroom Behavior
dataset (SCB-Dataset), which includes 18.4k labels and 4.2k images, covering
three behaviors: hand raising, reading, and writing. To improve detection
accuracy in crowded scenes, we integrated the biformer attention module and
Wise-IoU into the YOLOv7 network. Finally, experiments were conducted on the
SCB-Dataset, and the model achieved an [email protected] of 79%, resulting in a 1.8%
improvement over previous results. The SCB-Dataset and code are available for
download at: https://github.com/Whiffe/SCB-dataset.
|
[
"cs.CV"
] | false |
2306.03380
|
2023-06-06T03:40:29Z
|
A Unified Framework to Super-Resolve Face Images of Varied Low
Resolutions
|
[
"Qiuyu Peng",
"Zifei Jiang",
"Yan Huang",
"Jingliang Peng"
] |
The existing face image super-resolution (FSR) algorithms usually train a
specific model for a specific low input resolution for optimal results. By
contrast, we explore in this work a unified framework that is trained once and
then used to super-resolve input face images of varied low resolutions. For
that purpose, we propose a novel neural network architecture that is composed
of three anchor auto-encoders, one feature weight regressor and a final image
decoder. The three anchor auto-encoders are meant for optimal FSR for three
pre-defined low input resolutions, or named anchor resolutions, respectively.
An input face image of an arbitrary low resolution is firstly up-scaled to the
target resolution by bi-cubic interpolation and then fed to the three
auto-encoders in parallel. The three encoded anchor features are then fused
with weights determined by the feature weight regressor. At last, the fused
feature is sent to the final image decoder to derive the super-resolution
result. As shown by experiments, the proposed algorithm achieves robust and
state-of-the-art performance over a wide range of low input resolutions by a
single framework. Code and models will be made available after the publication
of this work.
|
[
"cs.CV"
] | false |
2306.03422
|
2023-06-06T05:48:09Z
|
Prompting Large Language Models to Reformulate Queries for Moment
Localization
|
[
"Wenfeng Yan",
"Shaoxiang Chen",
"Zuxuan Wu",
"Yu-Gang Jiang"
] |
The task of moment localization is to localize a temporal moment in an
untrimmed video for a given natural language query. Since untrimmed video
contains highly redundant contents, the quality of the query is crucial for
accurately localizing moments, i.e., the query should provide precise
information about the target moment so that the localization model can
understand what to look for in the videos. However, the natural language
queries in current datasets may not be easy to understand for existing models.
For example, the Ego4D dataset uses question sentences as the query to describe
relatively complex moments. While being natural and straightforward for humans,
understanding such question sentences are challenging for mainstream moment
localization models like 2D-TAN. Inspired by the recent success of large
language models, especially their ability of understanding and generating
complex natural language contents, in this extended abstract, we make early
attempts at reformulating the moment queries into a set of instructions using
large language models and making them more friendly to the localization models.
|
[
"cs.CV"
] | false |
2306.03428
|
2023-06-06T05:59:23Z
|
GaitGCI: Generative Counterfactual Intervention for Gait Recognition
|
[
"Huanzhang Dou",
"Pengyi Zhang",
"Wei Su",
"Yunlong Yu",
"Yining Lin",
"Xi Li"
] |
Gait is one of the most promising biometrics that aims to identify
pedestrians from their walking patterns. However, prevailing methods are
susceptible to confounders, resulting in the networks hardly focusing on the
regions that reflect effective walking patterns. To address this fundamental
problem in gait recognition, we propose a Generative Counterfactual
Intervention framework, dubbed GaitGCI, consisting of Counterfactual
Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution
(DCDC). CIL eliminates the impacts of confounders by maximizing the likelihood
difference between factual/counterfactual attention while DCDC adaptively
generates sample-wise factual/counterfactual attention to efficiently perceive
the sample-wise properties. With matrix decomposition and diversity constraint,
DCDC guarantees the model to be efficient and effective. Extensive experiments
indicate that proposed GaitGCI: 1) could effectively focus on the
discriminative and interpretable regions that reflect gait pattern; 2) is
model-agnostic and could be plugged into existing models to improve performance
with nearly no extra cost; 3) efficiently achieves state-of-the-art performance
on arbitrary scenarios (in-the-lab and in-the-wild).
|
[
"cs.CV"
] | false |
2306.03482
|
2023-06-06T08:08:18Z
|
Looking and Listening: Audio Guided Text Recognition
|
[
"Wenwen Yu",
"Mingyu Liu",
"Biao Yang",
"Enming Zhang",
"Deqiang Jiang",
"Xing Sun",
"Yuliang Liu",
"Xiang Bai"
] |
Text recognition in the wild is a long-standing problem in computer vision.
Driven by end-to-end deep learning, recent studies suggest vision and language
processing are effective for scene text recognition. Yet, solving edit errors
such as add, delete, or replace is still the main challenge for existing
approaches. In fact, the content of the text and its audio are naturally
corresponding to each other, i.e., a single character error may result in a
clear different pronunciation. In this paper, we propose the AudioOCR, a simple
yet effective probabilistic audio decoder for mel spectrogram sequence
prediction to guide the scene text recognition, which only participates in the
training phase and brings no extra cost during the inference stage. The
underlying principle of AudioOCR can be easily applied to the existing
approaches. Experiments using 7 previous scene text recognition methods on 12
existing regular, irregular, and occluded benchmarks demonstrate our proposed
method can bring consistent improvement. More importantly, through our
experimentation, we show that AudioOCR possesses a generalizability that
extends to more challenging scenarios, including recognizing non-English text,
out-of-vocabulary words, and text with various accents. Code will be available
at https://github.com/wenwenyu/AudioOCR.
|
[
"cs.CV"
] | false |
2306.03497
|
2023-06-06T08:28:55Z
|
Instructive Feature Enhancement for Dichotomous Medical Image
Segmentation
|
[
"Lian Liu",
"Han Zhou",
"Jiongquan Chen",
"Sijing Liu",
"Wenlong Shi",
"Dong Ni",
"Deng-Ping Fan",
"Xin Yang"
] |
Deep neural networks have been widely applied in dichotomous medical image
segmentation (DMIS) of many anatomical structures in several modalities,
achieving promising performance. However, existing networks tend to struggle
with task-specific, heavy and complex designs to improve accuracy. They made
little instructions to which feature channels would be more beneficial for
segmentation, and that may be why the performance and universality of these
segmentation models are hindered. In this study, we propose an instructive
feature enhancement approach, namely IFE, to adaptively select feature channels
with rich texture cues and strong discriminability to enhance raw features
based on local curvature or global information entropy criteria. Being
plug-and-play and applicable for diverse DMIS tasks, IFE encourages the model
to focus on texture-rich features which are especially important for the
ambiguous and challenging boundary identification, simultaneously achieving
simplicity, universality, and certain interpretability. To evaluate the
proposed IFE, we constructed the first large-scale DMIS dataset Cosmos55k,
which contains 55,023 images from 7 modalities and 26 anatomical structures.
Extensive experiments show that IFE can improve the performance of classic
segmentation networks across different anatomies and modalities with only
slight modifications. Code is available at https://github.com/yezi-66/IFE
|
[
"cs.CV"
] | false |
2306.03508
|
2023-06-06T08:53:53Z
|
Semantic Segmentation on VSPW Dataset through Contrastive Loss and
Multi-dataset Training Approach
|
[
"Min Yan",
"Qianxiong Ning",
"Qian Wang"
] |
Video scene parsing incorporates temporal information, which can enhance the
consistency and accuracy of predictions compared to image scene parsing. The
added temporal dimension enables a more comprehensive understanding of the
scene, leading to more reliable results. This paper presents the winning
solution of the CVPR2023 workshop for video semantic segmentation, focusing on
enhancing Spatial-Temporal correlations with contrastive loss. We also explore
the influence of multi-dataset training by utilizing a label-mapping technique.
And the final result is aggregating the output of the above two models. Our
approach achieves 65.95% mIoU performance on the VSPW dataset, ranked 1st place
on the VSPW challenge at CVPR 2023.
|
[
"cs.CV"
] | false |
2306.03576
|
2023-06-06T10:51:05Z
|
Human 3D Avatar Modeling with Implicit Neural Representation: A Brief
Survey
|
[
"Mingyang Sun",
"Dingkang Yang",
"Dongliang Kou",
"Yang Jiang",
"Weihua Shan",
"Zhe Yan",
"Lihua Zhang"
] |
A human 3D avatar is one of the important elements in the metaverse, and the
modeling effect directly affects people's visual experience. However, the human
body has a complex topology and diverse details, so it is often expensive,
time-consuming, and laborious to build a satisfactory model. Recent studies
have proposed a novel method, implicit neural representation, which is a
continuous representation method and can describe objects with arbitrary
topology at arbitrary resolution. Researchers have applied implicit neural
representation to human 3D avatar modeling and obtained more excellent results
than traditional methods. This paper comprehensively reviews the application of
implicit neural representation in human body modeling. First, we introduce
three implicit representations of occupancy field, SDF, and NeRF, and make a
classification of the literature investigated in this paper. Then the
application of implicit modeling methods in the body, hand, and head are
compared and analyzed respectively. Finally, we point out the shortcomings of
current work and provide available suggestions for researchers.
|
[
"cs.CV"
] | false |
2306.03577
|
2023-06-06T10:52:06Z
|
An Open Patch Generator based Fingerprint Presentation Attack Detection
using Generative Adversarial Network
|
[
"Anuj Rai",
"Ashutosh Anshul",
"Ashwini Jha",
"Prayag Jain",
"Ramprakash Sharma",
"Somnath Dey"
] |
The low-cost, user-friendly, and convenient nature of Automatic Fingerprint
Recognition Systems (AFRS) makes them suitable for a wide range of
applications. This spreading use of AFRS also makes them vulnerable to various
security threats. Presentation Attack (PA) or spoofing is one of the threats
which is caused by presenting a spoof of a genuine fingerprint to the sensor of
AFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasure
intended to protect AFRS against fake or spoof fingerprints created using
various fabrication materials. In this paper, we have proposed a Convolutional
Neural Network (CNN) based technique that uses a Generative Adversarial Network
(GAN) to augment the dataset with spoof samples generated from the proposed
Open Patch Generator (OPG). This OPG is capable of generating realistic
fingerprint samples which have no resemblance to the existing spoof fingerprint
samples generated with other materials. The augmented dataset is fed to the
DenseNet classifier which helps in increasing the performance of the
Presentation Attack Detection (PAD) module for the various real-world attacks
possible with unknown spoof materials. Experimental evaluations of the proposed
approach are carried out on the Liveness Detection (LivDet) 2015, 2017, and
2019 competition databases. An overall accuracy of 96.20\%, 94.97\%, and
92.90\% has been achieved on the LivDet 2015, 2017, and 2019 databases,
respectively under the LivDet protocol scenarios. The performance of the
proposed PAD model is also validated in the cross-material and cross-sensor
attack paradigm which further exhibits its capability to be used under
real-world attack scenarios.
|
[
"cs.CV"
] | false |
2306.03594
|
2023-06-06T11:31:29Z
|
Emotional Talking Head Generation based on Memory-Sharing and
Attention-Augmented Networks
|
[
"Jianrong Wang",
"Yaxin Zhao",
"Li Liu",
"Tianyi Xu",
"Qi Li",
"Sen Li"
] |
Given an audio clip and a reference face image, the goal of the talking head
generation is to generate a high-fidelity talking head video. Although some
audio-driven methods of generating talking head videos have made some
achievements in the past, most of them only focused on lip and audio
synchronization and lack the ability to reproduce the facial expressions of the
target person. To this end, we propose a talking head generation model
consisting of a Memory-Sharing Emotion Feature extractor (MSEF) and an
Attention-Augmented Translator based on U-net (AATU). Firstly, MSEF can extract
implicit emotional auxiliary features from audio to estimate more accurate
emotional face landmarks.~Secondly, AATU acts as a translator between the
estimated landmarks and the photo-realistic video frames. Extensive qualitative
and quantitative experiments have shown the superiority of the proposed method
to the previous works. Codes will be made publicly available.
|
[
"cs.CV"
] | false |
2306.03597
|
2023-06-06T11:36:14Z
|
Human-Object Interaction Prediction in Videos through Gaze Following
|
[
"Zhifan Ni",
"Esteve Valls Mascaró",
"Hyemin Ahn",
"Dongheui Lee"
] |
Understanding the human-object interactions (HOIs) from a video is essential
to fully comprehend a visual scene. This line of research has been addressed by
detecting HOIs from images and lately from videos. However, the video-based HOI
anticipation task in the third-person view remains understudied. In this paper,
we design a framework to detect current HOIs and anticipate future HOIs in
videos. We propose to leverage human gaze information since people often fixate
on an object before interacting with it. These gaze features together with the
scene contexts and the visual appearances of human-object pairs are fused
through a spatio-temporal transformer. To evaluate the model in the HOI
anticipation task in a multi-person scenario, we propose a set of person-wise
multi-label metrics. Our model is trained and validated on the VidHOI dataset,
which contains videos capturing daily life and is currently the largest video
HOI dataset. Experimental results in the HOI detection task show that our
approach improves the baseline by a great margin of 36.3% relatively. Moreover,
we conduct an extensive ablation study to demonstrate the effectiveness of our
modifications and extensions to the spatio-temporal transformer. Our code is
publicly available on https://github.com/nizhf/hoi-prediction-gaze-transformer.
|
[
"cs.CV"
] | false |
2306.03630
|
2023-06-06T12:36:57Z
|
Mutual Information Regularization for Weakly-supervised RGB-D Salient
Object Detection
|
[
"Aixuan Li",
"Yuxin Mao",
"Jing Zhang",
"Yuchao Dai"
] |
In this paper, we present a weakly-supervised RGB-D salient object detection
model via scribble supervision. Specifically, as a multimodal learning task, we
focus on effective multimodal representation learning via inter-modal mutual
information regularization. In particular, following the principle of
disentangled representation learning, we introduce a mutual information upper
bound with a mutual information minimization regularizer to encourage the
disentangled representation of each modality for salient object detection.
