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2306.04510
|
2023-06-07T15:23:59Z
|
Unified Model for Crystalline Material Generation
|
[
"Astrid Klipfel",
"Yaël Frégier",
"Adlane Sayede",
"Zied Bouraoui"
] |
One of the greatest challenges facing our society is the discovery of new
innovative crystal materials with specific properties. Recently, the problem of
generating crystal materials has received increasing attention, however, it
remains unclear to what extent, or in what way, we can develop generative
models that consider both the periodicity and equivalence geometric of crystal
structures. To alleviate this issue, we propose two unified models that act at
the same time on crystal lattice and atomic positions using periodic
equivariant architectures. Our models are capable to learn any arbitrary
crystal lattice deformation by lowering the total energy to reach thermodynamic
stability. Code and data are available at https://github.com/aklipf/GemsNet.
|
[
"cond-mat.mtrl-sci",
"cs.LG",
"physics.comp-ph"
] | false |
2306.04556
|
2023-06-07T16:03:55Z
|
StudentEval: A Benchmark of Student-Written Prompts for Large Language
Models of Code
|
[
"Hannah McLean Babe",
"Sydney Nguyen",
"Yangtian Zi",
"Arjun Guha",
"Molly Q Feldman",
"Carolyn Jane Anderson"
] |
Code LLMs are being rapidly deployed and there is evidence that they can make
professional programmers more productive. Current benchmarks for code
generation measure whether models generate correct programs given an expert
prompt. In this paper, we present a new benchmark containing multiple prompts
per problem, written by a specific population of non-expert prompters:
beginning programmers. StudentEval contains 1,749 prompts for 48 problems,
written by 80 students who have only completed one semester of Python
programming. Our students wrote these prompts while working interactively with
a Code LLM, and we observed very mixed success rates. We use StudentEval to
evaluate 5 Code LLMs and find that StudentEval is a better discriminator of
model performance than existing benchmarks. We analyze the prompts and find
significant variation in students' prompting techniques. We also find that
nondeterministic LLM sampling could mislead students into thinking that their
prompts are more (or less) effective than they actually are, which has
implications for how to teach with Code LLMs.
|
[
"cs.LG",
"cs.HC",
"cs.SE"
] | false |
2306.04566
|
2023-06-07T16:13:16Z
|
Recent applications of machine learning, remote sensing, and iot
approaches in yield prediction: a critical review
|
[
"Fatima Zahra Bassine",
"Terence Epule Epule",
"Ayoub Kechchour",
"Abdelghani Chehbouni"
] |
The integration of remote sensing and machine learning in agriculture is
transforming the industry by providing insights and predictions through data
analysis. This combination leads to improved yield prediction and water
management, resulting in increased efficiency, better yields, and more
sustainable agricultural practices. Achieving the United Nations' Sustainable
Development Goals, especially "zero hunger," requires the investigation of crop
yield and precipitation gaps, which can be accomplished through, the usage of
artificial intelligence (AI), machine learning (ML), remote sensing (RS), and
the internet of things (IoT). By integrating these technologies, a robust
agricultural mobile or web application can be developed, providing farmers and
decision-makers with valuable information and tools for improving crop
management and increasing efficiency. Several studies have investigated these
new technologies and their potential for diverse tasks such as crop monitoring,
yield prediction, irrigation management, etc. Through a critical review, this
paper reviews relevant articles that have used RS, ML, cloud computing, and IoT
in crop yield prediction. It reviews the current state-of-the-art in this field
by critically evaluating different machine-learning approaches proposed in the
literature for crop yield prediction and water management. It provides insights
into how these methods can improve decision-making in agricultural production
systems. This work will serve as a compendium for those interested in yield
prediction in terms of primary literature but, most importantly, what
approaches can be used for real-time and robust prediction.
|
[
"cs.LG",
"cs.AI",
"cs.NI",
"cs.SE"
] | false |
2306.04595
|
2023-06-07T16:49:03Z
|
Generalization Across Observation Shifts in Reinforcement Learning
|
[
"Anuj Mahajan",
"Amy Zhang"
] |
Learning policies which are robust to changes in the environment are critical
for real world deployment of Reinforcement Learning agents. They are also
necessary for achieving good generalization across environment shifts. We focus
on bisimulation metrics, which provide a powerful means for abstracting task
relevant components of the observation and learning a succinct representation
space for training the agent using reinforcement learning. In this work, we
extend the bisimulation framework to also account for context dependent
observation shifts. Specifically, we focus on the simulator based learning
setting and use alternate observations to learn a representation space which is
invariant to observation shifts using a novel bisimulation based objective.
This allows us to deploy the agent to varying observation settings during test
time and generalize to unseen scenarios. We further provide novel theoretical
bounds for simulator fidelity and performance transfer guarantees for using a
learnt policy to unseen shifts. Empirical analysis on the high-dimensional
image based control domains demonstrates the efficacy of our method.
|
[
"cs.LG",
"cs.AI",
"cs.RO"
] | false |
2306.04662
|
2023-06-07T02:32:45Z
|
Understanding Place Identity with Generative AI
|
[
"Kee Moon Jang",
"Junda Chen",
"Yuhao Kang",
"Junghwan Kim",
"Jinhyung Lee",
"Fábio Duarte"
] |
Researchers are constantly leveraging new forms of data with the goal of
understanding how people perceive the built environment and build the
collective place identity of cities. Latest advancements in generative
artificial intelligence (AI) models have enabled the production of realistic
representations learned from vast amounts of data. In this study, we aim to
test the potential of generative AI as the source of textual and visual
information in capturing the place identity of cities assessed by filtered
descriptions and images. We asked questions on the place identity of a set of
31 global cities to two generative AI models, ChatGPT and DALL-E2. Since
generative AI has raised ethical concerns regarding its trustworthiness, we
performed cross-validation to examine whether the results show similar patterns
to real urban settings. In particular, we compared the outputs with Wikipedia
data for text and images searched from Google for image. Our results indicate
that generative AI models have the potential to capture the collective image of
cities that can make them distinguishable. This study is among the first
attempts to explore the capabilities of generative AI in understanding human
perceptions of the built environment. It contributes to urban design literature
by discussing future research opportunities and potential limitations.
|
[
"cs.LG",
"cs.CY",
"cs.HC",
"cs.SI"
] | false |
2306.04781
|
2023-06-07T21:02:20Z
|
Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A
Cooperative Deep Reinforcement Learning Approach
|
[
"Diego Patiño",
"Siddharth Mayya",
"Juan Calderon",
"Kostas Daniilidis",
"David Saldaña"
] |
Aerial operation in turbulent environments is a challenging problem due to
the chaotic behavior of the flow. This problem is made even more complex when a
team of aerial robots is trying to achieve coordinated motion in turbulent wind
conditions. In this paper, we present a novel multi-robot controller to
navigate in turbulent flows, decoupling the trajectory-tracking control from
the turbulence compensation via a nested control architecture. Unlike previous
works, our method does not learn to compensate for the air-flow at a specific
time and space. Instead, our method learns to compensate for the flow based on
its effect on the team. This is made possible via a deep reinforcement learning
approach, implemented via a Graph Convolutional Neural Network (GCNN)-based
architecture, which enables robots to achieve better wind compensation by
processing the spatial-temporal correlation of wind flows across the team. Our
approach scales well to large robot teams -- as each robot only uses
information from its nearest neighbors -- , and generalizes well to robot teams
larger than seen in training. Simulated experiments demonstrate how information
sharing improves turbulence compensation in a team of aerial robots and
demonstrate the flexibility of our method over different team configurations.
|
[
"cs.RO",
"cs.LG",
"cs.MA"
] | false |
2306.04835
|
2023-06-07T23:40:18Z
|
Empowering Counterfactual Reasoning over Graph Neural Networks through
Inductivity
|
[
"Samidha Verma",
"Burouj Armgaan",
"Sourav Medya",
"Sayan Ranu"
] |
Graph neural networks (GNNs) have various practical applications, such as
drug discovery, recommendation engines, and chip design. However, GNNs lack
transparency as they cannot provide understandable explanations for their
predictions. To address this issue, counterfactual reasoning is used. The main
goal is to make minimal changes to the input graph of a GNN in order to alter
its prediction. While several algorithms have been proposed for counterfactual
explanations of GNNs, most of them have two main drawbacks. Firstly, they only
consider edge deletions as perturbations. Secondly, the counterfactual
explanation models are transductive, meaning they do not generalize to unseen
data. In this study, we introduce an inductive algorithm called INDUCE, which
overcomes these limitations. By conducting extensive experiments on several
datasets, we demonstrate that incorporating edge additions leads to better
counterfactual results compared to the existing methods. Moreover, the
inductive modeling approach allows INDUCE to directly predict counterfactual
perturbations without requiring instance-specific training. This results in
significant computational speed improvements compared to baseline methods and
enables scalable counterfactual analysis for GNNs.
|
[
"cs.LG",
"cs.AI",
"cs.SI"
] | false |
2306.05294
|
2023-06-07T08:18:56Z
|
Deep Learning with Partially Labeled Data for Radio Map Reconstruction
|
[
"Alkesandra Malkova",
"Massih-Reza Amini",
"Benoit Denis",
"Christophe Villien"
] |
In this paper, we address the problem of Received Signal Strength map
reconstruction based on location-dependent radio measurements and utilizing
side knowledge about the local region; for example, city plan, terrain height,
gateway position. Depending on the quantity of such prior side information, we
employ Neural Architecture Search to find an optimized Neural Network model
with the best architecture for each of the supposed settings. We demonstrate
that using additional side information enhances the final accuracy of the
Received Signal Strength map reconstruction on three datasets that correspond
to three major cities, particularly in sub-areas near the gateways where larger
variations of the average received signal power are typically observed.
|
[
"eess.SP",
"cs.IT",
"cs.LG",
"math.IT"
] | false |
2306.04730
|
2023-06-07T18:49:19Z
|
Stochastic Natural Thresholding Algorithms
|
[
"Rachel Grotheer",
"Shuang Li",
"Anna Ma",
"Deanna Needell",
"Jing Qin"
] |
Sparse signal recovery is one of the most fundamental problems in various
applications, including medical imaging and remote sensing. Many greedy
algorithms based on the family of hard thresholding operators have been
developed to solve the sparse signal recovery problem. More recently, Natural
Thresholding (NT) has been proposed with improved computational efficiency.
This paper proposes and discusses convergence guarantees for stochastic natural
thresholding algorithms by extending the NT from the deterministic version with
linear measurements to the stochastic version with a general objective
function. We also conduct various numerical experiments on linear and nonlinear
measurements to demonstrate the performance of StoNT.
|
[
"eess.SP",
"cs.LG",
"cs.NA",
"math.NA",
"math.OC",
"stat.ML"
] | false |
2306.04842
|
2023-06-08T00:28:22Z
|
InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene
Understanding
|
[
"Hanrong Ye",
"Dan Xu"
] |
Multi-task scene understanding aims to design models that can simultaneously
predict several scene understanding tasks with one versatile model. Previous
studies typically process multi-task features in a more local way, and thus
cannot effectively learn spatially global and cross-task interactions, which
hampers the models' ability to fully leverage the consistency of various tasks
in multi-task learning. To tackle this problem, we propose an Inverted Pyramid
multi-task Transformer, capable of modeling cross-task interaction among
spatial features of different tasks in a global context. Specifically, we first
utilize a transformer encoder to capture task-generic features for all tasks.
And then, we design a transformer decoder to establish spatial and cross-task
interaction globally, and a novel UP-Transformer block is devised to increase
the resolutions of multi-task features gradually and establish cross-task
interaction at different scales. Furthermore, two types of Cross-Scale
Self-Attention modules, i.e., Fusion Attention and Selective Attention, are
proposed to efficiently facilitate cross-task interaction across different
feature scales. An Encoder Feature Aggregation strategy is further introduced
to better model multi-scale information in the decoder. Comprehensive
experiments on several 2D/3D multi-task benchmarks clearly demonstrate our
proposal's effectiveness, establishing significant state-of-the-art
performances.
|
[
"cs.CV"
] | false |
2306.04849
|
2023-06-08T00:57:09Z
|
ScaleDet: A Scalable Multi-Dataset Object Detector
|
[
"Yanbei Chen",
"Manchen Wang",
"Abhay Mittal",
"Zhenlin Xu",
"Paolo Favaro",
"Joseph Tighe",
"Davide Modolo"
] |
Multi-dataset training provides a viable solution for exploiting
heterogeneous large-scale datasets without extra annotation cost. In this work,
we propose a scalable multi-dataset detector (ScaleDet) that can scale up its
generalization across datasets when increasing the number of training datasets.