Based on our multimodal representation learning framework, we introduce an
asymmetric feature extractor for our multimodal data, which is proven more
effective than the conventional symmetric backbone setting. We also introduce
multimodal variational auto-encoder as stochastic prediction refinement
techniques, which takes pseudo labels from the first training stage as
supervision and generates refined prediction. Experimental results on benchmark
RGB-D salient object detection datasets verify both effectiveness of our
explicit multimodal disentangled representation learning method and the
stochastic prediction refinement strategy, achieving comparable performance
with the state-of-the-art fully supervised models. Our code and data are
available at: https://github.com/baneitixiaomai/MIRV.
|
[
"cs.CV"
] | false |
2306.03711
|
2023-06-06T14:21:22Z
|
Deep Learning-Enabled Sleep Staging From Vital Signs and Activity
Measured Using a Near-Infrared Video Camera
|
[
"Jonathan Carter",
"João Jorge",
"Bindia Venugopal",
"Oliver Gibson",
"Lionel Tarassenko"
] |
Conventional sleep monitoring is time-consuming, expensive and uncomfortable,
requiring a large number of contact sensors to be attached to the patient.
Video data is commonly recorded as part of a sleep laboratory assessment. If
accurate sleep staging could be achieved solely from video, this would overcome
many of the problems of traditional methods. In this work we use heart rate,
breathing rate and activity measures, all derived from a near-infrared video
camera, to perform sleep stage classification. We use a deep transfer learning
approach to overcome data scarcity, by using an existing contact-sensor dataset
to learn effective representations from the heart and breathing rate time
series. Using a dataset of 50 healthy volunteers, we achieve an accuracy of
73.4\% and a Cohen's kappa of 0.61 in four-class sleep stage classification,
establishing a new state-of-the-art for video-based sleep staging.
|
[
"cs.CV"
] | false |
2306.03747
|
2023-06-06T15:07:39Z
|
Towards Scalable Multi-View Reconstruction of Geometry and Materials
|
[
"Carolin Schmitt",
"Božidar Antić",
"Andrei Neculai",
"Joo Ho Lee",
"Andreas Geiger"
] |
In this paper, we propose a novel method for joint recovery of camera pose,
object geometry and spatially-varying Bidirectional Reflectance Distribution
Function (svBRDF) of 3D scenes that exceed object-scale and hence cannot be
captured with stationary light stages. The input are high-resolution RGB-D
images captured by a mobile, hand-held capture system with point lights for
active illumination. Compared to previous works that jointly estimate geometry
and materials from a hand-held scanner, we formulate this problem using a
single objective function that can be minimized using off-the-shelf
gradient-based solvers. To facilitate scalability to large numbers of
observation views and optimization variables, we introduce a distributed
optimization algorithm that reconstructs 2.5D keyframe-based representations of
the scene. A novel multi-view consistency regularizer effectively synchronizes
neighboring keyframes such that the local optimization results allow for
seamless integration into a globally consistent 3D model. We provide a study on
the importance of each component in our formulation and show that our method
compares favorably to baselines. We further demonstrate that our method
accurately reconstructs various objects and materials and allows for expansion
to spatially larger scenes. We believe that this work represents a significant
step towards making geometry and material estimation from hand-held scanners
scalable.
|
[
"cs.CV"
] | false |
2306.03847
|
2023-06-06T16:35:45Z
|
Learning Human Mesh Recovery in 3D Scenes
|
[
"Zehong Shen",
"Zhi Cen",
"Sida Peng",
"Qing Shuai",
"Hujun Bao",
"Xiaowei Zhou"
] |
We present a novel method for recovering the absolute pose and shape of a
human in a pre-scanned scene given a single image. Unlike previous methods that
perform sceneaware mesh optimization, we propose to first estimate absolute
position and dense scene contacts with a sparse 3D CNN, and later enhance a
pretrained human mesh recovery network by cross-attention with the derived 3D
scene cues. Joint learning on images and scene geometry enables our method to
reduce the ambiguity caused by depth and occlusion, resulting in more
reasonable global postures and contacts. Encoding scene-aware cues in the
network also allows the proposed method to be optimization-free, and opens up
the opportunity for real-time applications. The experiments show that the
proposed network is capable of recovering accurate and physically-plausible
meshes by a single forward pass and outperforms state-of-the-art methods in
terms of both accuracy and speed.
|
[
"cs.CV"
] | false |
2306.03908
|
2023-06-06T17:59:51Z
|
SAM3D: Segment Anything in 3D Scenes
|
[
"Yunhan Yang",
"Xiaoyang Wu",
"Tong He",
"Hengshuang Zhao",
"Xihui Liu"
] |
In this work, we propose SAM3D, a novel framework that is able to predict
masks in 3D point clouds by leveraging the Segment-Anything Model (SAM) in RGB
images without further training or finetuning. For a point cloud of a 3D scene
with posed RGB images, we first predict segmentation masks of RGB images with
SAM, and then project the 2D masks into the 3D points. Later, we merge the 3D
masks iteratively with a bottom-up merging approach. At each step, we merge the
point cloud masks of two adjacent frames with the bidirectional merging
approach. In this way, the 3D masks predicted from different frames are
gradually merged into the 3D masks of the whole 3D scene. Finally, we can
optionally ensemble the result from our SAM3D with the over-segmentation
results based on the geometric information of the 3D scenes. Our approach is
experimented with ScanNet dataset and qualitative results demonstrate that our
SAM3D achieves reasonable and fine-grained 3D segmentation results without any
training or finetuning of SAM.
|
[
"cs.CV"
] | false |
2306.03932
|
2023-06-06T18:00:47Z
|
Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA
Tasks? A: Self-Train on Unlabeled Images!
|
[
"Zaid Khan",
"Vijay Kumar BG",
"Samuel Schulter",
"Xiang Yu",
"Yun Fu",
"Manmohan Chandraker"
] |
Finetuning a large vision language model (VLM) on a target dataset after
large scale pretraining is a dominant paradigm in visual question answering
(VQA). Datasets for specialized tasks such as knowledge-based VQA or VQA in non
natural-image domains are orders of magnitude smaller than those for
general-purpose VQA. While collecting additional labels for specialized tasks
or domains can be challenging, unlabeled images are often available. We
introduce SelTDA (Self-Taught Data Augmentation), a strategy for finetuning
large VLMs on small-scale VQA datasets. SelTDA uses the VLM and target dataset
to build a teacher model that can generate question-answer pseudolabels
directly conditioned on an image alone, allowing us to pseudolabel unlabeled
images. SelTDA then finetunes the initial VLM on the original dataset augmented
with freshly pseudolabeled images. We describe a series of experiments showing
that our self-taught data augmentation increases robustness to adversarially
searched questions, counterfactual examples and rephrasings, improves domain
generalization, and results in greater retention of numerical reasoning skills.
The proposed strategy requires no additional annotations or architectural
modifications, and is compatible with any modern encoder-decoder multimodal
transformer. Code available at https://github.com/codezakh/SelTDA.
|
[
"cs.CV"
] | false |
2306.04000
|
2023-06-06T20:28:04Z
|
A Quality Aware Sample-to-Sample Comparison for Face Recognition
|
[
"Mohammad Saeed Ebrahimi Saadabadi",
"Sahar Rahimi Malakshan",
"Ali Zafari",
"Moktari Mostofa",
"Nasser M. Nasrabadi"
] |
Currently available face datasets mainly consist of a large number of
high-quality and a small number of low-quality samples. As a result, a Face
Recognition (FR) network fails to learn the distribution of low-quality samples
since they are less frequent during training (underrepresented). Moreover,
current state-of-the-art FR training paradigms are based on the
sample-to-center comparison (i.e., Softmax-based classifier), which results in
a lack of uniformity between train and test metrics. This work integrates a
quality-aware learning process at the sample level into the classification
training paradigm (QAFace). In this regard, Softmax centers are adaptively
guided to pay more attention to low-quality samples by using a quality-aware
function. Accordingly, QAFace adds a quality-based adjustment to the updating
procedure of the Softmax-based classifier to improve the performance on the
underrepresented low-quality samples. Our method adaptively finds and assigns
more attention to the recognizable low-quality samples in the training
datasets. In addition, QAFace ignores the unrecognizable low-quality samples
using the feature magnitude as a proxy for quality. As a result, QAFace
prevents class centers from getting distracted from the optimal direction. The
proposed method is superior to the state-of-the-art algorithms in extensive
experimental results on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, IJB-B, and IJB-C
datasets.
|
[
"cs.CV"
] | false |
2306.04451
|
2023-06-06T07:58:59Z
|
Referring Expression Comprehension Using Language Adaptive Inference
|
[
"Wei Su",
"Peihan Miao",
"Huanzhang Dou",
"Yongjian Fu",
"Xi Li"
] |
Different from universal object detection, referring expression comprehension
(REC) aims to locate specific objects referred to by natural language
expressions. The expression provides high-level concepts of relevant visual and
contextual patterns, which vary significantly with different expressions and
account for only a few of those encoded in the REC model. This leads us to a
question: do we really need the entire network with a fixed structure for
various referring expressions? Ideally, given an expression, only
expression-relevant components of the REC model are required. These components
should be small in number as each expression only contains very few visual and
contextual clues. This paper explores the adaptation between expressions and
REC models for dynamic inference. Concretely, we propose a neat yet efficient
framework named Language Adaptive Dynamic Subnets (LADS), which can extract
language-adaptive subnets from the REC model conditioned on the referring
expressions. By using the compact subnet, the inference can be more economical
and efficient. Extensive experiments on RefCOCO, RefCOCO+, RefCOCOg, and
Referit show that the proposed method achieves faster inference speed and
higher accuracy against state-of-the-art approaches.
|
[
"cs.CV"
] | false |
2306.04650
|
2023-06-06T07:24:53Z
|
GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning
|
[
"Huanzhang Dou",
"Pengyi Zhang",
"Yuhan Zhao",
"Lin Dong",
"Zequn Qin",
"Xi Li"
] |
Gait recognition aims at identifying the pedestrians at a long distance by
their biometric gait patterns. It is inherently challenging due to the various
covariates and the properties of silhouettes (textureless and colorless), which
result in two kinds of pair-wise hard samples: the same pedestrian could have
distinct silhouettes (intra-class diversity) and different pedestrians could
have similar silhouettes (inter-class similarity). In this work, we propose to
solve the hard sample issue with a Memory-augmented Progressive Learning
network (GaitMPL), including Dynamic Reweighting Progressive Learning module
(DRPL) and Global Structure-Aligned Memory bank (GSAM). Specifically, DRPL
reduces the learning difficulty of hard samples by easy-to-hard progressive
learning. GSAM further augments DRPL with a structure-aligned memory mechanism,
which maintains and models the feature distribution of each ID. Experiments on
two commonly used datasets, CASIA-B and OU-MVLP, demonstrate the effectiveness
of GaitMPL. On CASIA-B, we achieve the state-of-the-art performance, i.e.,
88.0% on the most challenging condition (Clothing) and 93.3% on the average
condition, which outperforms the other methods by at least 3.8% and 1.4%,
respectively.
|
[
"cs.CV"
] | false |
2306.04652
|
2023-06-06T08:26:22Z
|
Language Adaptive Weight Generation for Multi-task Visual Grounding
|
[
"Wei Su",
"Peihan Miao",
"Huanzhang Dou",
"Gaoang Wang",
"Liang Qiao",
"Zheyang Li",
"Xi Li"
] |
Although the impressive performance in visual grounding, the prevailing
approaches usually exploit the visual backbone in a passive way, i.e., the
visual backbone extracts features with fixed weights without expression-related
hints. The passive perception may lead to mismatches (e.g., redundant and
missing), limiting further performance improvement. Ideally, the visual
backbone should actively extract visual features since the expressions already
provide the blueprint of desired visual features. The active perception can
take expressions as priors to extract relevant visual features, which can
effectively alleviate the mismatches. Inspired by this, we propose an active
perception Visual Grounding framework based on Language Adaptive Weights,
called VG-LAW. The visual backbone serves as an expression-specific feature
extractor through dynamic weights generated for various expressions. Benefiting
from the specific and relevant visual features extracted from the
language-aware visual backbone, VG-LAW does not require additional modules for
cross-modal interaction. Along with a neat multi-task head, VG-LAW can be
competent in referring expression comprehension and segmentation jointly.
Extensive experiments on four representative datasets, i.e., RefCOCO, RefCOCO+,
RefCOCOg, and ReferItGame, validate the effectiveness of the proposed framework
and demonstrate state-of-the-art performance.
|
[
"cs.CV"
] | false |
2306.04654
|
2023-06-06T15:04:45Z
|
DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based
Point-Level Consistency
|
[
"Yike Yuan",
"Xinghe Fu",
"Yunlong Yu",
"Xi Li"
] |
In this paper, we propose a simple yet effective transformer framework for
self-supervised learning called DenseDINO to learn dense visual
representations. To exploit the spatial information that the dense prediction
tasks require but neglected by the existing self-supervised transformers, we
introduce point-level supervision across views in a novel token-based way.
Specifically, DenseDINO introduces some extra input tokens called reference
tokens to match the point-level features with the position prior. With the
reference token, the model could maintain spatial consistency and deal with
multi-object complex scene images, thus generalizing better on dense prediction
tasks. Compared with the vanilla DINO, our approach obtains competitive
performance when evaluated on classification in ImageNet and achieves a large
margin (+7.2% mIoU) improvement in semantic segmentation on PascalVOC under the
linear probing protocol for segmentation.
|
[
"cs.CV"
] | false |
2306.03331
|
2023-06-06T01:02:31Z
|
A Robust Likelihood Model for Novelty Detection
|
[
"Ranya Almohsen",
"Shivang Patel",
"Donald A. Adjeroh",
"Gianfranco Doretto"
] |
Current approaches to novelty or anomaly detection are based on deep neural
networks. Despite their effectiveness, neural networks are also vulnerable to
imperceptible deformations of the input data. This is a serious issue in
critical applications, or when data alterations are generated by an adversarial
attack. While this is a known problem that has been studied in recent years for
the case of supervised learning, the case of novelty detection has received
very limited attention. Indeed, in this latter setting the learning is
typically unsupervised because outlier data is not available during training,
and new approaches for this case need to be investigated. We propose a new
prior that aims at learning a robust likelihood for the novelty test, as a
defense against attacks. We also integrate the same prior with a
state-of-the-art novelty detection approach. Because of the geometric
properties of that approach, the resulting robust training is computationally
very efficient. An initial evaluation of the method indicates that it is
effective at improving performance with respect to the standard models in the
absence and presence of attacks.
|
[
"cs.CV",
"cs.LG"
] | false |
2306.03375
|
2023-06-06T03:29:47Z
|
Identifying Shared Decodable Concepts in the Human Brain Using
Image-Language Foundation Models
|
[
"Cory Efird",
"Alex Murphy",
"Joel Zylberberg",
"Alona Fyshe"
] |
We introduce a method that takes advantage of high-quality pretrained
multimodal representations to explore fine-grained semantic networks in the
human brain. Previous studies have documented evidence of functional
localization in the brain, with different anatomical regions preferentially
activating for different types of sensory input. Many such localized structures
are known, including the fusiform face area and parahippocampal place area.
This raises the question of whether additional brain regions (or conjunctions
of brain regions) are also specialized for other important semantic concepts.
To identify such brain regions, we developed a data-driven approach to uncover
visual concepts that are decodable from a massive functional magnetic resonance
imaging (fMRI) dataset. Our analysis is broadly split into three sections.
First, a fully connected neural network is trained to map brain responses to
the outputs of an image-language foundation model, CLIP (Radford et al., 2021).
Subsequently, a contrastive-learning dimensionality reduction method reveals
the brain-decodable components of CLIP space. In the final section of our
analysis, we localize shared decodable concepts in the brain using a
voxel-masking optimization method to produce a shared decodable concept (SDC)
space. The accuracy of our procedure is validated by comparing it to previous
localization experiments that identify regions for faces, bodies, and places.
In addition to these concepts, whose corresponding brain regions were already
known, we localize novel concept representations which are shared across
participants to other areas of the human brain. We also demonstrate how this
method can be used to inspect fine-grained semantic networks for individual
participants. We envisage that this extensible method can also be adapted to
explore other questions at the intersection of AI and neuroscience.
|
[
"cs.AI",
"cs.CV"
] | false |
2306.03445
|
2023-06-06T06:53:05Z
|
MetaGait: Learning to Learn an Omni Sample Adaptive Representation for
Gait Recognition
|
[
"Huanzhang Dou",
"Pengyi Zhang",
"Wei Su",
"Yunlong Yu",
"Xi Li"
] |
Gait recognition, which aims at identifying individuals by their walking
patterns, has recently drawn increasing research attention. However, gait
recognition still suffers from the conflicts between the limited binary visual
clues of the silhouette and numerous covariates with diverse scales, which
brings challenges to the model's adaptiveness. In this paper, we address this
conflict by developing a novel MetaGait that learns to learn an omni sample
adaptive representation. Towards this goal, MetaGait injects meta-knowledge,
which could guide the model to perceive sample-specific properties, into the
calibration network of the attention mechanism to improve the adaptiveness from
the omni-scale, omni-dimension, and omni-process perspectives. Specifically, we
leverage the meta-knowledge across the entire process, where Meta Triple
Attention and Meta Temporal Pooling are presented respectively to adaptively
capture omni-scale dependency from spatial/channel/temporal dimensions
simultaneously and to adaptively aggregate temporal information through
integrating the merits of three complementary temporal aggregation methods.
Extensive experiments demonstrate the state-of-the-art performance of the
proposed MetaGait. On CASIA-B, we achieve rank-1 accuracy of 98.7%, 96.0%, and
89.3% under three conditions, respectively. On OU-MVLP, we achieve rank-1
accuracy of 92.4%.
|
[
"cs.CV",
"cs.AI"
] | false |
2306.03476
|
2023-06-06T07:50:46Z
|
Putting Humans in the Image Captioning Loop
|
[
"Aliki Anagnostopoulou",
"Mareike Hartmann",
"Daniel Sonntag"
] |
Image Captioning (IC) models can highly benefit from human feedback in the
training process, especially in cases where data is limited. We present
work-in-progress on adapting an IC system to integrate human feedback, with the
goal to make it easily adaptable to user-specific data. Our approach builds on
a base IC model pre-trained on the MS COCO dataset, which generates captions
for unseen images. The user will then be able to offer feedback on the image
and the generated/predicted caption, which will be augmented to create
additional training instances for the adaptation of the model. The additional
instances are integrated into the model using step-wise updates, and a sparse
memory replay component is used to avoid catastrophic forgetting. We hope that
this approach, while leading to improved results, will also result in
customizable IC models.
|
[
"cs.CL",
"cs.CV"
] | false |
2306.03491
|
2023-06-06T08:16:16Z
|
SciCap+: A Knowledge Augmented Dataset to Study the Challenges of
Scientific Figure Captioning
|
[
"Zhishen Yang",
"Raj Dabre",
"Hideki Tanaka",
"Naoaki Okazaki"
] |
In scholarly documents, figures provide a straightforward way of
communicating scientific findings to readers. Automating figure caption
generation helps move model understandings of scientific documents beyond text
and will help authors write informative captions that facilitate communicating
scientific findings. Unlike previous studies, we reframe scientific figure
captioning as a knowledge-augmented image captioning task that models need to
utilize knowledge embedded across modalities for caption generation. To this
end, we extended the large-scale SciCap
dataset~\cite{hsu-etal-2021-scicap-generating} to SciCap+ which includes
mention-paragraphs (paragraphs mentioning figures) and OCR tokens. Then, we
conduct experiments with the M4C-Captioner (a multimodal transformer-based
model with a pointer network) as a baseline for our study. Our results indicate
that mention-paragraphs serves as additional context knowledge, which
significantly boosts the automatic standard image caption evaluation scores
compared to the figure-only baselines. Human evaluations further reveal the
challenges of generating figure captions that are informative to readers. The
code and SciCap+ dataset will be publicly available at
https://github.com/ZhishenYang/scientific_figure_captioning_dataset
|
[
"cs.CV",
"cs.CL"
] | false |
2306.03494
|
2023-06-06T08:22:47Z
|
LegoNet: Alternating Model Blocks for Medical Image Segmentation
|
[
"Ikboljon Sobirov",
"Cheng Xie",
"Muhammad Siddique",
"Parijat Patel",
"Kenneth Chan",
"Thomas Halborg",
"Christos Kotanidis",
"Zarqiash Fatima",
"Henry West",
"Keith Channon",
"Stefan Neubauer",
"Charalambos Antoniades",
"Mohammad Yaqub"
] |
Since the emergence of convolutional neural networks (CNNs), and later vision
transformers (ViTs), the common paradigm for model development has always been
using a set of identical block types with varying parameters/hyper-parameters.
To leverage the benefits of different architectural designs (e.g. CNNs and
ViTs), we propose to alternate structurally different types of blocks to
generate a new architecture, mimicking how Lego blocks can be assembled
together. Using two CNN-based and one SwinViT-based blocks, we investigate
three variations to the so-called LegoNet that applies the new concept of block
alternation for the segmentation task in medical imaging. We also study a new
clinical problem which has not been investigated before, namely the right
internal mammary artery (RIMA) and perivascular space segmentation from
computed tomography angiography (CTA) which has demonstrated a prognostic value
to major cardiovascular outcomes. We compare the model performance against
popular CNN and ViT architectures using two large datasets (e.g. achieving
0.749 dice similarity coefficient (DSC) on the larger dataset). We evaluate the
performance of the model on three external testing cohorts as well, where an
expert clinician made corrections to the model segmented results (DSC>0.90 for
the three cohorts). To assess our proposed model for suitability in clinical
use, we perform intra- and inter-observer variability analysis. Finally, we
investigate a joint self-supervised learning approach to assess its impact on
model performance. The code and the pretrained model weights will be available
upon acceptance.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.03500
|
2023-06-06T08:38:10Z
|
Towards Adaptable and Interactive Image Captioning with Data
Augmentation and Episodic Memory
|
[
"Aliki Anagnostopoulou",
"Mareike Hartmann",
"Daniel Sonntag"
] |
Interactive machine learning (IML) is a beneficial learning paradigm in cases
of limited data availability, as human feedback is incrementally integrated
into the training process. In this paper, we present an IML pipeline for image
captioning which allows us to incrementally adapt a pre-trained image
captioning model to a new data distribution based on user input. In order to
incorporate user input into the model, we explore the use of a combination of
simple data augmentation methods to obtain larger data batches for each newly
annotated data instance and implement continual learning methods to prevent
catastrophic forgetting from repeated updates. For our experiments, we split a
domain-specific image captioning dataset, namely VizWiz, into non-overlapping
parts to simulate an incremental input flow for continually adapting the model
to new data. We find that, while data augmentation worsens results, even when
relatively small amounts of data are available, episodic memory is an effective
strategy to retain knowledge from previously seen clusters.
|
[
"cs.CL",
"cs.CV"
] | false |
2306.03511
|
2023-06-06T08:56:58Z
|
Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical
Image Segmentation
|
[
"An Wang",
"Mobarakol Islam",
"Mengya Xu",
"Hongliang Ren"
] |
Accurate and robust medical image segmentation is fundamental and crucial for
enhancing the autonomy of computer-aided diagnosis and intervention systems.