Unlike existing multi-dataset learners that mostly rely on manual relabelling
efforts or sophisticated optimizations to unify labels across datasets, we
introduce a simple yet scalable formulation to derive a unified semantic label
space for multi-dataset training. ScaleDet is trained by visual-textual
alignment to learn the label assignment with label semantic similarities across
datasets. Once trained, ScaleDet can generalize well on any given upstream and
downstream datasets with seen and unseen classes. We conduct extensive
experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and
13 datasets from Object Detection in the Wild (ODinW) as downstream datasets.
Our results show that ScaleDet achieves compelling strong model performance
with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on
OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the
same backbone.
|
[
"cs.CV"
] | false |
2306.04927
|
2023-06-08T04:18:31Z
|
An Efficient Transformer for Simultaneous Learning of BEV and Lane
Representations in 3D Lane Detection
|
[
"Ziye Chen",
"Kate Smith-Miles",
"Bo Du",
"Guoqi Qian",
"Mingming Gong"
] |
Accurately detecting lane lines in 3D space is crucial for autonomous
driving. Existing methods usually first transform image-view features into
bird-eye-view (BEV) by aid of inverse perspective mapping (IPM), and then
detect lane lines based on the BEV features. However, IPM ignores the changes
in road height, leading to inaccurate view transformations. Additionally, the
two separate stages of the process can cause cumulative errors and increased
complexity. To address these limitations, we propose an efficient transformer
for 3D lane detection. Different from the vanilla transformer, our model
contains a decomposed cross-attention mechanism to simultaneously learn lane
and BEV representations. The mechanism decomposes the cross-attention between
image-view and BEV features into the one between image-view and lane features,
and the one between lane and BEV features, both of which are supervised with
ground-truth lane lines. Our method obtains 2D and 3D lane predictions by
applying the lane features to the image-view and BEV features, respectively.
This allows for a more accurate view transformation than IPM-based methods, as
the view transformation is learned from data with a supervised cross-attention.
Additionally, the cross-attention between lane and BEV features enables them to
adjust to each other, resulting in more accurate lane detection than the two
separate stages. Finally, the decomposed cross-attention is more efficient than
the original one. Experimental results on OpenLane and ONCE-3DLanes demonstrate
the state-of-the-art performance of our method.
|
[
"cs.CV"
] | false |
2306.04957
|
2023-06-08T06:15:13Z
|
IFaceUV: Intuitive Motion Facial Image Generation by Identity
Preservation via UV map
|
[
"Hansol Lee",
"Yunhoe Ku",
"Eunseo Kim",
"Seungryul Baek"
] |
Reenacting facial images is an important task that can find numerous
applications. We proposed IFaceUV, a fully differentiable pipeline that
properly combines 2D and 3D information to conduct the facial reenactment task.
The three-dimensional morphable face models (3DMMs) and corresponding UV maps
are utilized to intuitively control facial motions and textures, respectively.
Two-dimensional techniques based on 2D image warping is further required to
compensate for missing components of the 3DMMs such as backgrounds, ear, hair
and etc. In our pipeline, we first extract 3DMM parameters and corresponding UV
maps from source and target images. Then, initial UV maps are refined by the UV
map refinement network and it is rendered to the image with the motion
manipulated 3DMM parameters. In parallel, we warp the source image according to
the 2D flow field obtained from the 2D warping network. Rendered and warped
images are combined in the final editing network to generate the final
reenactment image. Additionally, we tested our model for the audio-driven
facial reenactment task. Extensive qualitative and quantitative experiments
illustrate the remarkable performance of our method compared to other
state-of-the-art methods.
|
[
"cs.CV"
] | false |
2306.05061
|
2023-06-08T09:24:46Z
|
A Dynamic Feature Interaction Framework for Multi-task Visual Perception
|
[
"Yuling Xi",
"Hao Chen",
"Ning Wang",
"Peng Wang",
"Yanning Zhang",
"Chunhua Shen",
"Yifan Liu"
] |
Multi-task visual perception has a wide range of applications in scene
understanding such as autonomous driving. In this work, we devise an efficient
unified framework to solve multiple common perception tasks, including instance
segmentation, semantic segmentation, monocular 3D detection, and depth
estimation. Simply sharing the same visual feature representations for these
tasks impairs the performance of tasks, while independent task-specific feature
extractors lead to parameter redundancy and latency. Thus, we design two
feature-merge branches to learn feature basis, which can be useful to, and thus
shared by, multiple perception tasks. Then, each task takes the corresponding
feature basis as the input of the prediction task head to fulfill a specific
task. In particular, one feature merge branch is designed for instance-level
recognition the other for dense predictions. To enhance inter-branch
communication, the instance branch passes pixel-wise spatial information of
each instance to the dense branch using efficient dynamic convolution
weighting. Moreover, a simple but effective dynamic routing mechanism is
proposed to isolate task-specific features and leverage common properties among
tasks. Our proposed framework, termed D2BNet, demonstrates a unique approach to
parameter-efficient predictions for multi-task perception. In addition, as
tasks benefit from co-training with each other, our solution achieves on par
results on partially labeled settings on nuScenes and outperforms previous
works for 3D detection and depth estimation on the Cityscapes dataset with full
supervision.
|
[
"cs.CV"
] | false |
2306.05107
|
2023-06-08T11:15:04Z
|
Unsupervised augmentation optimization for few-shot medical image
segmentation
|
[
"Quan Quan",
"Shang Zhao",
"Qingsong Yao",
"Heqin Zhu",
"S. Kevin Zhou"
] |
The augmentation parameters matter to few-shot semantic segmentation since
they directly affect the training outcome by feeding the networks with varying
perturbated samples. However, searching optimal augmentation parameters for
few-shot segmentation models without annotations is a challenge that current
methods fail to address. In this paper, we first propose a framework to
determine the ``optimal'' parameters without human annotations by solving a
distribution-matching problem between the intra-instance and intra-class
similarity distribution, with the intra-instance similarity describing the
similarity between the original sample of a particular anatomy and its
augmented ones and the intra-class similarity representing the similarity
between the selected sample and the others in the same class. Extensive
experiments demonstrate the superiority of our optimized augmentation in
boosting few-shot segmentation models. We greatly improve the top competing
method by 1.27\% and 1.11\% on Abd-MRI and Abd-CT datasets, respectively, and
even achieve a significant improvement for SSL-ALP on the left kidney by 3.39\%
on the Abd-CT dataset.
|
[
"cs.CV"
] | false |
2306.05129
|
2023-06-08T11:54:37Z
|
Focus for Free in Density-Based Counting
|
[
"Zenglin Shi",
"Pascal Mettes",
"Cees G. M. Snoek"
] |
This work considers supervised learning to count from images and their
corresponding point annotations. Where density-based counting methods typically
use the point annotations only to create Gaussian-density maps, which act as
the supervision signal, the starting point of this work is that point
annotations have counting potential beyond density map generation. We introduce
two methods that repurpose the available point annotations to enhance counting
performance. The first is a counting-specific augmentation that leverages point
annotations to simulate occluded objects in both input and density images to
enhance the network's robustness to occlusions. The second method, foreground
distillation, generates foreground masks from the point annotations, from which
we train an auxiliary network on images with blacked-out backgrounds. By doing
so, it learns to extract foreground counting knowledge without interference
from the background. These methods can be seamlessly integrated with existing
counting advances and are adaptable to different loss functions. We demonstrate
complementary effects of the approaches, allowing us to achieve robust counting
results even in challenging scenarios such as background clutter, occlusion,
and varying crowd densities. Our proposed approach achieves strong counting
results on multiple datasets, including ShanghaiTech Part\_A and Part\_B,
UCF\_QNRF, JHU-Crowd++, and NWPU-Crowd.
|
[
"cs.CV"
] | false |
2306.05145
|
2023-06-08T12:12:02Z
|
Variable Radiance Field for Real-Life Category-Specifc Reconstruction
from Single Image
|
[
"Kun Wang",
"Zhiqiang Yan",
"Zhenyu Zhang",
"Xiang Li",
"Jun Li",
"Jian Yang"
] |
Reconstructing category-specific objects from a single image is a challenging
task that requires inferring the geometry and appearance of an object from a
limited viewpoint. Existing methods typically rely on local feature retrieval
based on re-projection with known camera intrinsic, which are slow and prone to
distortion at viewpoints distant from the input image. In this paper, we
present Variable Radiance Field (VRF), a novel framework that can efficiently
reconstruct category-specific objects from a single image without known camera
parameters. Our key contributions are: (1) We parameterize the geometry and
appearance of the object using a multi-scale global feature extractor, which
avoids frequent point-wise feature retrieval and camera dependency. We also
propose a contrastive learning-based pretraining strategy to improve the
feature extractor. (2) We reduce the geometric complexity of the object by
learning a category template, and use hypernetworks to generate a small neural
radiance field for fast and instance-specific rendering. (3) We align each
training instance to the template space using a learned similarity
transformation, which enables semantic-consistent learning across different
objects. We evaluate our method on the CO3D dataset and show that it
outperforms existing methods in terms of quality and speed. We also demonstrate
its applicability to shape interpolation and object placement tasks.
|
[
"cs.CV"
] | false |
2306.05147
|
2023-06-08T12:15:16Z
|
Human Action Recognition in Egocentric Perspective Using 2D Object and
Hands Pose
|
[
"Wiktor Mucha",
"Martin Kampel"
] |
Egocentric action recognition is essential for healthcare and assistive
technology that relies on egocentric cameras because it allows for the
automatic and continuous monitoring of activities of daily living (ADLs)
without requiring any conscious effort from the user. This study explores the
feasibility of using 2D hand and object pose information for egocentric action
recognition. While current literature focuses on 3D hand pose information, our
work shows that using 2D skeleton data is a promising approach for hand-based
action classification, might offer privacy enhancement, and could be less
computationally demanding. The study uses a state-of-the-art transformer-based
method to classify sequences and achieves validation results of 94%,
outperforming other existing solutions. The accuracy of the test subset drops
to 76%, indicating the need for further generalization improvement. This
research highlights the potential of 2D hand and object pose information for
action recognition tasks and offers a promising alternative to 3D-based
methods.
|
[
"cs.CV"
] | false |
2306.05236
|
2023-06-08T14:33:41Z
|
Population-Based Evolutionary Gaming for Unsupervised Person
Re-identification
|
[
"Yunpeng Zhai",
"Peixi Peng",
"Mengxi Jia",
"Shiyong Li",
"Weiqiang Chen",
"Xuesong Gao",
"Yonghong Tian"
] |
Unsupervised person re-identification has achieved great success through the
self-improvement of individual neural networks. However, limited by the lack of
diversity of discriminant information, a single network has difficulty learning
sufficient discrimination ability by itself under unsupervised conditions. To
address this limit, we develop a population-based evolutionary gaming (PEG)
framework in which a population of diverse neural networks is trained
concurrently through selection, reproduction, mutation, and population mutual
learning iteratively. Specifically, the selection of networks to preserve is
modeled as a cooperative game and solved by the best-response dynamics, then
the reproduction and mutation are implemented by cloning and fluctuating
hyper-parameters of networks to learn more diversity, and population mutual
learning improves the discrimination of networks by knowledge distillation from
each other within the population. In addition, we propose a cross-reference
scatter (CRS) to approximately evaluate re-ID models without labeled samples
and adopt it as the criterion of network selection in PEG. CRS measures a
model's performance by indirectly estimating the accuracy of its predicted
pseudo-labels according to the cohesion and separation of the feature space.