Medical data collection normally involves different scanners, protocols, and
populations, making domain adaptation (DA) a highly demanding research field to
alleviate model degradation in the deployment site. To preserve the model
performance across multiple testing domains, this work proposes the
Curriculum-based Augmented Fourier Domain Adaptation (Curri-AFDA) for robust
medical image segmentation. In particular, our curriculum learning strategy is
based on the causal relationship of a model under different levels of data
shift in the deployment phase, where the higher the shift is, the harder to
recognize the variance. Considering this, we progressively introduce more
amplitude information from the target domain to the source domain in the
frequency space during the curriculum-style training to smoothly schedule the
semantic knowledge transfer in an easier-to-harder manner. Besides, we
incorporate the training-time chained augmentation mixing to help expand the
data distributions while preserving the domain-invariant semantics, which is
beneficial for the acquired model to be more robust and generalize better to
unseen domains. Extensive experiments on two segmentation tasks of Retina and
Nuclei collected from multiple sites and scanners suggest that our proposed
method yields superior adaptation and generalization performance. Meanwhile,
our approach proves to be more robust under various corruption types and
increasing severity levels. In addition, we show our method is also beneficial
in the domain-adaptive classification task with skin lesion datasets. The code
is available at https://github.com/lofrienger/Curri-AFDA.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.03537
|
2023-06-06T09:35:45Z
|
Real-Time Onboard Object Detection for Augmented Reality: Enhancing
Head-Mounted Display with YOLOv8
|
[
"Mikołaj Łysakowski",
"Kamil Żywanowski",
"Adam Banaszczyk",
"Michał R. Nowicki",
"Piotr Skrzypczyński",
"Sławomir K. Tadeja"
] |
This paper introduces a software architecture for real-time object detection
using machine learning (ML) in an augmented reality (AR) environment. Our
approach uses the recent state-of-the-art YOLOv8 network that runs onboard on
the Microsoft HoloLens 2 head-mounted display (HMD). The primary motivation
behind this research is to enable the application of advanced ML models for
enhanced perception and situational awareness with a wearable, hands-free AR
platform. We show the image processing pipeline for the YOLOv8 model and the
techniques used to make it real-time on the resource-limited edge computing
platform of the headset. The experimental results demonstrate that our solution
achieves real-time processing without needing offloading tasks to the cloud or
any other external servers while retaining satisfactory accuracy regarding the
usual mAP metric and measured qualitative performance
|
[
"cs.CV",
"cs.HC"
] | false |
2306.03641
|
2023-06-06T12:52:07Z
|
Single-Shot Global Localization via Graph-Theoretic Correspondence
Matching
|
[
"Shigemichi Matsuzaki",
"Kenji Koide",
"Shuji Oishi",
"Masashi Yokozuka",
"Atsuhiko Banno"
] |
This paper describes a method of global localization based on graph-theoretic
association of instances between a query and the prior map. The proposed
framework employs correspondence matching based on the maximum clique problem
(MCP). The framework is potentially applicable to other map and/or query
modalities thanks to the graph-based abstraction of the problem, while many of
existing global localization methods rely on a query and the dataset in the
same modality. We implement it with a semantically labeled 3D point cloud map,
and a semantic segmentation image as a query. Leveraging the graph-theoretic
framework, the proposed method realizes global localization exploiting only the
map and the query. The method shows promising results on multiple large-scale
simulated maps of urban scenes.
|
[
"cs.RO",
"cs.CV"
] | false |
2306.03648
|
2023-06-06T13:04:05Z
|
Supervised Knowledge May Hurt Novel Class Discovery Performance
|
[
"Ziyun Li",
"Jona Otholt",
"Ben Dai",
"Di Hu",
"Christoph Meinel",
"Haojin Yang"
] |
Novel class discovery (NCD) aims to infer novel categories in an unlabeled
dataset by leveraging prior knowledge of a labeled set comprising disjoint but
related classes. Given that most existing literature focuses primarily on
utilizing supervised knowledge from a labeled set at the methodology level,
this paper considers the question: Is supervised knowledge always helpful at
different levels of semantic relevance? To proceed, we first establish a novel
metric, so-called transfer flow, to measure the semantic similarity between
labeled/unlabeled datasets. To show the validity of the proposed metric, we
build up a large-scale benchmark with various degrees of semantic similarities
between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical
class structure. The results based on the proposed benchmark show that the
proposed transfer flow is in line with the hierarchical class structure; and
that NCD performance is consistent with the semantic similarities (measured by
the proposed metric). Next, by using the proposed transfer flow, we conduct
various empirical experiments with different levels of semantic similarity,
yielding that supervised knowledge may hurt NCD performance. Specifically,
using supervised information from a low-similarity labeled set may lead to a
suboptimal result as compared to using pure self-supervised knowledge. These
results reveal the inadequacy of the existing NCD literature which usually
assumes that supervised knowledge is beneficial. Finally, we develop a
pseudo-version of the transfer flow as a practical reference to decide if
supervised knowledge should be used in NCD. Its effectiveness is supported by
our empirical studies, which show that the pseudo transfer flow (with or
without supervised knowledge) is consistent with the corresponding accuracy
based on various datasets. Code is released at
https://github.com/J-L-O/SK-Hurt-NCD
|
[
"cs.LG",
"cs.CV"
] | false |
2306.03660
|
2023-06-06T13:23:42Z
|
PQM: A Point Quality Evaluation Metric for Dense Maps
|
[
"Yash Turkar",
"Pranay Meshram",
"Charuvahan Adhivarahan",
"Karthik Dantu"
] |
LiDAR-based mapping/reconstruction are important for various applications,
but evaluating the quality of the dense maps they produce is challenging. The
current methods have limitations, including the inability to capture
completeness, structural information, and local variations in error. In this
paper, we propose a novel point quality evaluation metric (PQM) that consists
of four sub-metrics to provide a more comprehensive evaluation of point cloud
quality. The completeness sub-metric evaluates the proportion of missing data,
the artifact score sub-metric recognizes and characterizes artifacts, the
accuracy sub-metric measures registration accuracy, and the resolution
sub-metric quantifies point cloud density. Through an ablation study using a
prototype dataset, we demonstrate the effectiveness of each of the sub-metrics
and compare them to popular point cloud distance measures. Using three LiDAR
SLAM systems to generate maps, we evaluate their output map quality and
demonstrate the metrics robustness to noise and artifacts. Our implementation
of PQM, datasets and detailed documentation on how to integrate with your
custom dense mapping pipeline can be found at github.com/droneslab/pqm
|
[
"cs.CV",
"eess.IV"
] | false |
2306.03730
|
2023-06-06T14:48:50Z
|
Modality-Agnostic Learning for Medical Image Segmentation Using
Multi-modality Self-distillation
|
[
"Qisheng He",
"Nicholas Summerfield",
"Ming Dong",
"Carri Glide-Hurst"
] |
Medical image segmentation of tumors and organs at risk is a time-consuming
yet critical process in the clinic that utilizes multi-modality imaging (e.g,
different acquisitions, data types, and sequences) to increase segmentation
precision. In this paper, we propose a novel framework, Modality-Agnostic
learning through Multi-modality Self-dist-illation (MAG-MS), to investigate the
impact of input modalities on medical image segmentation. MAG-MS distills
knowledge from the fusion of multiple modalities and applies it to enhance
representation learning for individual modalities. Thus, it provides a
versatile and efficient approach to handle limited modalities during testing.
Our extensive experiments on benchmark datasets demonstrate the high efficiency
of MAG-MS and its superior segmentation performance than current
state-of-the-art methods. Furthermore, using MAG-MS, we provide valuable
insight and guidance on selecting input modalities for medical image
segmentation tasks.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.03779
|
2023-06-06T15:34:45Z
|
Performance-optimized deep neural networks are evolving into worse
models of inferotemporal visual cortex
|
[
"Drew Linsley",
"Ivan F. Rodriguez",
"Thomas Fel",
"Michael Arcaro",
"Saloni Sharma",
"Margaret Livingstone",
"Thomas Serre"
] |
One of the most impactful findings in computational neuroscience over the
past decade is that the object recognition accuracy of deep neural networks
(DNNs) correlates with their ability to predict neural responses to natural
images in the inferotemporal (IT) cortex. This discovery supported the
long-held theory that object recognition is a core objective of the visual
cortex, and suggested that more accurate DNNs would serve as better models of
IT neuron responses to images. Since then, deep learning has undergone a
revolution of scale: billion parameter-scale DNNs trained on billions of images
are rivaling or outperforming humans at visual tasks including object
recognition. Have today's DNNs become more accurate at predicting IT neuron
responses to images as they have grown more accurate at object recognition?
Surprisingly, across three independent experiments, we find this is not the
case. DNNs have become progressively worse models of IT as their accuracy has
increased on ImageNet. To understand why DNNs experience this trade-off and
evaluate if they are still an appropriate paradigm for modeling the visual
system, we turn to recordings of IT that capture spatially resolved maps of
neuronal activity elicited by natural images. These neuronal activity maps
reveal that DNNs trained on ImageNet learn to rely on different visual features
than those encoded by IT and that this problem worsens as their accuracy
increases. We successfully resolved this issue with the neural harmonizer, a
plug-and-play training routine for DNNs that aligns their learned
representations with humans. Our results suggest that harmonized DNNs break the
trade-off between ImageNet accuracy and neural prediction accuracy that assails
current DNNs and offer a path to more accurate models of biological vision.
|
[
"cs.CV",
"cs.AI"
] | false |
2306.03802
|
2023-06-06T15:45:53Z
|
Learning to Ground Instructional Articles in Videos through Narrations
|
[
"Effrosyni Mavroudi",
"Triantafyllos Afouras",
"Lorenzo Torresani"
] |
In this paper we present an approach for localizing steps of procedural
activities in narrated how-to videos. To deal with the scarcity of labeled data
at scale, we source the step descriptions from a language knowledge base
(wikiHow) containing instructional articles for a large variety of procedural
tasks. Without any form of manual supervision, our model learns to temporally
ground the steps of procedural articles in how-to videos by matching three
modalities: frames, narrations, and step descriptions. Specifically, our method
aligns steps to video by fusing information from two distinct pathways: i) {\em
direct} alignment of step descriptions to frames, ii) {\em indirect} alignment
obtained by composing steps-to-narrations with narrations-to-video
correspondences. Notably, our approach performs global temporal grounding of
all steps in an article at once by exploiting order information, and is trained
with step pseudo-labels which are iteratively refined and aggressively
filtered. In order to validate our model we introduce a new evaluation
benchmark -- HT-Step -- obtained by manually annotating a 124-hour subset of
HowTo100M\footnote{A test server is accessible at
\url{https://eval.ai/web/challenges/challenge-page/2082}.} with steps sourced
from wikiHow articles. Experiments on this benchmark as well as zero-shot
evaluations on CrossTask demonstrate that our multi-modality alignment yields
dramatic gains over several baselines and prior works. Finally, we show that
our inner module for matching narration-to-video outperforms by a large margin
the state of the art on the HTM-Align narration-video alignment benchmark.
|
[
"cs.CV",
"cs.AI"
] | true |
2306.03810
|
2023-06-06T15:52:55Z
|
X-Align++: cross-modal cross-view alignment for Bird's-eye-view
segmentation
|
[
"Shubhankar Borse",
"Senthil Yogamani",
"Marvin Klingner",
"Varun Ravi",
"Hong Cai",
"Abdulaziz Almuzairee",
"Fatih Porikli"
] |
Bird's-eye-view (BEV) grid is a typical representation of the perception of
road components, e.g., drivable area, in autonomous driving. Most existing
approaches rely on cameras only to perform segmentation in BEV space, which is
fundamentally constrained by the absence of reliable depth information. The
latest works leverage both camera and LiDAR modalities but suboptimally fuse
their features using simple, concatenation-based mechanisms. In this paper, we
address these problems by enhancing the alignment of the unimodal features in
order to aid feature fusion, as well as enhancing the alignment between the
cameras' perspective view (PV) and BEV representations. We propose X-Align, a
novel end-to-end cross-modal and cross-view learning framework for BEV
segmentation consisting of the following components: (i) a novel Cross-Modal
Feature Alignment (X-FA) loss, (ii) an attention-based Cross-Modal Feature
Fusion (X-FF) module to align multi-modal BEV features implicitly, and (iii) an
auxiliary PV segmentation branch with Cross-View Segmentation Alignment (X-SA)
losses to improve the PV-to-BEV transformation. We evaluate our proposed method
across two commonly used benchmark datasets, i.e., nuScenes and KITTI-360.
Notably, X-Align significantly outperforms the state-of-the-art by 3 absolute
mIoU points on nuScenes. We also provide extensive ablation studies to
demonstrate the effectiveness of the individual components.
|
[
"cs.CV",
"cs.RO"
] | false |
2306.03983
|
2023-06-06T19:36:11Z
|
Unsupervised Iterative U-Net with an Internal Guidance Layer for
Vertebrae Contrast Enhancement in Chest X-Ray Images
|
[
"Ella Eidlin",
"Assaf Hoogi",
"Nathan S. Netanyahu"
] |
X-ray imaging is a fundamental clinical tool for screening and diagnosing
various diseases. However, the spatial resolution of radiographs is often
limited, making it challenging to diagnose small image details and leading to
difficulties in identifying vertebrae anomalies at an early stage in chest
radiographs. To address this limitation, we propose a novel and robust approach
to significantly improve the quality of X-ray images by iteratively training a
deep neural network. Our framework includes an embedded internal guidance layer
that enhances the fine structures of spinal vertebrae in chest X-ray images
through fully unsupervised training, utilizing an iterative procedure that
employs the same network architecture in each enhancement phase. Additionally,
we have designed an optimized loss function that accurately identifies object
boundaries and enhances spinal features, thereby further enhancing the quality
of the images. Experimental results demonstrate that our proposed method
surpasses existing detail enhancement methods in terms of BRISQUE scores, and
is comparable in terms of LPC-SI. Furthermore, our approach exhibits superior
performance in restoring hidden fine structures, as evidenced by our
qualitative results. This innovative approach has the potential to
significantly enhance the diagnostic accuracy and early detection of diseases,
making it a promising advancement in X-ray imaging technology.
|
[
"eess.IV",
"cs.CV",
"I.4; I.4.3"
] | false |
2306.03355
|
2023-06-06T02:13:27Z
|
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision,
Language, and Graphs
|
[
"Zhen Yang",
"Tinglin Huang",
"Ming Ding",
"Yuxiao Dong",
"Rex Ying",
"Yukuo Cen",
"Yangliao Geng",
"Jie Tang"
] |
In-Batch contrastive learning is a state-of-the-art self-supervised method
that brings semantically-similar instances close while pushing dissimilar
instances apart within a mini-batch. Its key to success is the negative sharing
strategy, in which every instance serves as a negative for the others within
the mini-batch. Recent studies aim to improve performance by sampling hard
negatives \textit{within the current mini-batch}, whose quality is bounded by
the mini-batch itself. In this work, we propose to improve contrastive learning
by sampling mini-batches from the input data. We present
BatchSampler\footnote{The code is available at
\url{https://github.com/THUDM/BatchSampler}} to sample mini-batches of
hard-to-distinguish (i.e., hard and true negatives to each other) instances. To
make each mini-batch have fewer false negatives, we design the proximity graph
of randomly-selected instances. To form the mini-batch, we leverage random walk
with restart on the proximity graph to help sample hard-to-distinguish
instances. BatchSampler is a simple and general technique that can be directly
plugged into existing contrastive learning models in vision, language, and
graphs. Extensive experiments on datasets of three modalities show that
BatchSampler can consistently improve the performance of powerful contrastive
models, as shown by significant improvements of SimCLR on ImageNet-100, SimCSE
on STS (language), and GraphCL and MVGRL on graph datasets.
|
[
"cs.LG",
"cs.CL",
"cs.CV"
] | false |
2306.03400
|
2023-06-06T04:30:18Z
|
G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors
|
[
"Quoc Khanh Nguyen",
"Truong Thanh Hung Nguyen",
"Vo Thanh Khang Nguyen",
"Van Binh Truong",
"Quoc Hung Cao"
] |
Nowadays, deep neural networks for object detection in images are very
prevalent. However, due to the complexity of these networks, users find it hard
to understand why these objects are detected by models. We proposed Gaussian
Class Activation Mapping Explainer (G-CAME), which generates a saliency map as
the explanation for object detection models. G-CAME can be considered a
CAM-based method that uses the activation maps of selected layers combined with
the Gaussian kernel to highlight the important regions in the image for the
predicted box. Compared with other Region-based methods, G-CAME can transcend
time constraints as it takes a very short time to explain an object. We also
evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO
2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2306.03406
|
2023-06-06T04:57:39Z
|
Deep neural networks architectures from the perspective of manifold
learning
|
[
"German Magai"
] |
Despite significant advances in the field of deep learning in ap-plications
to various areas, an explanation of the learning pro-cess of neural network
models remains an important open ques-tion. The purpose of this paper is a
comprehensive comparison and description of neural network architectures in
terms of ge-ometry and topology. We focus on the internal representation of
neural networks and on the dynamics of changes in the topology and geometry of
a data manifold on different layers. In this paper, we use the concepts of
topological data analysis (TDA) and persistent homological fractal dimension.
We present a wide range of experiments with various datasets and configurations
of convolutional neural network (CNNs) architectures and Transformers in CV and
NLP tasks. Our work is a contribution to the development of the important field
of explainable and interpretable AI within the framework of geometrical deep
learning.
|
[
"cs.LG",
"cs.AI",
"cs.CV",
"math.AT"
] | false |
2306.03522
|
2023-06-06T09:14:05Z
|
A Functional Data Perspective and Baseline On Multi-Layer
Out-of-Distribution Detection
|
[
"Eduardo Dadalto",
"Pierre Colombo",
"Guillaume Staerman",
"Nathan Noiry",
"Pablo Piantanida"
] |
A key feature of out-of-distribution (OOD) detection is to exploit a trained
neural network by extracting statistical patterns and relationships through the
multi-layer classifier to detect shifts in the expected input data
distribution. Despite achieving solid results, several state-of-the-art methods
rely on the penultimate or last layer outputs only, leaving behind valuable
information for OOD detection. Methods that explore the multiple layers either
require a special architecture or a supervised objective to do so. This work
adopts an original approach based on a functional view of the network that
exploits the sample's trajectories through the various layers and their
statistical dependencies. It goes beyond multivariate features aggregation and
introduces a baseline rooted in functional anomaly detection. In this new
framework, OOD detection translates into detecting samples whose trajectories
differ from the typical behavior characterized by the training set. We validate
our method and empirically demonstrate its effectiveness in OOD detection
compared to strong state-of-the-art baselines on computer vision benchmarks.
|
[
"cs.LG",
"cs.CV",
"stat.ML"
] | false |
2306.03551
|
2023-06-06T09:57:04Z
|
Scalable Concept Extraction in Industry 4.0
|
[
"Andrés Felipe Posada-Moreno",
"Kai Müller",
"Florian Brillowski",
"Friedrich Solowjow",
"Thomas Gries",
"Sebastian Trimpe"
] |
The industry 4.0 is leveraging digital technologies and machine learning
techniques to connect and optimize manufacturing processes. Central to this
idea is the ability to transform raw data into human understandable knowledge
for reliable data-driven decision-making. Convolutional Neural Networks (CNNs)
have been instrumental in processing image data, yet, their ``black box''
nature complicates the understanding of their prediction process. In this
context, recent advances in the field of eXplainable Artificial Intelligence
(XAI) have proposed the extraction and localization of concepts, or which
visual cues intervene on the prediction process of CNNs. This paper tackles the
application of concept extraction (CE) methods to industry 4.0 scenarios. To
this end, we modify a recently developed technique, ``Extracting Concepts with
Local Aggregated Descriptors'' (ECLAD), improving its scalability.
Specifically, we propose a novel procedure for calculating concept importance,
utilizing a wrapper function designed for CNNs. This process is aimed at
decreasing the number of times each image needs to be evaluated. Subsequently,
we demonstrate the potential of CE methods, by applying them in three
industrial use cases. We selected three representative use cases in the context
of quality control for material design (tailored textiles), manufacturing
(carbon fiber reinforcement), and maintenance (photovoltaic module inspection).
In these examples, CE was able to successfully extract and locate concepts
directly related to each task. This is, the visual cues related to each
concept, coincided with what human experts would use to perform the task
themselves, even when the visual cues were entangled between multiple classes.
Through empirical results, we show that CE can be applied for understanding
CNNs in an industrial context, giving useful insights that can relate to domain
knowledge.
|
[
"cs.AI",
"cs.CV",
"cs.LG"
] | false |
2306.03727
|
2023-06-06T14:45:44Z
|
Towards Visual Foundational Models of Physical Scenes
|
[
"Chethan Parameshwara",
"Alessandro Achille",
"Matthew Trager",
"Xiaolong Li",
"Jiawei Mo",
"Matthew Trager",
"Ashwin Swaminathan",
"CJ Taylor",
"Dheera Venkatraman",
"Xiaohan Fei",
"Stefano Soatto"
] |
We describe a first step towards learning general-purpose visual
representations of physical scenes using only image prediction as a training
criterion. To do so, we first define "physical scene" and show that, even
though different agents may maintain different representations of the same
scene, the underlying physical scene that can be inferred is unique. Then, we
show that NeRFs cannot represent the physical scene, as they lack extrapolation
mechanisms. Those, however, could be provided by Diffusion Models, at least in
theory. To test this hypothesis empirically, NeRFs can be combined with
Diffusion Models, a process we refer to as NeRF Diffusion, used as unsupervised
representations of the physical scene. Our analysis is limited to visual data,
without external grounding mechanisms that can be provided by independent
sensory modalities.
|
[
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | false |
2306.03835
|
2023-06-06T16:25:29Z
|
Atrial Septal Defect Detection in Children Based on Ultrasound Video
Using Multiple Instances Learning
|
[
"Yiman Liu",
"Qiming Huang",
"Xiaoxiang Han",
"Tongtong Liang",
"Zhifang Zhang",
"Lijun Chen",
"Jinfeng Wang",
"Angelos Stefanidis",
"Jionglong Su",
"Jiangang Chen",
"Qingli Li",
"Yuqi Zhang"
] |
Purpose: Congenital heart defect (CHD) is the most common birth defect.
Thoracic echocardiography (TTE) can provide sufficient cardiac structure
information, evaluate hemodynamics and cardiac function, and is an effective
method for atrial septal defect (ASD) examination. This paper aims to study a
deep learning method based on cardiac ultrasound video to assist in ASD
diagnosis. Materials and methods: We select two standard views of the atrial
septum (subAS) and low parasternal four-compartment view (LPS4C) as the two
views to identify ASD. We enlist data from 300 children patients as part of a
double-blind experiment for five-fold cross-validation to verify the
performance of our model. In addition, data from 30 children patients (15
positives and 15 negatives) are collected for clinician testing and compared to
our model test results (these 30 samples do not participate in model training).
We propose an echocardiography video-based atrial septal defect diagnosis
system. In our model, we present a block random selection, maximal agreement
decision and frame sampling strategy for training and testing respectively,
resNet18 and r3D networks are used to extract the frame features and aggregate
them to build a rich video-level representation. Results: We validate our model
using our private dataset by five-cross validation. For ASD detection, we
achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and
81.99 F1 score. Conclusion: The proposed model is multiple instances
learning-based deep learning model for video atrial septal defect detection
which effectively improves ASD detection accuracy when compared to the
performances of previous networks and clinical doctors.
|
[
"eess.IV",
"cs.CV",
"cs.LG"
] | false |
2306.03934
|
2023-06-06T18:01:08Z
|
Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via
Volumetric Pseudo-Labeling
|
[
"Constantin Seibold",
"Alexander Jaus",
"Matthias A. Fink",
"Moon Kim",
"Simon Reiß",
"Ken Herrmann",
"Jens Kleesiek",
"Rainer Stiefelhagen"
] |
Purpose: Interpreting chest radiographs (CXR) remains challenging due to the
ambiguity of overlapping structures such as the lungs, heart, and bones. To
address this issue, we propose a novel method for extracting fine-grained
anatomical structures in CXR using pseudo-labeling of three-dimensional
computed tomography (CT) scans.
Methods: We created a large-scale dataset of 10,021 thoracic CTs with 157
labels and applied an ensemble of 3D anatomy segmentation models to extract
anatomical pseudo-labels. These labels were projected onto a two-dimensional
plane, similar to the CXR, allowing the training of detailed semantic
segmentation models for CXR without any manual annotation effort.
Results: Our resulting segmentation models demonstrated remarkable
performance on CXR, with a high average model-annotator agreement between two
radiologists with mIoU scores of 0.93 and 0.85 for frontal and lateral anatomy,
while inter-annotator agreement remained at 0.95 and 0.83 mIoU. Our anatomical
segmentations allowed for the accurate extraction of relevant explainable
medical features such as the cardio-thoracic-ratio.
Conclusion: Our method of volumetric pseudo-labeling paired with CT
projection offers a promising approach for detailed anatomical segmentation of
CXR with a high agreement with human annotators. This technique may have
important clinical implications, particularly in the analysis of various
thoracic pathologies.
|
[
"eess.IV",
"cs.CV",
"cs.LG",
"I.4.6; I.4.7; I.4.8"
] | false |
2306.03954
|
2023-06-06T18:30:51Z
|
Recognition of Handwritten Japanese Characters Using Ensemble of
Convolutional Neural Networks
|
[
"Angel I. Solis",
"Justin Zarkovacki",
"John Ly",
"Adham Atyabi"
] |
The Japanese writing system is complex, with three character types of
Hiragana, Katakana, and Kanji. Kanji consists of thousands of unique
characters, further adding to the complexity of character identification and
literature understanding. Being able to translate handwritten Japanese
characters into digital text is useful for data analysis, translation, learning
and cultural preservation. In this study, a machine learning approach to
analyzing and recognizing handwritten Japanese characters (Kanji) is proposed.
The study used an ensemble of three convolutional neural networks (CNNs) for
recognizing handwritten Kanji characters and utilized four datasets of MNIST,
K-MNIST, Kuzushiji-49 (K49) and the top 150 represented classes in the
Kuzushiji-Kanji (K-Kanji) dataset for its performance evaluation. The results
indicate feasibility of using proposed CNN-ensemble architecture for
recognizing handwritten characters, achieving 99.4%, 96.4%, 95.0% and 96.4%
classification accuracy on MNIST, K-MNIS, K49, and K-Kanji datasets
respectively.
|
[
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | false |
2306.03993
|
2023-06-06T20:08:54Z
|
Real-Time Online Unsupervised Domain Adaptation for Real-World Person
Re-identification
|
[
"Christopher Neff",
"Armin Danesh Pazho",
"Hamed Tabkhi"
] |
Following the popularity of Unsupervised Domain Adaptation (UDA) in person
re-identification, the recently proposed setting of Online Unsupervised Domain
Adaptation (OUDA) attempts to bridge the gap towards practical applications by
introducing a consideration of streaming data. However, this still falls short
of truly representing real-world applications. This paper defines the setting
of Real-world Real-time Online Unsupervised Domain Adaptation (R$^2$OUDA) for
Person Re-identification. The R$^2$OUDA setting sets the stage for true
real-world real-time OUDA, bringing to light four major limitations found in
real-world applications that are often neglected in current research: system
generated person images, subset distribution selection, time-based data stream
segmentation, and a segment-based time constraint. To address all aspects of
this new R$^2$OUDA setting, this paper further proposes Real-World Real-Time
Online Streaming Mutual Mean-Teaching (R$^2$MMT), a novel multi-camera system
for real-world person re-identification. Taking a popular person
re-identification dataset, R$^2$MMT was used to construct over 100 data subsets
and train more than 3000 models, exploring the breadth of the R$^2$OUDA setting
to understand the training time and accuracy trade-offs and limitations for
real-world applications. R$^2$MMT, a real-world system able to respect the
strict constraints of the proposed R$^2$OUDA setting, achieves accuracies
within 0.1% of comparable OUDA methods that cannot be applied directly to
real-world applications.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2306.04021
|
2023-06-06T21:27:08Z
|
Energy-Based Models for Cross-Modal Localization using Convolutional
Transformers
|
[
"Alan Wu",
"Michael S. Ryoo"
] |
We present a novel framework using Energy-Based Models (EBMs) for localizing
a ground vehicle mounted with a range sensor against satellite imagery in the
absence of GPS. Lidar sensors have become ubiquitous on autonomous vehicles for
describing its surrounding environment. Map priors are typically built using
the same sensor modality for localization purposes. However, these map building
endeavors using range sensors are often expensive and time-consuming.