Extensive experiments demonstrate that (1) CRS approximately measures the
performance of models without labeled samples; (2) and PEG produces new
state-of-the-art accuracy for person re-identification, indicating the great
potential of population-based network cooperative training for unsupervised
learning.
|
[
"cs.CV"
] | false |
2306.05262
|
2023-06-08T15:03:47Z
|
EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving
Object
|
[
"Hyunseo Kim",
"Hye Jung Yoon",
"Minji Kim",
"Dong-Sig Han",
"Byoung-Tak Zhang"
] |
Current robotic hand manipulation narrowly operates with objects in
predictable positions in limited environments. Thus, when the location of the
target object deviates severely from the expected location, a robot sometimes
responds in an unexpected way, especially when it operates with a human. For
safe robot operation, we propose the EXit-aware Object Tracker (EXOT) on a
robot hand camera that recognizes an object's absence during manipulation. The
robot decides whether to proceed by examining the tracker's bounding box output
containing the target object. We adopt an out-of-distribution classifier for
more accurate object recognition since trackers can mistrack a background as a
target object. To the best of our knowledge, our method is the first approach
of applying an out-of-distribution classification technique to a tracker
output. We evaluate our method on the first-person video benchmark dataset,
TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e
robot. Then we test our tracker on the UR5e robot in real-time with a
conveyor-belt sushi task, to examine the tracker's ability to track target
dishes and to determine the exit status. Our tracker shows 38% higher
exit-aware performance than a baseline method. The dataset and the code will be
released at https://github.com/hskAlena/EXOT.
|
[
"cs.CV"
] | false |
2306.05303
|
2023-06-08T15:49:30Z
|
Enhance-NeRF: Multiple Performance Evaluation for Neural Radiance Fields
|
[
"Qianqiu Tan",
"Tao Liu",
"Yinling Xie",
"Shuwan Yu",
"Baohua Zhang"
] |
The quality of three-dimensional reconstruction is a key factor affecting the
effectiveness of its application in areas such as virtual reality (VR) and
augmented reality (AR) technologies. Neural Radiance Fields (NeRF) can generate
realistic images from any viewpoint. It simultaneously reconstructs the shape,
lighting, and materials of objects, and without surface defects, which breaks
down the barrier between virtuality and reality. The potential spatial
correspondences displayed by NeRF between reconstructed scenes and real-world
scenes offer a wide range of practical applications possibilities. Despite
significant progress in 3D reconstruction since NeRF were introduced, there
remains considerable room for exploration and experimentation. NeRF-based
models are susceptible to interference issues caused by colored "fog" noise.
Additionally, they frequently encounter instabilities and failures while
attempting to reconstruct unbounded scenes. Moreover, the model takes a
significant amount of time to converge, making it even more challenging to use
in such scenarios. Our approach, coined Enhance-NeRF, which adopts joint color
to balance low and high reflectivity objects display, utilizes a decoding
architecture with prior knowledge to improve recognition, and employs
multi-layer performance evaluation mechanisms to enhance learning capacity. It
achieves reconstruction of outdoor scenes within one hour under single-card
condition. Based on experimental results, Enhance-NeRF partially enhances
fitness capability and provides some support to outdoor scene reconstruction.
The Enhance-NeRF method can be used as a plug-and-play component, making it
easy to integrate with other NeRF-based models. The code is available at:
https://github.com/TANQIanQ/Enhance-NeRF
|
[
"cs.CV"
] | false |
2306.05311
|
2023-06-08T16:00:04Z
|
Predictive Modeling of Equine Activity Budgets Using a 3D Skeleton
Reconstructed from Surveillance Recordings
|
[
"Ernest Pokropek",
"Sofia Broomé",
"Pia Haubro Andersen",
"Hedvig Kjellström"
] |
In this work, we present a pipeline to reconstruct the 3D pose of a horse
from 4 simultaneous surveillance camera recordings. Our environment poses
interesting challenges to tackle, such as limited field view of the cameras and
a relatively closed and small environment. The pipeline consists of training a
2D markerless pose estimation model to work on every viewpoint, then applying
it to the videos and performing triangulation. We present numerical evaluation
of the results (error analysis), as well as show the utility of the achieved
poses in downstream tasks of selected behavioral predictions. Our analysis of
the predictive model for equine behavior showed a bias towards pain-induced
horses, which aligns with our understanding of how behavior varies across
painful and healthy subjects.
|
[
"cs.CV"
] | false |
2306.05341
|
2023-06-08T16:45:16Z
|
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic
Permafrost Features
|
[
"Wenwen Li",
"Chia-Yu Hsu",
"Sizhe Wang",
"Chandi Witharana",
"Anna Liljedahl"
] |
This paper introduces a real-time GeoAI workflow for large-scale image
analysis and the segmentation of Arctic permafrost features at a
fine-granularity. Very high-resolution (0.5m) commercial imagery is used in
this analysis. To achieve real-time prediction, our workflow employs a
lightweight, deep learning-based instance segmentation model, SparseInst, which
introduces and uses Instance Activation Maps to accurately locate the position
of objects within the image scene. Experimental results show that the model can
achieve better accuracy of prediction at a much faster inference speed than the
popular Mask-RCNN model.
|
[
"cs.CV"
] | false |
2306.05356
|
2023-06-08T17:01:14Z
|
ReliableSwap: Boosting General Face Swapping Via Reliable Supervision
|
[
"Ge Yuan",
"Maomao Li",
"Yong Zhang",
"Huicheng Zheng"
] |
Almost all advanced face swapping approaches use reconstruction as the proxy
task, i.e., supervision only exists when the target and source belong to the
same person. Otherwise, lacking pixel-level supervision, these methods struggle
for source identity preservation. This paper proposes to construct reliable
supervision, dubbed cycle triplets, which serves as the image-level guidance
when the source identity differs from the target one during training.
Specifically, we use face reenactment and blending techniques to synthesize the
swapped face from real images in advance, where the synthetic face preserves
source identity and target attributes. However, there may be some artifacts in
such a synthetic face. To avoid the potential artifacts and drive the
distribution of the network output close to the natural one, we reversely take
synthetic images as input while the real face as reliable supervision during
the training stage of face swapping. Besides, we empirically find that the
existing methods tend to lose lower-face details like face shape and mouth from
the source. This paper additionally designs a FixerNet, providing
discriminative embeddings of lower faces as an enhancement. Our face swapping
framework, named ReliableSwap, can boost the performance of any existing face
swapping network with negligible overhead. Extensive experiments demonstrate
the efficacy of our ReliableSwap, especially in identity preservation. The
project page is https://reliable-swap.github.io/.
|
[
"cs.CV"
] | false |
2306.05390
|
2023-06-08T17:44:21Z
|
HQ-50K: A Large-scale, High-quality Dataset for Image Restoration
|
[
"Qinhong Yang",
"Dongdong Chen",
"Zhentao Tan",
"Qiankun Liu",
"Qi Chu",
"Jianmin Bao",
"Lu Yuan",
"Gang Hua",
"Nenghai Yu"
] |
This paper introduces a new large-scale image restoration dataset, called
HQ-50K, which contains 50,000 high-quality images with rich texture details and
semantic diversity. We analyze existing image restoration datasets from five
different perspectives, including data scale, resolution, compression rates,
texture details, and semantic coverage. However, we find that all of these
datasets are deficient in some aspects. In contrast, HQ-50K considers all of
these five aspects during the data curation process and meets all requirements.
We also present a new Degradation-Aware Mixture of Expert (DAMoE) model, which
enables a single model to handle multiple corruption types and unknown levels.
Our extensive experiments demonstrate that HQ-50K consistently improves the
performance on various image restoration tasks, such as super-resolution,
denoising, dejpeg, and deraining. Furthermore, our proposed DAMoE, trained on
our \dataset, outperforms existing state-of-the-art unified models designed for
multiple restoration tasks and levels. The dataset and code are available at
\url{https://github.com/littleYaang/HQ-50K}.
|
[
"cs.CV"
] | false |
2306.05410
|
2023-06-08T17:56:22Z
|
LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs
|
[
"Zezhou Cheng",
"Carlos Esteves",
"Varun Jampani",
"Abhishek Kar",
"Subhransu Maji",
"Ameesh Makadia"
] |
A critical obstacle preventing NeRF models from being deployed broadly in the
wild is their reliance on accurate camera poses. Consequently, there is growing
interest in extending NeRF models to jointly optimize camera poses and scene
representation, which offers an alternative to off-the-shelf SfM pipelines
which have well-understood failure modes. Existing approaches for unposed NeRF
operate under limited assumptions, such as a prior pose distribution or coarse
pose initialization, making them less effective in a general setting. In this
work, we propose a novel approach, LU-NeRF, that jointly estimates camera poses
and neural radiance fields with relaxed assumptions on pose configuration. Our
approach operates in a local-to-global manner, where we first optimize over
local subsets of the data, dubbed mini-scenes. LU-NeRF estimates local pose and
geometry for this challenging few-shot task. The mini-scene poses are brought
into a global reference frame through a robust pose synchronization step, where
a final global optimization of pose and scene can be performed. We show our
LU-NeRF pipeline outperforms prior attempts at unposed NeRF without making
restrictive assumptions on the pose prior. This allows us to operate in the
general SE(3) pose setting, unlike the baselines. Our results also indicate our
model can be complementary to feature-based SfM pipelines as it compares
favorably to COLMAP on low-texture and low-resolution images.
|
[
"cs.CV"
] | true |
2306.05416
|
2023-06-08T17:58:45Z
|
Tracking Objects with 3D Representation from Videos
|
[
"Jiawei He",
"Lue Fan",
"Yuqi Wang",
"Yuntao Chen",
"Zehao Huang",
"Naiyan Wang",
"Zhaoxiang Zhang"
] |
Data association is a knotty problem for 2D Multiple Object Tracking due to
the object occlusion. However, in 3D space, data association is not so hard.
Only with a 3D Kalman Filter, the online object tracker can associate the
detections from LiDAR. In this paper, we rethink the data association in 2D MOT
and utilize the 3D object representation to separate each object in the feature
space. Unlike the existing depth-based MOT methods, the 3D object
representation can be jointly learned with the object association module.
Besides, the object's 3D representation is learned from the video and
supervised by the 2D tracking labels without additional manual annotations from
LiDAR or pretrained depth estimator. With 3D object representation learning
from Pseudo 3D object labels in monocular videos, we propose a new 2D MOT
paradigm, called P3DTrack. Extensive experiments show the effectiveness of our
method. We achieve new state-of-the-art performance on the large-scale Waymo
Open Dataset.
|
[
"cs.CV"
] | false |
2306.05421
|
2023-06-08T17:59:09Z
|
Stochastic Multi-Person 3D Motion Forecasting
|
[
"Sirui Xu",
"Yu-Xiong Wang",
"Liang-Yan Gui"
] |
This paper aims to deal with the ignored real-world complexities in prior
work on human motion forecasting, emphasizing the social properties of
multi-person motion, the diversity of motion and social interactions, and the
complexity of articulated motion. To this end, we introduce a novel task of
stochastic multi-person 3D motion forecasting. We propose a dual-level
generative modeling framework that separately models independent individual
motion at the local level and social interactions at the global level. Notably,
this dual-level modeling mechanism can be achieved within a shared generative
model, through introducing learnable latent codes that represent intents of
future motion and switching the codes' modes of operation at different levels.
Our framework is general; we instantiate it with different generative models,
including generative adversarial networks and diffusion models, and various
multi-person forecasting models. Extensive experiments on CMU-Mocap, MuPoTS-3D,
and SoMoF benchmarks show that our approach produces diverse and accurate
multi-person predictions, significantly outperforming the state of the art.
|
[
"cs.CV"
] | false |
2306.05424
|
2023-06-08T17:59:56Z
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and
Language Models
|
[
"Muhammad Maaz",
"Hanoona Rasheed",
"Salman Khan",
"Fahad Shahbaz Khan"
] |
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
[
"cs.CV"
] | true |
2306.05428
|
2023-06-08T17:59:59Z
|
Background Prompting for Improved Object Depth
|
[
"Manel Baradad",
"Yuanzhen Li",
"Forrester Cole",
"Michael Rubinstein",
"Antonio Torralba",
"William T. Freeman",
"Varun Jampani"
] |
Estimating the depth of objects from a single image is a valuable task for
many vision, robotics, and graphics applications. However, current methods
often fail to produce accurate depth for objects in diverse scenes. In this
work, we propose a simple yet effective Background Prompting strategy that
adapts the input object image with a learned background. We learn the
background prompts only using small-scale synthetic object datasets. To infer
object depth on a real image, we place the segmented object into the learned
background prompt and run off-the-shelf depth networks. Background Prompting
helps the depth networks focus on the foreground object, as they are made
invariant to background variations. Moreover, Background Prompting minimizes
the domain gap between synthetic and real object images, leading to better
sim2real generalization than simple finetuning. Results on multiple synthetic
and real datasets demonstrate consistent improvements in real object depths for
a variety of existing depth networks. Code and optimized background prompts can
be found at: https://mbaradad.github.io/depth_prompt.
|
[
"cs.CV"
] | true |
2306.05442
|
2023-06-08T12:24:04Z
|
FlowFormer: A Transformer Architecture and Its Masked Cost Volume
Autoencoding for Optical Flow
|
[
"Zhaoyang Huang",
"Xiaoyu Shi",
"Chao Zhang",
"Qiang Wang",
"Yijin Li",
"Hongwei Qin",
"Jifeng Dai",
"Xiaogang Wang",
"Hongsheng Li"
] |
This paper introduces a novel transformer-based network architecture,
FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for
pretraining it to tackle the problem of optical flow estimation. FlowFormer
tokenizes the 4D cost-volume built from the source-target image pair and
iteratively refines flow estimation with a cost-volume encoder-decoder
architecture. The cost-volume encoder derives a cost memory with
alternate-group transformer~(AGT) layers in a latent space and the decoder
recurrently decodes flow from the cost memory with dynamic positional cost
queries. On the Sintel benchmark, FlowFormer architecture achieves 1.16 and
2.09 average end-point-error~(AEPE) on the clean and final pass, a 16.5\% and
15.5\% error reduction from the GMA~(1.388 and 2.47). MCVA enhances FlowFormer
by pretraining the cost-volume encoder with a masked autoencoding scheme, which
further unleashes the capability of FlowFormer with unlabeled data. This is
especially critical in optical flow estimation because ground truth flows are
more expensive to acquire than labels in other vision tasks. MCVA improves
FlowFormer all-sided and FlowFormer+MCVA ranks 1st among all published methods
on both Sintel and KITTI-2015 benchmarks and achieves the best generalization
performance. Specifically, FlowFormer+MCVA achieves 1.07 and 1.94 AEPE on the
Sintel benchmark, leading to 7.76\% and 7.18\% error reductions from
FlowFormer.
|
[
"cs.CV"
] | false |
2306.05572
|
2023-06-08T22:07:48Z
|
Merging Deep Learning with Expert Knowledge for Seizure Onset Zone
localization from rs-fMRI in Pediatric Pharmaco Resistant Epilepsy
|
[
"Payal Kamboj",
"Ayan Banerjee",
"Sandeep K. S. Gupta",
"Varina L. Boerwinkle"
] |
Surgical disconnection of Seizure Onset Zones (SOZs) at an early age is an
effective treatment for Pharmaco-Resistant Epilepsy (PRE). Pre-surgical
localization of SOZs with intra-cranial EEG (iEEG) requires safe and effective
depth electrode placement. Resting-state functional Magnetic Resonance Imaging
(rs-fMRI) combined with signal decoupling using independent component (IC)
analysis has shown promising SOZ localization capability that guides iEEG lead
placement. However, SOZ ICs identification requires manual expert sorting of
100s of ICs per patient by the surgical team which limits the reproducibility
and availability of this pre-surgical screening. Automated approaches for SOZ
IC identification using rs-fMRI may use deep learning (DL) that encodes
intricacies of brain networks from scarcely available pediatric data but has
low precision, or shallow learning (SL) expert rule-based inference approaches
that are incapable of encoding the full spectrum of spatial features. This
paper proposes DeepXSOZ that exploits the synergy between DL based spatial
feature and SL based expert knowledge encoding to overcome performance
drawbacks of these strategies applied in isolation. DeepXSOZ is an
expert-in-the-loop IC sorting technique that a) can be configured to either
significantly reduce expert sorting workload or operate with high sensitivity
based on expertise of the surgical team and b) can potentially enable the usage
of rs-fMRI as a low cost outpatient pre-surgical screening tool. Comparison
with state-of-art on 52 children with PRE shows that DeepXSOZ achieves
sensitivity of 89.79%, precision of 93.6% and accuracy of 84.6%, and reduces
sorting effort by 6.7-fold. Knowledge level ablation studies show a pathway
towards maximizing patient outcomes while optimizing the machine-expert
collaboration for various scenarios.
|
[
"cs.CV"
] | false |
2306.04898
|
2023-06-08T03:00:10Z
|
Understanding Masked Autoencoders via Hierarchical Latent Variable
Models
|
[
"Lingjing Kong",
"Martin Q. Ma",
"Guangyi Chen",
"Eric P. Xing",
"Yuejie Chi",
"Louis-Philippe Morency",
"Kun Zhang"
] |
Masked autoencoder (MAE), a simple and effective self-supervised learning
framework based on the reconstruction of masked image regions, has recently
achieved prominent success in a variety of vision tasks. Despite the emergence
of intriguing empirical observations on MAE, a theoretically principled
understanding is still lacking. In this work, we formally characterize and
justify existing empirical insights and provide theoretical guarantees of MAE.
We formulate the underlying data-generating process as a hierarchical latent
variable model and show that under reasonable assumptions, MAE provably
identifies a set of latent variables in the hierarchical model, explaining why
MAE can extract high-level information from pixels. Further, we show how key
hyperparameters in MAE (the masking ratio and the patch size) determine which
true latent variables to be recovered, therefore influencing the level of
semantic information in the representation. Specifically, extremely large or
small masking ratios inevitably lead to low-level representations. Our theory
offers coherent explanations of existing empirical observations and provides
insights for potential empirical improvements and fundamental limitations of
the masking-reconstruction paradigm. We conduct extensive experiments to
validate our theoretical insights.
|
[
"cs.LG",
"cs.CV"
] | false |
2306.04905
|
2023-06-08T03:17:00Z
|
ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation
|
[
"Juntao Jiang",
"Xiyu Chen",
"Guanzhong Tian",
"Yong Liu"
] |
Deep neural networks have been widely used in medical image analysis and
medical image segmentation is one of the most important tasks. U-shaped neural
networks with encoder-decoder are prevailing and have succeeded greatly in
various segmentation tasks. While CNNs treat an image as a grid of pixels in
Euclidean space and Transformers recognize an image as a sequence of patches,
graph-based representation is more generalized and can construct connections
for each part of an image. In this paper, we propose a novel ViG-UNet, a graph
neural network-based U-shaped architecture with the encoder, the decoder, the
bottleneck, and skip connections. The downsampling and upsampling modules are
also carefully designed. The experimental results on ISIC 2016, ISIC 2017 and
Kvasir-SEG datasets demonstrate that our proposed architecture outperforms most
existing classic and state-of-the-art U-shaped networks.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.04938
|
2023-06-08T05:08:32Z
|
Knowledge Detection by Relevant Question and Image Attributes in Visual
Question Answering
|
[
"Param Ahir",
"Dr. Hiteishi Diwanji"
] |
Visual question answering (VQA) is a Multidisciplinary research problem that
pursued through practices of natural language processing and computer vision.
Visual question answering automatically answers natural language questions
according to the content of an image. Some testing questions require external
knowledge to derive a solution. Such knowledge-based VQA uses various methods
to retrieve features of image and text, and combine them to generate the
answer. To generate knowledgebased answers either question dependent or image
dependent knowledge retrieval methods are used. If knowledge about all the
objects in the image is derived, then not all knowledge is relevant to the
question. On other side only question related knowledge may lead to incorrect
answers and over trained model that answers question that is irrelevant to
image. Our proposed method takes image attributes and question features as
input for knowledge derivation module and retrieves only question relevant
knowledge about image objects which can provide accurate answers.
|
[
"cs.CV",
"cs.AI"
] | false |
2306.04947
|
2023-06-08T05:40:07Z
|
Neighborhood Attention Makes the Encoder of ResUNet Stronger for
Accurate Road Extraction
|
[
"Ali Jamali",
"Swalpa Kumar Roy",
"Jonathan Li",
"Pedram Ghamisi"
] |
In the domain of remote sensing image interpretation, road extraction from
high-resolution aerial imagery has already been a hot research topic. Although
deep CNNs have presented excellent results for semantic segmentation, the
efficiency and capabilities of vision transformers are yet to be fully
researched. As such, for accurate road extraction, a deep semantic segmentation
neural network that utilizes the abilities of residual learning, HetConvs,
UNet, and vision transformers, which is called \texttt{ResUNetFormer}, is
proposed in this letter. The developed \texttt{ResUNetFormer} is evaluated on
various cutting-edge deep learning-based road extraction techniques on the
public Massachusetts road dataset. Statistical and visual results demonstrate
the superiority of the \texttt{ResUNetFormer} over the state-of-the-art CNNs
and vision transformers for segmentation. The code will be made available
publicly at \url{https://github.com/aj1365/ResUNetFormer}.
|
[
"cs.CV",
"eess.IV"
] | false |
2306.04988
|
2023-06-08T07:19:27Z
|
StreetSurf: Extending Multi-view Implicit Surface Reconstruction to
Street Views
|
[
"Jianfei Guo",
"Nianchen Deng",
"Xinyang Li",
"Yeqi Bai",
"Botian Shi",
"Chiyu Wang",
"Chenjing Ding",
"Dongliang Wang",
"Yikang Li"
] |
We present a novel multi-view implicit surface reconstruction technique,
termed StreetSurf, that is readily applicable to street view images in
widely-used autonomous driving datasets, such as Waymo-perception sequences,
without necessarily requiring LiDAR data. As neural rendering research expands
rapidly, its integration into street views has started to draw interests.
Existing approaches on street views either mainly focus on novel view synthesis
with little exploration of the scene geometry, or rely heavily on dense LiDAR
data when investigating reconstruction. Neither of them investigates multi-view
implicit surface reconstruction, especially under settings without LiDAR data.
Our method extends prior object-centric neural surface reconstruction
techniques to address the unique challenges posed by the unbounded street views
that are captured with non-object-centric, long and narrow camera trajectories.