Alternatively, we leverage the use of satellite images as map priors, which are
widely available, easily accessible, and provide comprehensive coverage. We
propose a method using convolutional transformers that performs accurate
metric-level localization in a cross-modal manner, which is challenging due to
the drastic difference in appearance between the sparse range sensor readings
and the rich satellite imagery. We train our model end-to-end and demonstrate
our approach achieving higher accuracy than the state-of-the-art on KITTI,
Pandaset, and a custom dataset.
|
[
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | false |
2306.04032
|
2023-06-06T21:49:56Z
|
BokehOrNot: Transforming Bokeh Effect with Image Transformer and Lens
Metadata Embedding
|
[
"Zhihao Yang",
"Wenyi Lian",
"Siyuan Lai"
] |
Bokeh effect is an optical phenomenon that offers a pleasant visual
experience, typically generated by high-end cameras with wide aperture lenses.
The task of bokeh effect transformation aims to produce a desired effect in one
set of lenses and apertures based on another combination. Current models are
limited in their ability to render a specific set of bokeh effects, primarily
transformations from sharp to blur. In this paper, we propose a novel universal
method for embedding lens metadata into the model and introducing a loss
calculation method using alpha masks from the newly released Bokeh Effect
Transformation Dataset(BETD) [3]. Based on the above techniques, we propose the
BokehOrNot model, which is capable of producing both blur-to-sharp and
sharp-to-blur bokeh effect with various combinations of lenses and aperture
sizes. Our proposed model outperforms current leading bokeh rendering and image
restoration models and renders visually natural bokeh effects. Our code is
available at: https://github.com/indicator0/bokehornot.
|
[
"cs.CV",
"cs.LG",
"eess.IV"
] | false |
2306.04653
|
2023-06-06T10:22:43Z
|
From Data to Action: Exploring AI and IoT-driven Solutions for Smarter
Cities
|
[
"Tiago Dias",
"Tiago Fonseca",
"João Vitorino",
"Andreia Martins",
"Sofia Malpique",
"Isabel Praça"
] |
The emergence of smart cities demands harnessing advanced technologies like
the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to
unlock cities' potential to become more sustainable, efficient, and ultimately
livable for their inhabitants. This work introduces an intelligent city
management system that provides a data-driven approach to three use cases: (i)
analyze traffic information to reduce the risk of traffic collisions and
improve driver and pedestrian safety, (ii) identify when and where energy
consumption can be reduced to improve cost savings, and (iii) detect
maintenance issues like potholes in the city's roads and sidewalks, as well as
the beginning of hazards like floods and fires. A case study in Aveiro City
demonstrates the system's effectiveness in generating actionable insights that
enhance security, energy efficiency, and sustainability, while highlighting the
potential of AI and IoT-driven solutions for smart city development.
|
[
"cs.LG",
"cs.CV",
"cs.SY",
"eess.SY"
] | false |
2306.07349
|
2023-06-06T17:59:10Z
|
ATT3D: Amortized Text-to-3D Object Synthesis
|
[
"Jonathan Lorraine",
"Kevin Xie",
"Xiaohui Zeng",
"Chen-Hsuan Lin",
"Towaki Takikawa",
"Nicholas Sharp",
"Tsung-Yi Lin",
"Ming-Yu Liu",
"Sanja Fidler",
"James Lucas"
] |
Text-to-3D modelling has seen exciting progress by combining generative
text-to-image models with image-to-3D methods like Neural Radiance Fields.
DreamFusion recently achieved high-quality results but requires a lengthy,
per-prompt optimization to create 3D objects. To address this, we amortize
optimization over text prompts by training on many prompts simultaneously with
a unified model, instead of separately. With this, we share computation across
a prompt set, training in less time than per-prompt optimization. Our framework
- Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to
generalize to unseen setups and smooth interpolations between text for novel
assets and simple animations.
|
[
"cs.LG",
"cs.AI",
"cs.CV",
"68T45",
"I.2.6; I.2.7; I.3.6; I.3.7"
] | true |
2306.03679
|
2023-06-06T13:41:37Z
|
Human-imperceptible, Machine-recognizable Images
|
[
"Fusheng Hao",
"Fengxiang He",
"Yikai Wang",
"Fuxiang Wu",
"Jing Zhang",
"Jun Cheng",
"Dacheng Tao"
] |
Massive human-related data is collected to train neural networks for computer
vision tasks. A major conflict is exposed relating to software engineers
between better developing AI systems and distancing from the sensitive training
data. To reconcile this conflict, this paper proposes an efficient
privacy-preserving learning paradigm, where images are first encrypted to
become ``human-imperceptible, machine-recognizable'' via one of the two
encryption strategies: (1) random shuffling to a set of equally-sized patches
and (2) mixing-up sub-patches of the images. Then, minimal adaptations are made
to vision transformer to enable it to learn on the encrypted images for vision
tasks, including image classification and object detection. Extensive
experiments on ImageNet and COCO show that the proposed paradigm achieves
comparable accuracy with the competitive methods. Decrypting the encrypted
images requires solving an NP-hard jigsaw puzzle or an ill-posed inverse
problem, which is empirically shown intractable to be recovered by various
attackers, including the powerful vision transformer-based attacker. We thus
show that the proposed paradigm can ensure the encrypted images have become
human-imperceptible while preserving machine-recognizable information. The code
is available at \url{https://github.com/FushengHao/PrivacyPreservingML.}
|
[
"cs.CV",
"cs.AI",
"cs.CR",
"cs.LG",
"stat.ML"
] | false |
2306.03350
|
2023-06-06T01:56:44Z
|
Click: Controllable Text Generation with Sequence Likelihood Contrastive
Learning
|
[
"Chujie Zheng",
"Pei Ke",
"Zheng Zhang",
"Minlie Huang"
] |
It has always been an important yet challenging problem to control language
models to avoid generating texts with undesirable attributes, such as toxic
language and unnatural repetition. We introduce Click for controllable text
generation, which needs no modification to the model architecture and
facilitates out-of-the-box use of trained models. It employs a contrastive loss
on sequence likelihood, which fundamentally decreases the generation
probability of negative samples (i.e., generations with undesirable
attributes). It also adopts a novel likelihood ranking-based strategy to
construct contrastive samples from model generations. On the tasks of language
detoxification, sentiment steering, and repetition reduction, we show that
Click outperforms strong baselines of controllable text generation and
demonstrate the superiority of Click's sample construction strategy.
|
[
"cs.CL"
] | false |
2306.03415
|
2023-06-06T05:30:49Z
|
Efficient and Interpretable Compressive Text Summarisation with
Unsupervised Dual-Agent Reinforcement Learning
|
[
"Peggy Tang",
"Junbin Gao",
"Lei Zhang",
"Zhiyong Wang"
] |
Recently, compressive text summarisation offers a balance between the
conciseness issue of extractive summarisation and the factual hallucination
issue of abstractive summarisation. However, most existing compressive
summarisation methods are supervised, relying on the expensive effort of
creating a new training dataset with corresponding compressive summaries. In
this paper, we propose an efficient and interpretable compressive summarisation
method that utilises unsupervised dual-agent reinforcement learning to optimise
a summary's semantic coverage and fluency by simulating human judgment on
summarisation quality. Our model consists of an extractor agent and a
compressor agent, and both agents have a multi-head attentional pointer-based
structure. The extractor agent first chooses salient sentences from a document,
and then the compressor agent compresses these extracted sentences by selecting
salient words to form a summary without using reference summaries to compute
the summary reward. To our best knowledge, this is the first work on
unsupervised compressive summarisation. Experimental results on three widely
used datasets (e.g., Newsroom, CNN/DM, and XSum) show that our model achieves
promising performance and a significant improvement on Newsroom in terms of the
ROUGE metric, as well as interpretability of semantic coverage of summarisation
results.
|
[
"cs.CL"
] | false |
2306.03469
|
2023-06-06T07:42:39Z
|
Joint Event Extraction via Structural Semantic Matching
|
[
"Haochen Li",
"Tianhao Gao",
"Jingkun Wang",
"Weiping Li"
] |
Event Extraction (EE) is one of the essential tasks in information
extraction, which aims to detect event mentions from text and find the
corresponding argument roles. The EE task can be abstracted as a process of
matching the semantic definitions and argument structures of event types with
the target text. This paper encodes the semantic features of event types and
makes structural matching with target text. Specifically, Semantic Type
Embedding (STE) and Dynamic Structure Encoder (DSE) modules are proposed. Also,
the Joint Structural Semantic Matching (JSSM) model is built to jointly perform
event detection and argument extraction tasks through a bidirectional attention
layer. The experimental results on the ACE2005 dataset indicate that our model
achieves a significant performance improvement
|
[
"cs.CL"
] | false |
2306.03507
|
2023-06-06T08:53:01Z
|
"A Little is Enough": Few-Shot Quality Estimation based Corpus Filtering
improves Machine Translation
|
[
"Akshay Batheja",
"Pushpak Bhattacharyya"
] |
Quality Estimation (QE) is the task of evaluating the quality of a
translation when reference translation is not available. The goal of QE aligns
with the task of corpus filtering, where we assign the quality score to the
sentence pairs present in the pseudo-parallel corpus. We propose a Quality
Estimation based Filtering approach to extract high-quality parallel data from
the pseudo-parallel corpus. To the best of our knowledge, this is a novel
adaptation of the QE framework to extract quality parallel corpus from the
pseudo-parallel corpus. By training with this filtered corpus, we observe an
improvement in the Machine Translation (MT) system's performance by up to 1.8
BLEU points, for English-Marathi, Chinese-English, and Hindi-Bengali language
pairs, over the baseline model. The baseline model is the one that is trained
on the whole pseudo-parallel corpus. Our Few-shot QE model transfer learned
from the English-Marathi QE model and fine-tuned on only 500 Hindi-Bengali
training instances, shows an improvement of up to 0.6 BLEU points for
Hindi-Bengali language pair, compared to the baseline model. This demonstrates
the promise of transfer learning in the setting under discussion. QE systems
typically require in the order of (7K-25K) of training data. Our Hindi-Bengali
QE is trained on only 500 instances of training that is 1/40th of the normal
requirement and achieves comparable performance. All the scripts and datasets
utilized in this study will be publicly available.
|
[
"cs.CL"
] | false |
2306.03598
|
2023-06-06T11:37:46Z
|
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built
on Pre-Trained Language Models
|
[
"Jiazheng Li",
"Zhaoyue Sun",
"Bin Liang",
"Lin Gui",
"Yulan He"
] |
Text classifiers built on Pre-trained Language Models (PLMs) have achieved
remarkable progress in various tasks including sentiment analysis, natural
language inference, and question-answering. However, the occurrence of
uncertain predictions by these classifiers poses a challenge to their
reliability when deployed in practical applications. Much effort has been
devoted to designing various probes in order to understand what PLMs capture.
But few studies have delved into factors influencing PLM-based classifiers'
predictive uncertainty. In this paper, we propose a novel framework, called
CUE, which aims to interpret uncertainties inherent in the predictions of
PLM-based models. In particular, we first map PLM-encoded representations to a
latent space via a variational auto-encoder. We then generate text
representations by perturbing the latent space which causes fluctuation in
predictive uncertainty. By comparing the difference in predictive uncertainty
between the perturbed and the original text representations, we are able to
identify the latent dimensions responsible for uncertainty and subsequently
trace back to the input features that contribute to such uncertainty. Our
extensive experiments on four benchmark datasets encompassing linguistic
acceptability classification, emotion classification, and natural language
inference show the feasibility of our proposed framework. Our source code is
available at: https://github.com/lijiazheng99/CUE.
|
[
"cs.CL"
] | false |
2306.03652
|
2023-06-06T13:13:27Z
|
Injecting knowledge into language generation: a case study in
auto-charting after-visit care instructions from medical dialogue
|
[
"Maksim Eremeev",
"Ilya Valmianski",
"Xavier Amatriain",
"Anitha Kannan"
] |
Factual correctness is often the limiting factor in practical applications of
natural language generation in high-stakes domains such as healthcare. An
essential requirement for maintaining factuality is the ability to deal with
rare tokens. This paper focuses on rare tokens that appear in both the source
and the reference sequences, and which, when missed during generation, decrease
the factual correctness of the output text. For high-stake domains that are
also knowledge-rich, we show how to use knowledge to (a) identify which rare
tokens that appear in both source and reference are important and (b) uplift
their conditional probability. We introduce the ``utilization rate'' that
encodes knowledge and serves as a regularizer by maximizing the marginal
probability of selected tokens. We present a study in a knowledge-rich domain
of healthcare, where we tackle the problem of generating after-visit care
instructions based on patient-doctor dialogues. We verify that, in our dataset,
specific medical concepts with high utilization rates are underestimated by
conventionally trained sequence-to-sequence models. We observe that correcting
this with our approach to knowledge injection reduces the uncertainty of the
model as well as improves factuality and coherence without negatively impacting
fluency.
|
[
"cs.CL"
] | false |
2306.03678
|
2023-06-06T13:41:09Z
|
On the Difference of BERT-style and CLIP-style Text Encoders
|
[
"Zhihong Chen",
"Guiming Hardy Chen",
"Shizhe Diao",
"Xiang Wan",
"Benyou Wang"
] |
Masked language modeling (MLM) has been one of the most popular pretraining
recipes in natural language processing, e.g., BERT, one of the representative
models. Recently, contrastive language-image pretraining (CLIP) has also
attracted attention, especially its vision models that achieve excellent
performance on a broad range of vision tasks. However, few studies are
dedicated to studying the text encoders learned by CLIP. In this paper, we
analyze the difference between BERT-style and CLIP-style text encoders from
three experiments: (i) general text understanding, (ii) vision-centric text
understanding, and (iii) text-to-image generation. Experimental analyses show
that although CLIP-style text encoders underperform BERT-style ones for general
text understanding tasks, they are equipped with a unique ability, i.e.,
synesthesia, for the cross-modal association, which is more similar to the
senses of humans.
|
[
"cs.CL"
] | false |
2306.03736
|
2023-06-06T14:52:47Z
|
FinRED: A Dataset for Relation Extraction in Financial Domain
|
[
"Soumya Sharma",
"Tapas Nayak",
"Arusarka Bose",
"Ajay Kumar Meena",
"Koustuv Dasgupta",
"Niloy Ganguly",
"Pawan Goyal"
] |
Relation extraction models trained on a source domain cannot be applied on a
different target domain due to the mismatch between relation sets. In the
current literature, there is no extensive open-source relation extraction
dataset specific to the finance domain. In this paper, we release FinRED, a
relation extraction dataset curated from financial news and earning call
transcripts containing relations from the finance domain. FinRED has been
created by mapping Wikidata triplets using distance supervision method. We
manually annotate the test data to ensure proper evaluation. We also experiment
with various state-of-the-art relation extraction models on this dataset to
create the benchmark. We see a significant drop in their performance on FinRED
compared to the general relation extraction datasets which tells that we need
better models for financial relation extraction.
|
[
"cs.CL"
] | false |
2306.03853
|
2023-06-06T16:45:44Z
|
From Key Points to Key Point Hierarchy: Structured and Expressive
Opinion Summarization
|
[
"Arie Cattan",
"Lilach Eden",
"Yoav Kantor",
"Roy Bar-Haim"
] |
Key Point Analysis (KPA) has been recently proposed for deriving fine-grained
insights from collections of textual comments. KPA extracts the main points in
the data as a list of concise sentences or phrases, termed key points, and
quantifies their prevalence. While key points are more expressive than word
clouds and key phrases, making sense of a long, flat list of key points, which
often express related ideas in varying levels of granularity, may still be
challenging. To address this limitation of KPA, we introduce the task of
organizing a given set of key points into a hierarchy, according to their
specificity. Such hierarchies may be viewed as a novel type of Textual
Entailment Graph. We develop ThinkP, a high quality benchmark dataset of key
point hierarchies for business and product reviews, obtained by consolidating
multiple annotations. We compare different methods for predicting pairwise
relations between key points, and for inferring a hierarchy from these pairwise
predictions. In particular, for the task of computing pairwise key point
relations, we achieve significant gains over existing strong baselines by
applying directional distributional similarity methods to a novel
distributional representation of key points, and further boost performance via
weak supervision.
|
[
"cs.CL"
] | false |
2306.03978
|
2023-06-06T19:31:08Z
|
Büyük dil modellerinin Türkçe verisetleri ile
eğitilmesi ve ince ayarlanması
|
[
"A. Taha Arslan"
] |
Large language models have advanced enormously, gained vast attraction and
are having a phase of intensed research. Some of the developed models and
training datasets have been made open-accessible. Hence these may be further
fine-tuned with some techniques to obtain specialized models for specific
tasks. When it comes to Turkish language, open-access models do not provide
satisfactory coverage. This is also observed over published datasets. In this
work, we propose some ideas to mitigate this issue: creating large Turkish
datasets, training LLMs with these and fine-tuning pre-trained models with
Turkish inputs. We report our findings on Turkish-based trainings with the
problems encountered along the way. We conclude with outcomes of these
experiments and propose ideas for further works.
--
B\"uy\"uk dil modelleri inan{\i}lmaz \"ol\c{c}\"ude geli\c{s}mekte, b\"uy\"uk
ilgi toplayarak ve \"uzerlerinde yo\u{g}un ara\c{s}tirmalarin yapildi\u{g}i bir
d\"onemdedirler. Geli\c{s}tirilen modeller ve e\u{g}itimde kullanilan
verisetlerinden bazilari a\c{c}ik eri\c{s}imli olarak sunulmaktadir. B\"oylece
ince ayarlama teknikleri uygulayarak \"ozelle\c{s}mi\c{s} g\"orevler i\c{c}in
\c{c}ali\c{s}abilir modeller elde edilmektedir. T\"urk\c{c}e s\"oz konusu
oldu\u{g}unda bu modellerinin kapsayicili\u{g}i yeterli d\"uzeyde de\u{g}ildir.
Bu durum, yayimlanan verisetlerinde de g\"ozlemlenebilir. Bunu a\c{s}manin
yollari T\"urk\c{c}e i\c{c}erikli b\"uy\"uk verisetlerinin olu\c{s}turulmasi,
b\"uy\"uk dil modellerinin bunlarla e\u{g}itilmesi ve \"onceden
e\u{g}itilmi\c{s} modellerin T\"urk\c{c}e girdilerle ince ayarlanmalari
olabilir. Bu \c{c}ali\c{s}mada a\c{c}ik eri\c{s}imli dil modelleri ve
verisetleri \"uzerinde durulmakta ve T\"urk\c{c}e temelli bazi deneyler,
kar\c{s}ila\c{s}ilan sorunlar ve sonu\c{c}lar irdelenmektedir.
|
[
"cs.CL",
"I.2.7"
] | false |
2306.04043
|
2023-06-06T22:14:59Z
|
An Analysis of Reader Engagement in Literary Fiction through Eye
Tracking and Linguistic Features
|
[
"Rose Neis",
"Karin de Langis",
"Zae Myung Kim",
"Dongyeop Kang"
] |
Capturing readers' engagement in fiction is a challenging but important
aspect of narrative understanding. In this study, we collected 23 readers'
reactions to 2 short stories through eye tracking, sentence-level annotations,
and an overall engagement scale survey. We analyzed the significance of various
qualities of the text in predicting how engaging a reader is likely to find it.
As enjoyment of fiction is highly contextual, we also investigated individual
differences in our data. Furthering our understanding of what captivates
readers in fiction will help better inform models used in creative narrative
generation and collaborative writing tools.
|
[
"cs.CL"
] | false |
2306.04067
|
2023-06-06T23:56:18Z
|
An Empirical Analysis of Parameter-Efficient Methods for Debiasing
Pre-Trained Language Models
|
[
"Zhongbin Xie",
"Thomas Lukasiewicz"
] |
The increasingly large size of modern pretrained language models not only
makes them inherit more human-like biases from the training corpora, but also
makes it computationally expensive to mitigate such biases. In this paper, we
investigate recent parameter-efficient methods in combination with
counterfactual data augmentation (CDA) for bias mitigation. We conduct
extensive experiments with prefix tuning, prompt tuning, and adapter tuning on
different language models and bias types to evaluate their debiasing
performance and abilities to preserve the internal knowledge of a pre-trained
model. We find that the parameter-efficient methods (i) are effective in
mitigating gender bias, where adapter tuning is consistently the most effective
one and prompt tuning is more suitable for GPT-2 than BERT, (ii) are less
effective when it comes to racial and religious bias, which may be attributed
to the limitations of CDA, and (iii) can perform similarly to or sometimes
better than full fine-tuning with improved time and memory efficiency, as well
as maintain the internal knowledge in BERT and GPT-2, evaluated via fact
retrieval and downstream fine-tuning.
|
[
"cs.CL"
] | false |
2306.05434
|
2023-06-06T18:06:24Z
|
How Good is the Model in Model-in-the-loop Event Coreference Resolution
Annotation?
|
[
"Shafiuddin Rehan Ahmed",
"Abhijnan Nath",
"Michael Regan",
"Adam Pollins",
"Nikhil Krishnaswamy",
"James H. Martin"
] |
Annotating cross-document event coreference links is a time-consuming and
cognitively demanding task that can compromise annotation quality and
efficiency. To address this, we propose a model-in-the-loop annotation approach
for event coreference resolution, where a machine learning model suggests
likely corefering event pairs only. We evaluate the effectiveness of this
approach by first simulating the annotation process and then, using a novel
annotator-centric Recall-Annotation effort trade-off metric, we compare the
results of various underlying models and datasets. We finally present a method
for obtaining 97\% recall while substantially reducing the workload required by
a fully manual annotation process. Code and data can be found at
https://github.com/ahmeshaf/model_in_coref
|
[
"cs.CL"
] | false |
2306.03586
|
2023-06-06T11:08:20Z
|
Language acquisition: do children and language models follow similar
learning stages?
|
[
"Linnea Evanson",
"Yair Lakretz",
"Jean-Rémi King"
] |
During language acquisition, children follow a typical sequence of learning
stages, whereby they first learn to categorize phonemes before they develop
their lexicon and eventually master increasingly complex syntactic structures.
However, the computational principles that lead to this learning trajectory
remain largely unknown. To investigate this, we here compare the learning
trajectories of deep language models to those of children. Specifically, we
test whether, during its training, GPT-2 exhibits stages of language
acquisition comparable to those observed in children aged between 18 months and
6 years. For this, we train 48 GPT-2 models from scratch and evaluate their
syntactic and semantic abilities at each training step, using 96 probes curated
from the BLiMP, Zorro and BIG-Bench benchmarks. We then compare these
evaluations with the behavior of 54 children during language production. Our
analyses reveal three main findings. First, similarly to children, the language
models tend to learn linguistic skills in a systematic order. Second, this
learning scheme is parallel: the language tasks that are learned last improve
from the very first training steps. Third, some - but not all - learning stages
are shared between children and these language models. Overall, these results
shed new light on the principles of language acquisition, and highlight
important divergences in how humans and modern algorithms learn to process
natural language.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.03608
|
2023-06-06T11:54:48Z
|
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
|
[
"Yaochen Liu",
"Qiuchi Li",
"Benyou Wang",
"Yazhou Zhang",
"Dawei Song"
] |
Quantum theory, originally proposed as a physical theory to describe the
motions of microscopic particles, has been applied to various non-physics
domains involving human cognition and decision-making that are inherently
uncertain and exhibit certain non-classical, quantum-like characteristics.