We delimit the unbounded space into three parts, close-range, distant-view and
sky, with aligned cuboid boundaries, and adapt cuboid/hyper-cuboid hash-grids
along with road-surface initialization scheme for finer and disentangled
representation. To further address the geometric errors arising from
textureless regions and insufficient viewing angles, we adopt geometric priors
that are estimated using general purpose monocular models. Coupled with our
implementation of efficient and fine-grained multi-stage ray marching strategy,
we achieve state of the art reconstruction quality in both geometry and
appearance within only one to two hours of training time with a single RTX3090
GPU for each street view sequence. Furthermore, we demonstrate that the
reconstructed implicit surfaces have rich potential for various downstream
tasks, including ray tracing and LiDAR simulation.
|
[
"cs.CV",
"cs.GR"
] | false |
2306.05001
|
2023-06-08T07:45:24Z
|
COURIER: Contrastive User Intention Reconstruction for Large-Scale
Pre-Train of Image Features
|
[
"Jia-Qi Yang",
"Chenglei Dai",
"OU Dan",
"Ju Huang",
"De-Chuan Zhan",
"Qingwen Liu",
"Xiaoyi Zeng",
"Yang Yang"
] |
With the development of the multi-media internet, visual characteristics have
become an important factor affecting user interests. Thus, incorporating visual
features is a promising direction for further performance improvements in
click-through rate (CTR) prediction. However, we found that simply injecting
the image embeddings trained with established pre-training methods only has
marginal improvements. We attribute the failure to two reasons: First, The
pre-training methods are designed for well-defined computer vision tasks
concentrating on semantic features, and they cannot learn personalized interest
in recommendations. Secondly, pre-trained image embeddings only containing
semantic information have little information gain, considering we already have
semantic features such as categories and item titles as inputs in the CTR
prediction task. We argue that a pre-training method tailored for
recommendation is necessary for further improvements. To this end, we propose a
recommendation-aware image pre-training method that can learn visual features
from user click histories. Specifically, we propose a user interest
reconstruction module to mine visual features related to user interests from
behavior histories. We further propose a contrastive training method to avoid
collapsing of embedding vectors. We conduct extensive experiments to verify
that our method can learn users' visual interests, and our method achieves
$0.46\%$ improvement in offline AUC and $0.88\%$ improvement in Taobao online
GMV with p-value$<0.01$.
|
[
"cs.CV",
"cs.LG"
] | false |
2306.05056
|
2023-06-08T09:20:51Z
|
Magnitude Attention-based Dynamic Pruning
|
[
"Jihye Back",
"Namhyuk Ahn",
"Jangho Kim"
] |
Existing pruning methods utilize the importance of each weight based on
specified criteria only when searching for a sparse structure but do not
utilize it during training. In this work, we propose a novel approach -
\textbf{M}agnitude \textbf{A}ttention-based Dynamic \textbf{P}runing (MAP)
method, which applies the importance of weights throughout both the forward and
backward paths to explore sparse model structures dynamically. Magnitude
attention is defined based on the magnitude of weights as continuous
real-valued numbers enabling a seamless transition from a redundant to an
effective sparse network by promoting efficient exploration. Additionally, the
attention mechanism ensures more effective updates for important layers within
the sparse network. In later stages of training, our approach shifts from
exploration to exploitation, exclusively updating the sparse model composed of
crucial weights based on the explored structure, resulting in pruned models
that not only achieve performance comparable to dense models but also
outperform previous pruning methods on CIFAR-10/100 and ImageNet.
|
[
"cs.CV",
"cs.LG"
] | false |
2306.05089
|
2023-06-08T10:48:11Z
|
A review of UAV Visual Detection and Tracking Methods
|
[
"Raed Abu Zitar",
"Mohammad Al-Betar",
"Mohamad Ryalat",
"Sofian Kassaymeh"
] |
This paper presents a review of techniques used for the detection and
tracking of UAVs or drones. There are different techniques that depend on
collecting measurements of the position, velocity, and image of the UAV and
then using them in detection and tracking. Hybrid detection techniques are also
presented. The paper is a quick reference for a wide spectrum of methods that
are used in the drone detection process.
|
[
"cs.CV",
"eess.SP"
] | false |
2306.05135
|
2023-06-08T12:02:03Z
|
Does Image Anonymization Impact Computer Vision Training?
|
[
"Håkon Hukkelås",
"Frank Lindseth"
] |
Image anonymization is widely adapted in practice to comply with privacy
regulations in many regions. However, anonymization often degrades the quality
of the data, reducing its utility for computer vision development. In this
paper, we investigate the impact of image anonymization for training computer
vision models on key computer vision tasks (detection, instance segmentation,
and pose estimation). Specifically, we benchmark the recognition drop on common
detection datasets, where we evaluate both traditional and realistic
anonymization for faces and full bodies. Our comprehensive experiments reflect
that traditional image anonymization substantially impacts final model
performance, particularly when anonymizing the full body. Furthermore, we find
that realistic anonymization can mitigate this decrease in performance, where
our experiments reflect a minimal performance drop for face anonymization. Our
study demonstrates that realistic anonymization can enable privacy-preserving
computer vision development with minimal performance degradation across a range
of important computer vision benchmarks.
|
[
"cs.CV",
"cs.AI"
] | false |
2306.05175
|
2023-06-08T13:14:35Z
|
Large-scale Dataset Pruning with Dynamic Uncertainty
|
[
"Muyang He",
"Shuo Yang",
"Tiejun Huang",
"Bo Zhao"
] |
The state of the art of many learning tasks, e.g., image classification, is
advanced by collecting larger datasets and then training larger models on them.
As the outcome, the increasing computational cost is becoming unaffordable. In
this paper, we investigate how to prune the large-scale datasets, and thus
produce an informative subset for training sophisticated deep models with
negligible performance drop. We propose a simple yet effective dataset pruning
method by exploring both the prediction uncertainty and training dynamics. To
our knowledge, this is the first work to study dataset pruning on large-scale
datasets, i.e., ImageNet-1K and ImageNet-21K, and advanced models, i.e., Swin
Transformer and ConvNeXt. Extensive experimental results indicate that our
method outperforms the state of the art and achieves 75% lossless compression
ratio on both ImageNet-1K and ImageNet-21K. The code and pruned datasets are
available at https://github.com/BAAI-DCAI/Dataset-Pruning.
|
[
"cs.LG",
"cs.CV"
] | false |
2306.05196
|
2023-06-08T13:52:41Z
|
Channel prior convolutional attention for medical image segmentation
|
[
"Hejun Huang",
"Zuguo Chen",
"Ying Zou",
"Ming Lu",
"Chaoyang Chen"
] |
Characteristics such as low contrast and significant organ shape variations
are often exhibited in medical images. The improvement of segmentation
performance in medical imaging is limited by the generally insufficient
adaptive capabilities of existing attention mechanisms. An efficient Channel
Prior Convolutional Attention (CPCA) method is proposed in this paper,
supporting the dynamic distribution of attention weights in both channel and
spatial dimensions. Spatial relationships are effectively extracted while
preserving the channel prior by employing a multi-scale depth-wise
convolutional module. The ability to focus on informative channels and
important regions is possessed by CPCA. A segmentation network called CPCANet
for medical image segmentation is proposed based on CPCA. CPCANet is validated
on two publicly available datasets. Improved segmentation performance is
achieved by CPCANet while requiring fewer computational resources through
comparisons with state-of-the-art algorithms. Our code is publicly available at
\url{https://github.com/Cuthbert-Huang/CPCANet}.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.05242
|
2023-06-08T14:41:56Z
|
Efficient Multi-Task Scene Analysis with RGB-D Transformers
|
[
"Söhnke Benedikt Fischedick",
"Daniel Seichter",
"Robin Schmidt",
"Leonard Rabes",
"Horst-Michael Gross"
] |
Scene analysis is essential for enabling autonomous systems, such as mobile
robots, to operate in real-world environments. However, obtaining a
comprehensive understanding of the scene requires solving multiple tasks, such
as panoptic segmentation, instance orientation estimation, and scene
classification. Solving these tasks given limited computing and battery
capabilities on mobile platforms is challenging. To address this challenge, we
introduce an efficient multi-task scene analysis approach, called EMSAFormer,
that uses an RGB-D Transformer-based encoder to simultaneously perform the
aforementioned tasks. Our approach builds upon the previously published
EMSANet. However, we show that the dual CNN-based encoder of EMSANet can be
replaced with a single Transformer-based encoder. To achieve this, we
investigate how information from both RGB and depth data can be effectively
incorporated in a single encoder. To accelerate inference on robotic hardware,
we provide a custom NVIDIA TensorRT extension enabling highly optimization for
our EMSAFormer approach. Through extensive experiments on the commonly used
indoor datasets NYUv2, SUNRGB-D, and ScanNet, we show that our approach
achieves state-of-the-art performance while still enabling inference with up to
39.1 FPS on an NVIDIA Jetson AGX Orin 32 GB.
|
[
"cs.CV",
"cs.RO"
] | false |
2306.05297
|
2023-06-08T15:39:27Z
|
Connectional-Style-Guided Contextual Representation Learning for Brain
Disease Diagnosis
|
[
"Gongshu Wang",
"Ning Jiang",
"Yunxiao Ma",
"Tiantian Liu",
"Duanduan Chen",
"Jinglong Wu",
"Guoqi Li",
"Dong Liang",
"Tianyi Yan"
] |
Structural magnetic resonance imaging (sMRI) has shown great clinical value
and has been widely used in deep learning (DL) based computer-aided brain
disease diagnosis. Previous approaches focused on local shapes and textures in
sMRI that may be significant only within a particular domain. The learned
representations are likely to contain spurious information and have a poor
generalization ability in other diseases and datasets. To facilitate capturing
meaningful and robust features, it is necessary to first comprehensively
understand the intrinsic pattern of the brain that is not restricted within a
single data/task domain. Considering that the brain is a complex connectome of
interlinked neurons, the connectional properties in the brain have strong
biological significance, which is shared across multiple domains and covers
most pathological information. In this work, we propose a connectional style
contextual representation learning model (CS-CRL) to capture the intrinsic
pattern of the brain, used for multiple brain disease diagnosis. Specifically,
it has a vision transformer (ViT) encoder and leverages mask reconstruction as
the proxy task and Gram matrices to guide the representation of connectional
information. It facilitates the capture of global context and the aggregation
of features with biological plausibility. The results indicate that CS-CRL
achieves superior accuracy in multiple brain disease diagnosis tasks across six
datasets and three diseases and outperforms state-of-the-art models.
Furthermore, we demonstrate that CS-CRL captures more brain-network-like
properties, better aggregates features, is easier to optimize and is more
robust to noise, which explains its superiority in theory. Our source code will
be released soon.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.05420
|
2023-06-08T17:59:08Z
|
Scaling Spherical CNNs
|
[
"Carlos Esteves",
"Jean-Jacques Slotine",
"Ameesh Makadia"
] |
Spherical CNNs generalize CNNs to functions on the sphere, by using spherical
convolutions as the main linear operation. The most accurate and efficient way
to compute spherical convolutions is in the spectral domain (via the
convolution theorem), which is still costlier than the usual planar
convolutions. For this reason, applications of spherical CNNs have so far been
limited to small problems that can be approached with low model capacity. In
this work, we show how spherical CNNs can be scaled for much larger problems.
To achieve this, we make critical improvements including novel variants of
common model components, an implementation of core operations to exploit
hardware accelerator characteristics, and application-specific input
representations that exploit the properties of our model. Experiments show our
larger spherical CNNs reach state-of-the-art on several targets of the QM9
molecular benchmark, which was previously dominated by equivariant graph neural
networks, and achieve competitive performance on multiple weather forecasting
tasks. Our code is available at
https://github.com/google-research/spherical-cnn.
|
[
"cs.LG",
"cs.CV"
] | true |
2306.05495
|
2023-06-08T18:33:12Z
|
Is Attentional Channel Processing Design Required? Comprehensive
Analysis Of Robustness Between Vision Transformers And Fully Attentional
Networks
|
[
"Abhishri Ajit Medewar",
"Swanand Ashokrao Kavitkar"
] |
The robustness testing has been performed for standard CNN models and Vision
Transformers, however there is a lack of comprehensive study between the
robustness of traditional Vision Transformers without an extra attentional
channel design and the latest fully attentional network(FAN) models. So in this
paper, we use the ImageNet dataset to compare the robustness of fully
attentional network(FAN) models with traditional Vision Transformers to
understand the role of an attentional channel processing design using white box
attacks and also study the transferability between the same using black box
attacks.
|
[
"cs.CV",
"cs.LG"
] | false |
2306.05544
|
2023-06-08T20:30:55Z
|
BOOT: Data-free Distillation of Denoising Diffusion Models with
Bootstrapping
|
[
"Jiatao Gu",
"Shuangfei Zhai",
"Yizhe Zhang",
"Lingjie Liu",
"Josh Susskind"
] |
Diffusion models have demonstrated excellent potential for generating diverse
images. However, their performance often suffers from slow generation due to
iterative denoising. Knowledge distillation has been recently proposed as a
remedy that can reduce the number of inference steps to one or a few without
significant quality degradation. However, existing distillation methods either
require significant amounts of offline computation for generating synthetic
training data from the teacher model or need to perform expensive online
learning with the help of real data. In this work, we present a novel technique
called BOOT, that overcomes these limitations with an efficient data-free
distillation algorithm. The core idea is to learn a time-conditioned model that
predicts the output of a pre-trained diffusion model teacher given any time
step. Such a model can be efficiently trained based on bootstrapping from two
consecutive sampled steps. Furthermore, our method can be easily adapted to
large-scale text-to-image diffusion models, which are challenging for
conventional methods given the fact that the training sets are often large and
difficult to access. We demonstrate the effectiveness of our approach on
several benchmark datasets in the DDIM setting, achieving comparable generation
quality while being orders of magnitude faster than the diffusion teacher. The
text-to-image results show that the proposed approach is able to handle highly
complex distributions, shedding light on more efficient generative modeling.
|
[
"cs.CV",
"cs.LG"
] | true |
2306.05553
|
2023-06-08T20:52:01Z
|
Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling
for Point Cloud Classification
|
[
"Ashkan Shahbazi",
"Abihith Kothapalli",
"Xinran Liu",
"Robert Sheng",
"Soheil Kolouri"
] |
Learning from set-structured data, such as point clouds, has gained
significant attention from the community. Geometric deep learning provides a
blueprint for designing effective set neural networks by incorporating
permutation symmetry. Of our interest are permutation invariant networks, which
are composed of a permutation equivariant backbone, permutation invariant
global pooling, and regression/classification head. While existing literature
has focused on improving permutation equivariant backbones, the impact of
global pooling is often overlooked. In this paper, we examine the interplay
between permutation equivariant backbones and permutation invariant global
pooling on three benchmark point cloud classification datasets. Our findings
reveal that: 1) complex pooling methods, such as transport-based or
attention-based poolings, can significantly boost the performance of simple
backbones, but the benefits diminish for more complex backbones, 2) even
complex backbones can benefit from pooling layers in low data scenarios, 3)
surprisingly, the choice of pooling layers can have a more significant impact
on the model's performance than adjusting the width and depth of the backbone,
and 4) pairwise combination of pooling layers can significantly improve the
performance of a fixed backbone. Our comprehensive study provides insights for
practitioners to design better permutation invariant set neural networks.
|
[
"cs.CV",
"cs.LG"
] | false |
2306.04955
|
2023-06-08T06:02:39Z
|
Degraded Polygons Raise Fundamental Questions of Neural Network
Perception
|
[
"Leonard Tang",
"Dan Ley"
] |
It is well-known that modern computer vision systems often exhibit behaviors
misaligned with those of humans: from adversarial attacks to image corruptions,
deep learning vision models suffer in a variety of settings that humans capably
handle. In light of these phenomena, here we introduce another, orthogonal
perspective studying the human-machine vision gap. We revisit the task of
recovering images under degradation, first introduced over 30 years ago in the
Recognition-by-Components theory of human vision. Specifically, we study the
performance and behavior of neural networks on the seemingly simple task of
classifying regular polygons at varying orders of degradation along their
perimeters. To this end, we implement the Automated Shape Recoverability Test
for rapidly generating large-scale datasets of perimeter-degraded regular
polygons, modernizing the historically manual creation of image recoverability
experiments. We then investigate the capacity of neural networks to recognize
and recover such degraded shapes when initialized with different priors.
Ultimately, we find that neural networks' behavior on this simple task
conflicts with human behavior, raising a fundamental question of the robustness
and learning capabilities of modern computer vision models.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2306.05012
|
2023-06-08T08:04:56Z
|
Sequence-to-Sequence Model with Transformer-based Attention Mechanism
and Temporal Pooling for Non-Intrusive Load Monitoring
|
[
"Mohammad Irani Azad",
"Roozbeh Rajabi",
"Abouzar Estebsari"
] |
This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a
transformer-based attention mechanism and temporal pooling for Non-Intrusive
Load Monitoring (NILM) of smart buildings. The paper aims to improve the
accuracy of NILM by using a deep learning-based method. The proposed method
uses a Seq2Seq model with a transformer-based attention mechanism to capture
the long-term dependencies of NILM data. Additionally, temporal pooling is used
to improve the model's accuracy by capturing both the steady-state and
transient behavior of appliances. The paper evaluates the proposed method on a
publicly available dataset and compares the results with other state-of-the-art
NILM techniques. The results demonstrate that the proposed method outperforms
the existing methods in terms of both accuracy and computational efficiency.
|
[
"eess.SP",
"cs.CV",
"cs.LG"
] | false |
2306.05045
|
2023-06-08T08:55:16Z
|
Spain on Fire: A novel wildfire risk assessment model based on image
satellite processing and atmospheric information
|
[
"Helena Liz-López",
"Javier Huertas-Tato",
"Jorge Pérez-Aracil",
"Carlos Casanova-Mateo",
"Julia Sanz-Justo",
"David Camacho"
] |
Each year, wildfires destroy larger areas of Spain, threatening numerous
ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour
of individuals is unpredictable. However, atmospheric and environmental
variables affect the spread of wildfires, and they can be analysed by using
deep learning. In order to mitigate the damage of these events we proposed the
novel Wildfire Assessment Model (WAM). Our aim is to anticipate the economic
and ecological impact of a wildfire, assisting managers resource allocation and
decision making for dangerous regions in Spain, Castilla y Le\'on and
Andaluc\'ia. The WAM uses a residual-style convolutional network architecture
to perform regression over atmospheric variables and the greenness index,
computing necessary resources, the control and extinction time, and the
expected burnt surface area. It is first pre-trained with self-supervision over
100,000 examples of unlabelled data with a masked patch prediction objective
and fine-tuned using 311 samples of wildfires. The pretraining allows the model
to understand situations, outclassing baselines with a 1,4%, 3,7% and 9%
improvement estimating human, heavy and aerial resources; 21% and 10,2% in
expected extinction and control time; and 18,8% in expected burnt area. Using
the WAM we provide an example assessment map of Castilla y Le\'on, visualizing
the expected resources over an entire region.
|
[
"cs.CV",
"cs.AI",
"eess.IV"
] | false |
2306.05067
|
2023-06-08T09:31:28Z
|
Improving Visual Prompt Tuning for Self-supervised Vision Transformers
|
[
"Seungryong Yoo",
"Eunji Kim",
"Dahuin Jung",
"Jungbeom Lee",
"Sungroh Yoon"
] |
Visual Prompt Tuning (VPT) is an effective tuning method for adapting
pretrained Vision Transformers (ViTs) to downstream tasks. It leverages extra
learnable tokens, known as prompts, which steer the frozen pretrained ViTs.
Although VPT has demonstrated its applicability with supervised vision
transformers, it often underperforms with self-supervised ones. Through
empirical observations, we deduce that the effectiveness of VPT hinges largely
on the ViT blocks with which the prompt tokens interact. Specifically, VPT
shows improved performance on image classification tasks for MAE and MoCo v3
when the prompt tokens are inserted into later blocks rather than the first
block. These observations suggest that there exists an optimal location of
blocks for the insertion of prompt tokens. Unfortunately, identifying the
optimal blocks for prompts within each self-supervised ViT for diverse future
scenarios is a costly process. To mitigate this problem, we propose a simple
yet effective method that learns a gate for each ViT block to adjust its
intervention into the prompt tokens. With our method, prompt tokens are
selectively influenced by blocks that require steering for task adaptation. Our
method outperforms VPT variants in FGVC and VTAB image classification and
ADE20K semantic segmentation. The code is available at
https://github.com/ryongithub/GatedPromptTuning.
|
[
"cs.LG",
"cs.AI",
"cs.CV"
] | false |
2306.05208
|
2023-06-08T14:05:06Z
|
PriSampler: Mitigating Property Inference of Diffusion Models
|
[
"Hailong Hu",
"Jun Pang"
] |
Diffusion models have been remarkably successful in data synthesis. Such
successes have also driven diffusion models to apply to sensitive data, such as
human face data, but this might bring about severe privacy concerns. In this
work, we systematically present the first privacy study about property
inference attacks against diffusion models, in which adversaries aim to extract
sensitive global properties of the training set from a diffusion model, such as
the proportion of the training data for certain sensitive properties.
Specifically, we consider the most practical attack scenario: adversaries are
only allowed to obtain synthetic data. Under this realistic scenario, we
evaluate the property inference attacks on different types of samplers and
diffusion models. A broad range of evaluations shows that various diffusion
models and their samplers are all vulnerable to property inference attacks.
Furthermore, one case study on off-the-shelf pre-trained diffusion models also
demonstrates the effectiveness of the attack in practice. Finally, we propose a
new model-agnostic plug-in method PriSampler to mitigate the property inference
of diffusion models. PriSampler can be directly applied to well-trained
diffusion models and support both stochastic and deterministic sampling.
Extensive experiments illustrate the effectiveness of our defense and it makes
adversaries infer the proportion of properties as close as random guesses.
PriSampler also shows its significantly superior performance to diffusion
models trained with differential privacy on both model utility and defense
performance.
|
[
"cs.CR",
"cs.CV",
"cs.LG"
] | false |
2306.05233
|
2023-06-08T14:31:58Z
|
Ownership Protection of Generative Adversarial Networks
|
[
"Hailong Hu",
"Jun Pang"
] |
Generative adversarial networks (GANs) have shown remarkable success in image
synthesis, making GAN models themselves commercially valuable to legitimate
model owners. Therefore, it is critical to technically protect the intellectual
property of GANs. Prior works need to tamper with the training set or training
process, and they are not robust to emerging model extraction attacks. In this
paper, we propose a new ownership protection method based on the common
characteristics of a target model and its stolen models. Our method can be
directly applicable to all well-trained GANs as it does not require retraining
target models. Extensive experimental results show that our new method can
achieve the best protection performance, compared to the state-of-the-art
methods. Finally, we demonstrate the effectiveness of our method with respect
to the number of generations of model extraction attacks, the number of
generated samples, different datasets, as well as adaptive attacks.
|
[
"cs.CR",
"cs.CV",
"cs.LG"
] | false |
2306.05240
|
2023-06-08T14:39:24Z
|
Dealing with Semantic Underspecification in Multimodal NLP
|
[
"Sandro Pezzelle"
] |
Intelligent systems that aim at mastering language as humans do must deal
with its semantic underspecification, namely, the possibility for a linguistic
signal to convey only part of the information needed for communication to
succeed. Consider the usages of the pronoun they, which can leave the gender
and number of its referent(s) underspecified. Semantic underspecification is
not a bug but a crucial language feature that boosts its storage and processing
efficiency. Indeed, human speakers can quickly and effortlessly integrate
semantically-underspecified linguistic signals with a wide range of
non-linguistic information, e.g., the multimodal context, social or cultural
conventions, and shared knowledge. Standard NLP models have, in principle, no
or limited access to such extra information, while multimodal systems grounding
language into other modalities, such as vision, are naturally equipped to
account for this phenomenon. However, we show that they struggle with it, which
could negatively affect their performance and lead to harmful consequences when
used for applications. In this position paper, we argue that our community
should be aware of semantic underspecification if it aims to develop language
technology that can successfully interact with human users. We discuss some
applications where mastering it is crucial and outline a few directions toward
achieving this goal.
|
[
"cs.CL",
"cs.AI",
"cs.CV"
] | false |
2306.05256
|
2023-06-08T14:53:02Z
|
Unscented Autoencoder
|
[
"Faris Janjoš",
"Lars Rosenbaum",
"Maxim Dolgov",
"J. Marius Zöllner"
] |
The Variational Autoencoder (VAE) is a seminal approach in deep generative
modeling with latent variables. Interpreting its reconstruction process as a
nonlinear transformation of samples from the latent posterior distribution, we
apply the Unscented Transform (UT) -- a well-known distribution approximation
used in the Unscented Kalman Filter (UKF) from the field of filtering. A finite
set of statistics called sigma points, sampled deterministically, provides a
more informative and lower-variance posterior representation than the
ubiquitous noise-scaling of the reparameterization trick, while ensuring
higher-quality reconstruction. We further boost the performance by replacing
the Kullback-Leibler (KL) divergence with the Wasserstein distribution metric
that allows for a sharper posterior. Inspired by the two components, we derive
a novel, deterministic-sampling flavor of the VAE, the Unscented Autoencoder
(UAE), trained purely with regularization-like terms on the per-sample
posterior. We empirically show competitive performance in Fr\'echet Inception
Distance (FID) scores over closely-related models, in addition to a lower
training variance than the VAE.
|
[
"cs.LG",
"cs.AI",
"cs.CV"
] | false |
2306.05419
|
2023-06-08T17:58:57Z
|
TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem
via Transformer-Based Architecture
|
[
"M. Esat Kalfaoglu",
"Halil Ibrahim Ozturk",
"Ozsel Kilinc",
"Alptekin Temizel"
] |
Driving scene understanding task involves detecting static elements such as
lanes, traffic signs, and traffic lights, and their relationships with each
other. To facilitate the development of comprehensive scene understanding
solutions using multiple camera views, a new dataset called Road Genome
(OpenLane-V2) has been released. This dataset allows for the exploration of
complex road connections and situations where lane markings may be absent.