Sentiment analysis is a typical example of such domains. In the last few years,
by leveraging the modeling power of quantum probability (a non-classical
probability stemming from quantum mechanics methodology) and deep neural
networks, a range of novel quantum-cognitively inspired models for sentiment
analysis have emerged and performed well. This survey presents a timely
overview of the latest developments in this fascinating cross-disciplinary
area. We first provide a background of quantum probability and quantum
cognition at a theoretical level, analyzing their advantages over classical
theories in modeling the cognitive aspects of sentiment analysis. Then, recent
quantum-cognitively inspired models are introduced and discussed in detail,
focusing on how they approach the key challenges of the sentiment analysis
task. Finally, we discuss the limitations of the current research and highlight
future research directions.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.03628
|
2023-06-06T12:30:29Z
|
Convergence and Diversity in the Control Hierarchy
|
[
"Alexandra Butoi",
"Ryan Cotterell",
"David Chiang"
] |
Weir has defined a hierarchy of language classes whose second member
($\mathcal{L}_2$) is generated by tree-adjoining grammars (TAG), linear indexed
grammars (LIG), combinatory categorial grammars, and head grammars. The
hierarchy is obtained using the mechanism of control, and $\mathcal{L}_2$ is
obtained using a context-free grammar (CFG) whose derivations are controlled by
another CFG. We adapt Weir's definition of a controllable CFG to give a
definition of controllable pushdown automata (PDAs). This yields three new
characterizations of $\mathcal{L}_2$ as the class of languages generated by
PDAs controlling PDAs, PDAs controlling CFGs, and CFGs controlling PDAs. We
show that these four formalisms are not only weakly equivalent but equivalent
in a stricter sense that we call d-weak equivalence. Furthermore, using an even
stricter notion of equivalence called d-strong equivalence, we make precise the
intuition that a CFG controlling a CFG is a TAG, a PDA controlling a PDA is an
embedded PDA, and a PDA controlling a CFG is a LIG. The fourth member of this
family, a CFG controlling a PDA, does not correspond to any formalism we know
of, so we invent one and call it a Pushdown Adjoining Automaton.
|
[
"cs.FL",
"cs.CL"
] | false |
2306.03650
|
2023-06-06T13:08:22Z
|
A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm,
Sentiment and Emotion Analysis
|
[
"Yaochen Liu",
"Yazhou Zhang",
"Dawei Song"
] |
Sarcasm, sentiment, and emotion are three typical kinds of spontaneous
affective responses of humans to external events and they are tightly
intertwined with each other. Such events may be expressed in multiple
modalities (e.g., linguistic, visual and acoustic), e.g., multi-modal
conversations. Joint analysis of humans' multi-modal sarcasm, sentiment, and
emotion is an important yet challenging topic, as it is a complex cognitive
process involving both cross-modality interaction and cross-affection
correlation. From the probability theory perspective, cross-affection
correlation also means that the judgments on sarcasm, sentiment, and emotion
are incompatible. However, this exposed phenomenon cannot be sufficiently
modelled by classical probability theory due to its assumption of
compatibility. Neither do the existing approaches take it into consideration.
In view of the recent success of quantum probability (QP) in modeling human
cognition, particularly contextual incompatible decision making, we take the
first step towards introducing QP into joint multi-modal sarcasm, sentiment,
and emotion analysis. Specifically, we propose a QUantum probabIlity driven
multi-modal sarcasm, sEntiment and emoTion analysis framework, termed QUIET.
Extensive experiments on two datasets and the results show that the
effectiveness and advantages of QUIET in comparison with a wide range of the
state-of-the-art baselines. We also show the great potential of QP in
multi-affect analysis.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.03733
|
2023-06-06T14:49:25Z
|
A Novel Approach To User Agent String Parsing For Vulnerability Analysis
Using Mutli-Headed Attention
|
[
"Dhruv Nandakumar",
"Sathvik Murli",
"Ankur Khosla",
"Kevin Choi",
"Abdul Rahman",
"Drew Walsh",
"Scott Riede",
"Eric Dull",
"Edward Bowen"
] |
The increasing reliance on the internet has led to the proliferation of a
diverse set of web-browsers and operating systems (OSs) capable of browsing the
web. User agent strings (UASs) are a component of web browsing that are
transmitted with every Hypertext Transfer Protocol (HTTP) request. They contain
information about the client device and software, which is used by web servers
for various purposes such as content negotiation and security. However, due to
the proliferation of various browsers and devices, parsing UASs is a
non-trivial task due to a lack of standardization of UAS formats. Current
rules-based approaches are often brittle and can fail when encountering such
non-standard formats. In this work, a novel methodology for parsing UASs using
Multi-Headed Attention Based transformers is proposed. The proposed methodology
exhibits strong performance in parsing a variety of UASs with differing
formats. Furthermore, a framework to utilize parsed UASs to estimate the
vulnerability scores for large sections of publicly visible IT networks or
regions is also discussed. The methodology present here can also be easily
extended or deployed for real-time parsing of logs in enterprise settings.
|
[
"cs.CR",
"cs.CL"
] | false |
2306.03856
|
2023-06-06T16:51:03Z
|
Iterative Translation Refinement with Large Language Models
|
[
"Pinzhen Chen",
"Zhicheng Guo",
"Barry Haddow",
"Kenneth Heafield"
] |
Large language models have shown surprising performances in understanding
instructions and performing natural language tasks. In this paper, we propose
iterative translation refinement to leverage the power of large language models
for more natural translation and post-editing. We show that by simply involving
a large language model in an iterative process, the output quality improves
beyond mere translation. Extensive test scenarios with GPT-3.5 reveal that
although iterations reduce string-based metric scores, neural metrics indicate
comparable if not improved translation quality. Further, human evaluations
demonstrate that our method effectively reduces translationese compared to
initial GPT translations and even human references, especially for into-English
directions. Ablation studies underscore the importance of anchoring the
refinement process to the source input and a reasonable initial translation.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.03866
|
2023-06-06T17:09:29Z
|
Correction of Errors in Preference Ratings from Automated Metrics for
Text Generation
|
[
"Jan Deriu",
"Pius von Däniken",
"Don Tuggener",
"Mark Cieliebak"
] |
A major challenge in the field of Text Generation is evaluation: Human
evaluations are cost-intensive, and automated metrics often display
considerable disagreement with human judgments. In this paper, we propose a
statistical model of Text Generation evaluation that accounts for the
error-proneness of automated metrics when used to generate preference rankings
between system outputs. We show that existing automated metrics are generally
over-confident in assigning significant differences between systems in this
setting. However, our model enables an efficient combination of human and
automated ratings to remedy the error-proneness of the automated metrics. We
show that using this combination, we only require about 50% of the human
annotations typically used in evaluations to arrive at robust and statistically
significant results while yielding the same evaluation outcome as the pure
human evaluation in 95% of cases. We showcase the benefits of approach for
three text generation tasks: dialogue systems, machine translation, and text
summarization.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.03907
|
2023-06-06T17:59:49Z
|
CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental
Fine-Tuning and Multi-Task Learning with Label Descriptions
|
[
"Janis Goldzycher"
] |
The widespread popularity of social media has led to an increase in hateful,
abusive, and sexist language, motivating methods for the automatic detection of
such phenomena. The goal of the SemEval shared task \textit{Towards Explainable
Detection of Online Sexism} (EDOS 2023) is to detect sexism in English social
media posts (subtask A), and to categorize such posts into four coarse-grained
sexism categories (subtask B), and eleven fine-grained subcategories (subtask
C). In this paper, we present our submitted systems for all three subtasks,
based on a multi-task model that has been fine-tuned on a range of related
tasks and datasets before being fine-tuned on the specific EDOS subtasks. We
implement multi-task learning by formulating each task as binary pairwise text
classification, where the dataset and label descriptions are given along with
the input text. The results show clear improvements over a fine-tuned
DeBERTa-V3 serving as a baseline leading to $F_1$-scores of 85.9\% in subtask A
(rank 13/84), 64.8\% in subtask B (rank 19/69), and 44.9\% in subtask C
(26/63).
|
[
"cs.CL",
"cs.CY"
] | false |
2306.03959
|
2023-06-06T18:42:08Z
|
Leveraging Explicit Procedural Instructions for Data-Efficient Action
Prediction
|
[
"Julia White",
"Arushi Raghuvanshi",
"Yada Pruksachatkun"
] |
Task-oriented dialogues often require agents to enact complex, multi-step
procedures in order to meet user requests. While large language models have
found success automating these dialogues in constrained environments, their
widespread deployment is limited by the substantial quantities of task-specific
data required for training. The following paper presents a data-efficient
solution to constructing dialogue systems, leveraging explicit instructions
derived from agent guidelines, such as company policies or customer service
manuals. Our proposed Knowledge-Augmented Dialogue System (KADS) combines a
large language model with a knowledge retrieval module that pulls documents
outlining relevant procedures from a predefined set of policies, given a
user-agent interaction. To train this system, we introduce a semi-supervised
pre-training scheme that employs dialogue-document matching and action-oriented
masked language modeling with partial parameter freezing. We evaluate the
effectiveness of our approach on prominent task-oriented dialogue datasets,
Action-Based Conversations Dataset and Schema-Guided Dialogue, for two dialogue
tasks: action state tracking and workflow discovery. Our results demonstrate
that procedural knowledge augmentation improves accuracy predicting in- and
out-of-distribution actions while preserving high performance in settings with
low or sparse data.
|
[
"cs.CL",
"cs.IR"
] | false |
2306.04009
|
2023-06-06T20:45:18Z
|
Triggering Multi-Hop Reasoning for Question Answering in Language Models
using Soft Prompts and Random Walks
|
[
"Kanishka Misra",
"Cicero Nogueira dos Santos",
"Siamak Shakeri"
] |
Despite readily memorizing world knowledge about entities, pre-trained
language models (LMs) struggle to compose together two or more facts to perform
multi-hop reasoning in question-answering tasks. In this work, we propose
techniques that improve upon this limitation by relying on random walks over
structured knowledge graphs. Specifically, we use soft prompts to guide LMs to
chain together their encoded knowledge by learning to map multi-hop questions
to random walk paths that lead to the answer. Applying our methods on two T5
LMs shows substantial improvements over standard tuning approaches in answering
questions that require 2-hop reasoning.
|
[
"cs.CL",
"cs.AI"
] | true |
2306.04059
|
2023-06-06T23:15:59Z
|
Augmenting Reddit Posts to Determine Wellness Dimensions impacting
Mental Health
|
[
"Chandreen Liyanage",
"Muskan Garg",
"Vijay Mago",
"Sunghwan Sohn"
] |
Amid ongoing health crisis, there is a growing necessity to discern possible
signs of Wellness Dimensions (WD) manifested in self-narrated text. As the
distribution of WD on social media data is intrinsically imbalanced, we
experiment the generative NLP models for data augmentation to enable further
improvement in the pre-screening task of classifying WD. To this end, we
propose a simple yet effective data augmentation approach through prompt-based
Generative NLP models, and evaluate the ROUGE scores and syntactic/semantic
similarity among existing interpretations and augmented data. Our approach with
ChatGPT model surpasses all the other methods and achieves improvement over
baselines such as Easy-Data Augmentation and Backtranslation. Introducing data
augmentation to generate more training samples and balanced dataset, results in
the improved F-score and the Matthew's Correlation Coefficient for upto 13.11%
and 15.95%, respectively.
|
[
"cs.CL",
"cs.CY"
] | false |
2306.05370
|
2023-06-06T12:59:03Z
|
Detecting Human Rights Violations on Social Media during Russia-Ukraine
War
|
[
"Poli Nemkova",
"Solomon Ubani",
"Suleyman Olcay Polat",
"Nayeon Kim",
"Rodney D. Nielsen"
] |
The present-day Russia-Ukraine military conflict has exposed the pivotal role
of social media in enabling the transparent and unbridled sharing of
information directly from the frontlines. In conflict zones where freedom of
expression is constrained and information warfare is pervasive, social media
has emerged as an indispensable lifeline. Anonymous social media platforms, as
publicly available sources for disseminating war-related information, have the
potential to serve as effective instruments for monitoring and documenting
Human Rights Violations (HRV). Our research focuses on the analysis of data
from Telegram, the leading social media platform for reading independent news
in post-Soviet regions. We gathered a dataset of posts sampled from 95 public
Telegram channels that cover politics and war news, which we have utilized to
identify potential occurrences of HRV. Employing a mBERT-based text classifier,
we have conducted an analysis to detect any mentions of HRV in the Telegram
data. Our final approach yielded an $F_2$ score of 0.71 for HRV detection,
representing an improvement of 0.38 over the multilingual BERT base model. We
release two datasets that contains Telegram posts: (1) large corpus with over
2.3 millions posts and (2) annotated at the sentence-level dataset to indicate
HRVs. The Telegram posts are in the context of the Russia-Ukraine war. We posit
that our findings hold significant implications for NGOs, governments, and
researchers by providing a means to detect and document possible human rights
violations.
|
[
"cs.CY",
"cs.CL"
] | false |
2306.03411
|
2023-06-06T05:18:21Z
|
Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search
|
[
"Zhiyu Chen",
"Jason Choi",
"Besnik Fetahu",
"Oleg Rokhlenko",
"Shervin Malmasi"
] |
Customers interacting with product search engines are increasingly
formulating information-seeking queries. Frequently Asked Question (FAQ)
retrieval aims to retrieve common question-answer pairs for a user query with
question intent. Integrating FAQ retrieval in product search can not only
empower users to make more informed purchase decisions, but also enhance user
retention through efficient post-purchase support. Determining when an FAQ
entry can satisfy a user's information need within product search, without
disrupting their shopping experience, represents an important challenge. We
propose an intent-aware FAQ retrieval system consisting of (1) an intent
classifier that predicts when a user's information need can be answered by an
FAQ; (2) a reformulation model that rewrites a query into a natural question.
Offline evaluation demonstrates that our approach improves Hit@1 by 13% on
retrieving ground-truth FAQs, while reducing latency by 95% compared to
baseline systems. These improvements are further validated by real user
feedback, where 71% of displayed FAQs on top of product search results received
explicit positive user feedback. Overall, our findings show promising
directions for integrating FAQ retrieval into product search at scale.
|
[
"cs.CL",
"cs.AI",
"cs.IR"
] | false |
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