Instead of using traditional lane markings, the lanes in this dataset are
represented by centerlines, which offer a more suitable representation of lanes
and their connections. In this study, we have introduced a new approach called
TopoMask for predicting centerlines in road topology. Unlike existing
approaches in the literature that rely on keypoints or parametric methods,
TopoMask utilizes an instance-mask based formulation with a transformer-based
architecture and, in order to enrich the mask instances with flow information,
a direction label representation is proposed. TopoMask have ranked 4th in the
OpenLane-V2 Score (OLS) and ranked 2nd in the F1 score of centerline prediction
in OpenLane Topology Challenge 2023. In comparison to the current
state-of-the-art method, TopoNet, the proposed method has achieved similar
performance in Frechet-based lane detection and outperformed TopoNet in
Chamfer-based lane detection without utilizing its scene graph neural network.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2306.05425
|
2023-06-08T17:59:56Z
|
MIMIC-IT: Multi-Modal In-Context Instruction Tuning
|
[
"Bo Li",
"Yuanhan Zhang",
"Liangyu Chen",
"Jinghao Wang",
"Fanyi Pu",
"Jingkang Yang",
"Chunyuan Li",
"Ziwei Liu"
] |
High-quality instructions and responses are essential for the zero-shot
performance of large language models on interactive natural language tasks. For
interactive vision-language tasks involving intricate visual scenes, a large
quantity of diverse and creative instruction-response pairs should be
imperative to tune vision-language models (VLMs). Nevertheless, the current
availability of vision-language instruction-response pairs in terms of
quantity, diversity, and creativity remains limited, posing challenges to the
generalization of interactive VLMs. Here we present MultI-Modal In-Context
Instruction Tuning (MIMIC-IT), a dataset comprising 2.8 million multimodal
instruction-response pairs, with 2.2 million unique instructions derived from
images and videos. Each pair is accompanied by multi-modal in-context
information, forming conversational contexts aimed at empowering VLMs in
perception, reasoning, and planning. The instruction-response collection
process, dubbed as Syphus, is scaled using an automatic annotation pipeline
that combines human expertise with GPT's capabilities. Using the MIMIC-IT
dataset, we train a large VLM named Otter. Based on extensive evaluations
conducted on vision-language benchmarks, it has been observed that Otter
demonstrates remarkable proficiency in multi-modal perception, reasoning, and
in-context learning. Human evaluation reveals it effectively aligns with the
user's intentions. We release the MIMIC-IT dataset, instruction-response
collection pipeline, benchmarks, and the Otter model.
|
[
"cs.CV",
"cs.AI",
"cs.CL",
"cs.HC"
] | true |
2306.05493
|
2023-06-08T18:31:56Z
|
Multi-Modal Classifiers for Open-Vocabulary Object Detection
|
[
"Prannay Kaul",
"Weidi Xie",
"Andrew Zisserman"
] |
The goal of this paper is open-vocabulary object detection (OVOD)
$\unicode{x2013}$ building a model that can detect objects beyond the set of
categories seen at training, thus enabling the user to specify categories of
interest at inference without the need for model retraining. We adopt a
standard two-stage object detector architecture, and explore three ways for
specifying novel categories: via language descriptions, via image exemplars, or
via a combination of the two. We make three contributions: first, we prompt a
large language model (LLM) to generate informative language descriptions for
object classes, and construct powerful text-based classifiers; second, we
employ a visual aggregator on image exemplars that can ingest any number of
images as input, forming vision-based classifiers; and third, we provide a
simple method to fuse information from language descriptions and image
exemplars, yielding a multi-modal classifier. When evaluating on the
challenging LVIS open-vocabulary benchmark we demonstrate that: (i) our
text-based classifiers outperform all previous OVOD works; (ii) our
vision-based classifiers perform as well as text-based classifiers in prior
work; (iii) using multi-modal classifiers perform better than either modality
alone; and finally, (iv) our text-based and multi-modal classifiers yield
better performance than a fully-supervised detector.
|
[
"cs.CV",
"cs.AI",
"cs.LG",
"I.4.6; I.4.8; I.4.9; I.2.10"
] | true |
2306.05514
|
2023-06-08T19:07:22Z
|
Robust Brain Age Estimation via Regression Models and MRI-derived
Features
|
[
"Mansoor Ahmed",
"Usama Sardar",
"Sarwan Ali",
"Shafiq Alam",
"Murray Patterson",
"Imdad Ullah Khan"
] |
The determination of biological brain age is a crucial biomarker in the
assessment of neurological disorders and understanding of the morphological
changes that occur during aging. Various machine learning models have been
proposed for estimating brain age through Magnetic Resonance Imaging (MRI) of
healthy controls. However, developing a robust brain age estimation (BAE)
framework has been challenging due to the selection of appropriate MRI-derived
features and the high cost of MRI acquisition. In this study, we present a
novel BAE framework using the Open Big Healthy Brain (OpenBHB) dataset, which
is a new multi-site and publicly available benchmark dataset that includes
region-wise feature metrics derived from T1-weighted (T1-w) brain MRI scans of
3965 healthy controls aged between 6 to 86 years. Our approach integrates three
different MRI-derived region-wise features and different regression models,
resulting in a highly accurate brain age estimation with a Mean Absolute Error
(MAE) of 3.25 years, demonstrating the framework's robustness. We also analyze
our model's regression-based performance on gender-wise (male and female)
healthy test groups. The proposed BAE framework provides a new approach for
estimating brain age, which has important implications for the understanding of
neurological disorders and age-related brain changes.
|
[
"eess.IV",
"cs.CV",
"cs.LG",
"q-bio.NC"
] | false |
2306.06130
|
2023-06-08T11:14:51Z
|
Towards Understanding the Interplay of Generative Artificial
Intelligence and the Internet
|
[
"Gonzalo Martínez",
"Lauren Watson",
"Pedro Reviriego",
"José Alberto Hernández",
"Marc Juarez",
"Rik Sarkar"
] |
The rapid adoption of generative Artificial Intelligence (AI) tools that can
generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT, have
put the societal impacts of these technologies at the center of public debate.
These tools are possible due to the massive amount of data (text and images)
that is publicly available through the Internet. At the same time, these
generative AI tools become content creators that are already contributing to
the data that is available to train future models. Therefore, future versions
of generative AI tools will be trained with a mix of human-created and
AI-generated content, causing a potential feedback loop between generative AI
and public data repositories. This interaction raises many questions: how will
future versions of generative AI tools behave when trained on a mixture of real
and AI generated data? Will they evolve and improve with the new data sets or
on the contrary will they degrade? Will evolution introduce biases or reduce
diversity in subsequent generations of generative AI tools? What are the
societal implications of the possible degradation of these models? Can we
mitigate the effects of this feedback loop? In this document, we explore the
effect of this interaction and report some initial results using simple
diffusion models trained with various image datasets. Our results show that the
quality and diversity of the generated images can degrade over time suggesting
that incorporating AI-created data can have undesired effects on future
versions of generative models.
|
[
"cs.AI",
"cs.CV",
"cs.LG"
] | false |
2306.04841
|
2023-06-08T00:24:29Z
|
Improving Vietnamese Legal Question--Answering System based on Automatic
Data Enrichment
|
[
"Thi-Hai-Yen Vuong",
"Ha-Thanh Nguyen",
"Quang-Huy Nguyen",
"Le-Minh Nguyen",
"Xuan-Hieu Phan"
] |
Question answering (QA) in law is a challenging problem because legal
documents are much more complicated than normal texts in terms of terminology,
structure, and temporal and logical relationships. It is even more difficult to
perform legal QA for low-resource languages like Vietnamese where labeled data
are rare and pre-trained language models are still limited. In this paper, we
try to overcome these limitations by implementing a Vietnamese article-level
retrieval-based legal QA system and introduce a novel method to improve the
performance of language models by improving data quality through weak labeling.
Our hypothesis is that in contexts where labeled data are limited, efficient
data enrichment can help increase overall performance. Our experiments are
designed to test multiple aspects, which demonstrate the effectiveness of the
proposed technique.
|
[
"cs.CL"
] | false |
2306.04845
|
2023-06-08T00:35:36Z
|
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with
Architecture-Routed Mixture-of-Experts
|
[
"Ganesh Jawahar",
"Haichuan Yang",
"Yunyang Xiong",
"Zechun Liu",
"Dilin Wang",
"Fei Sun",
"Meng Li",
"Aasish Pappu",
"Barlas Oguz",
"Muhammad Abdul-Mageed",
"Laks V. S. Lakshmanan",
"Raghuraman Krishnamoorthi",
"Vikas Chandra"
] |
Weight-sharing supernet has become a vital component for performance
estimation in the state-of-the-art (SOTA) neural architecture search (NAS)
frameworks. Although supernet can directly generate different subnetworks
without retraining, there is no guarantee for the quality of these subnetworks
because of weight sharing. In NLP tasks such as machine translation and
pre-trained language modeling, we observe that given the same model
architecture, there is a large performance gap between supernet and training
from scratch. Hence, supernet cannot be directly used and retraining is
necessary after finding the optimal architectures.
In this work, we propose mixture-of-supernets, a generalized supernet
formulation where mixture-of-experts (MoE) is adopted to enhance the expressive
power of the supernet model, with negligible training overhead. In this way,
different subnetworks do not share the model weights directly, but through an
architecture-based routing mechanism. As a result, model weights of different
subnetworks are customized towards their specific architectures and the weight
generation is learned by gradient descent. Compared to existing weight-sharing
supernet for NLP, our method can minimize the retraining time, greatly
improving training efficiency. In addition, the proposed method achieves the
SOTA performance in NAS for building fast machine translation models, yielding
better latency-BLEU tradeoff compared to HAT, state-of-the-art NAS for MT. We
also achieve the SOTA performance in NAS for building memory-efficient
task-agnostic BERT models, outperforming NAS-BERT and AutoDistil in various
model sizes.
|
[
"cs.CL"
] | true |
2306.04903
|
2023-06-08T03:10:49Z
|
NOWJ at COLIEE 2023 -- Multi-Task and Ensemble Approaches in Legal
Information Processing
|
[
"Thi-Hai-Yen Vuong",
"Hai-Long Nguyen",
"Tan-Minh Nguyen",
"Hoang-Trung Nguyen",
"Thai-Binh Nguyen",
"Ha-Thanh Nguyen"
] |
This paper presents the NOWJ team's approach to the COLIEE 2023 Competition,
which focuses on advancing legal information processing techniques and applying
them to real-world legal scenarios. Our team tackles the four tasks in the
competition, which involve legal case retrieval, legal case entailment, statute
law retrieval, and legal textual entailment. We employ state-of-the-art machine
learning models and innovative approaches, such as BERT, Longformer,
BM25-ranking algorithm, and multi-task learning models. Although our team did
not achieve state-of-the-art results, our findings provide valuable insights
and pave the way for future improvements in legal information processing.
|
[
"cs.CL"
] | false |
2306.04950
|
2023-06-08T05:45:25Z
|
Open Set Relation Extraction via Unknown-Aware Training
|
[
"Jun Zhao",
"Xin Zhao",
"Wenyu Zhan",
"Qi Zhang",
"Tao Gui",
"Zhongyu Wei",
"Yunwen Chen",
"Xiang Gao",
"Xuanjing Huang"
] |
The existing supervised relation extraction methods have achieved impressive
performance in a closed-set setting, where the relations during both training
and testing remain the same. In a more realistic open-set setting, unknown
relations may appear in the test set. Due to the lack of supervision signals
from unknown relations, a well-performing closed-set relation extractor can
still confidently misclassify them into known relations. In this paper, we
propose an unknown-aware training method, regularizing the model by dynamically
synthesizing negative instances. To facilitate a compact decision boundary,
``difficult'' negative instances are necessary. Inspired by text adversarial
attacks, we adaptively apply small but critical perturbations to original
training instances and thus synthesizing negative instances that are more
likely to be mistaken by the model as known relations. Experimental results
show that this method achieves SOTA unknown relation detection without
compromising the classification of known relations.
|
[
"cs.CL"
] | false |
2306.04954
|
2023-06-08T06:02:34Z
|
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot
Relation Extraction
|
[
"Jun Zhao",
"Wenyu Zhan",
"Xin Zhao",
"Qi Zhang",
"Tao Gui",
"Zhongyu Wei",
"Junzhe Wang",
"Minlong Peng",
"Mingming Sun"
] |
Semantic matching is a mainstream paradigm of zero-shot relation extraction,
which matches a given input with a corresponding label description. The
entities in the input should exactly match their hypernyms in the description,
while the irrelevant contexts should be ignored when matching. However, general
matching methods lack explicit modeling of the above matching pattern. In this
work, we propose a fine-grained semantic matching method tailored for zero-shot
relation extraction. Following the above matching pattern, we decompose the
sentence-level similarity score into entity and context matching scores. Due to
the lack of explicit annotations of the redundant components, we design a
feature distillation module to adaptively identify the relation-irrelevant
features and reduce their negative impact on context matching. Experimental
results show that our method achieves higher matching $F_1$ score and has an
inference speed 10 times faster, when compared with the state-of-the-art
methods.
|
[
"cs.CL"
] | false |
2306.04968
|
2023-06-08T06:55:02Z
|
Actively Supervised Clustering for Open Relation Extraction
|
[
"Jun Zhao",
"Yongxin Zhang",
"Qi Zhang",
"Tao Gui",
"Zhongyu Wei",
"Minlong Peng",
"Mingming Sun"
] |
Current clustering-based Open Relation Extraction (OpenRE) methods usually
adopt a two-stage pipeline. The first stage simultaneously learns relation
representations and assignments. The second stage manually labels several
instances and thus names the relation for each cluster. However, unsupervised
objectives struggle to optimize the model to derive accurate clustering
assignments, and the number of clusters has to be supplied in advance. In this
paper, we present a novel setting, named actively supervised clustering for
OpenRE. Our insight lies in that clustering learning and relation labeling can
be alternately performed, providing the necessary guidance for clustering
without a significant increase in human effort. The key to the setting is
selecting which instances to label. Instead of using classical active labeling
strategies designed for fixed known classes, we propose a new strategy, which
is applicable to dynamically discover clusters of unknown relations.
Experimental results show that our method is able to discover almost all
relational clusters in the data and improve the SOTA methods by 10.3\% and
5.2\%, on two datasets respectively.
|
[
"cs.CL"
] | false |
2306.04996
|
2023-06-08T07:33:22Z
|
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text
Classification
|
[
"Inigo Jauregi Unanue",
"Gholamreza Haffari",
"Massimo Piccardi"
] |
Cross-lingual text classification leverages text classifiers trained in a
high-resource language to perform text classification in other languages with
no or minimal fine-tuning (zero/few-shots cross-lingual transfer). Nowadays,
cross-lingual text classifiers are typically built on large-scale, multilingual
language models (LMs) pretrained on a variety of languages of interest.
However, the performance of these models vary significantly across languages
and classification tasks, suggesting that the superposition of the language
modelling and classification tasks is not always effective. For this reason, in
this paper we propose revisiting the classic "translate-and-test" pipeline to
neatly separate the translation and classification stages. The proposed
approach couples 1) a neural machine translator translating from the targeted
language to a high-resource language, with 2) a text classifier trained in the
high-resource language, but the neural machine translator generates "soft"
translations to permit end-to-end backpropagation during fine-tuning of the
pipeline. Extensive experiments have been carried out over three cross-lingual
text classification datasets (XNLI, MLDoc and MultiEURLEX), with the results
showing that the proposed approach has significantly improved performance over
a competitive baseline.
|
[
"cs.CL"
] | false |
2306.05075
|
2023-06-08T09:56:57Z
|
LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for
Sexism Detection and Classification
|
[
"Konstantin Chernyshev",
"Ekaterina Garanina",
"Duygu Bayram",
"Qiankun Zheng",
"Lukas Edman"
] |
Misogyny and sexism are growing problems in social media. Advances have been
made in online sexism detection but the systems are often uninterpretable.
SemEval-2023 Task 10 on Explainable Detection of Online Sexism aims at
increasing explainability of the sexism detection, and our team participated in
all the proposed subtasks. Our system is based on further domain-adaptive
pre-training (Gururangan et al., 2020). Building on the Transformer-based
models with the domain adaptation, we compare fine-tuning with multi-task
learning and show that each subtask requires a different system configuration.
In our experiments, multi-task learning performs on par with standard
fine-tuning for sexism detection and noticeably better for coarse-grained
sexism classification, while fine-tuning is preferable for fine-grained
classification.
|
[
"cs.CL"
] | false |
2306.05076
|
2023-06-08T09:59:48Z
|
DLAMA: A Framework for Curating Culturally Diverse Facts for Probing the
Knowledge of Pretrained Language Models
|
[
"Amr Keleg",
"Walid Magdy"
] |
A few benchmarking datasets have been released to evaluate the factual
knowledge of pretrained language models. These benchmarks (e.g., LAMA, and
ParaRel) are mainly developed in English and later are translated to form new
multilingual versions (e.g., mLAMA, and mParaRel). Results on these
multilingual benchmarks suggest that using English prompts to recall the facts
from multilingual models usually yields significantly better and more
consistent performance than using non-English prompts. Our analysis shows that
mLAMA is biased toward facts from Western countries, which might affect the
fairness of probing models. We propose a new framework for curating factual
triples from Wikidata that are culturally diverse. A new benchmark DLAMA-v1 is
built of factual triples from three pairs of contrasting cultures having a
total of 78,259 triples from 20 relation predicates. The three pairs comprise
facts representing the (Arab and Western), (Asian and Western), and (South
American and Western) countries respectively. Having a more balanced benchmark
(DLAMA-v1) supports that mBERT performs better on Western facts than
non-Western ones, while monolingual Arabic, English, and Korean models tend to
perform better on their culturally proximate facts. Moreover, both monolingual
and multilingual models tend to make a prediction that is culturally or
geographically relevant to the correct label, even if the prediction is wrong.
|
[
"cs.CL"
] | false |
2306.05119
|
2023-06-08T11:41:39Z
|
Reference Matters: Benchmarking Factual Error Correction for Dialogue
Summarization with Fine-grained Evaluation Framework
|
[
"Mingqi Gao",
"Xiaojun Wan",
"Jia Su",
"Zhefeng Wang",
"Baoxing Huai"
] |
Factuality is important to dialogue summarization. Factual error correction
(FEC) of model-generated summaries is one way to improve factuality. Current
FEC evaluation that relies on factuality metrics is not reliable and detailed
enough. To address this problem, we are the first to manually annotate a FEC
dataset for dialogue summarization containing 4000 items and propose FERRANTI,
a fine-grained evaluation framework based on reference correction that
automatically evaluates the performance of FEC models on different error
categories. Using this evaluation framework, we conduct sufficient experiments
with FEC approaches under a variety of settings and find the best training
modes and significant differences in the performance of the existing approaches
on different factual error categories.
|
[
"cs.CL"
] | false |
2306.05126
|
2023-06-08T11:50:58Z
|
Mapping Brains with Language Models: A Survey
|
[
"Antonia Karamolegkou",
"Mostafa Abdou",
"Anders Søgaard"
] |
Over the years, many researchers have seemingly made the same observation:
Brain and language model activations exhibit some structural similarities,
enabling linear partial mappings between features extracted from neural
recordings and computational language models. In an attempt to evaluate how
much evidence has been accumulated for this observation, we survey over 30
studies spanning 10 datasets and 8 metrics. How much evidence has been
accumulated, and what, if anything, is missing before we can draw conclusions?
Our analysis of the evaluation methods used in the literature reveals that some
of the metrics are less conservative. We also find that the accumulated
evidence, for now, remains ambiguous, but correlations with model size and
quality provide grounds for cautious optimism.
|
[
"cs.CL"
] | false |
2306.05270
|
2023-06-08T15:19:57Z
|
Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on
Summarizing Patients' Active Diagnoses and Problems from Electronic Health
Record Progress Notes
|
[
"Yanjun Gao",
"Dmitriy Dligach",
"Timothy Miller",
"Matthew M. Churpek",
"Majid Afshar"
] |
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem
List Summarization (ProbSum) in January 2023. The aim of this shared task is to
attract future research efforts in building NLP models for real-world
diagnostic decision support applications, where a system generating relevant
and accurate diagnoses will augment the healthcare providers decision-making
process and improve the quality of care for patients. The goal for participants
is to develop models that generated a list of diagnoses and problems using
input from the daily care notes collected from the hospitalization of
critically ill patients. Eight teams submitted their final systems to the
shared task leaderboard. In this paper, we describe the tasks, datasets,
evaluation metrics, and baseline systems. Additionally, the techniques and
results of the evaluation of the different approaches tried by the
participating teams are summarized.
|
[
"cs.CL"
] | false |
2306.05278
|
2023-06-08T15:26:52Z
|
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs.
Continual Pre-training
|
[
"Haode Zhang",
"Haowen Liang",
"Liming Zhan",
"Xiao-Ming Wu",
"Albert Y. S. Lam"
] |
We consider the task of few-shot intent detection, which involves training a
deep learning model to classify utterances based on their underlying intents
using only a small amount of labeled data. The current approach to address this
problem is through continual pre-training, i.e., fine-tuning pre-trained
language models (PLMs) on external resources (e.g., conversational corpora,
public intent detection datasets, or natural language understanding datasets)
before using them as utterance encoders for training an intent classifier. In
this paper, we show that continual pre-training may not be essential, since the
overfitting problem of PLMs on this task may not be as serious as expected.
Specifically, we find that directly fine-tuning PLMs on only a handful of
labeled examples already yields decent results compared to methods that employ
continual pre-training, and the performance gap diminishes rapidly as the
number of labeled data increases. To maximize the utilization of the limited
available data, we propose a context augmentation method and leverage
sequential self-distillation to boost performance. Comprehensive experiments on
real-world benchmarks show that given only two or more labeled samples per
class, direct fine-tuning outperforms many strong baselines that utilize
external data sources for continual pre-training. The code can be found at
https://github.com/hdzhang-code/DFTPlus.
|
[
"cs.CL"
] | false |
2306.05317
|
2023-06-08T16:08:10Z
|
CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models
|
[
"Potsawee Manakul",
"Yassir Fathullah",
"Adian Liusie",
"Vyas Raina",
"Vatsal Raina",
"Mark Gales"
] |
In this paper, we consider the challenge of summarizing patients' medical
progress notes in a limited data setting. For the Problem List Summarization
(shared task 1A) at the BioNLP Workshop 2023, we demonstrate that Clinical-T5
fine-tuned to 765 medical clinic notes outperforms other extractive,
abstractive and zero-shot baselines, yielding reasonable baseline systems for
medical note summarization. Further, we introduce Hierarchical Ensemble of
Summarization Models (HESM), consisting of token-level ensembles of diverse
fine-tuned Clinical-T5 models, followed by Minimum Bayes Risk (MBR) decoding.
Our HESM approach lead to a considerable summarization performance boost, and
when evaluated on held-out challenge data achieved a ROUGE-L of 32.77, which
was the best-performing system at the top of the shared task leaderboard.
|
[
"cs.CL"
] | false |
2306.05387
|
2023-06-08T17:38:14Z
|
Utterance Emotion Dynamics in Children's Poems: Emotional Changes Across
Age
|
[
"Daniela Teodorescu",
"Alona Fyshe",
"Saif M. Mohammad"
] |
Emerging psychopathology studies are showing that patterns of changes in
emotional state -- emotion dynamics -- are associated with overall well-being
and mental health. More recently, there has been some work in tracking emotion
dynamics through one's utterances, allowing for data to be collected on a
larger scale across time and people. However, several questions about how
emotion dynamics change with age, especially in children, and when determined
through children's writing, remain unanswered. In this work, we use both a
lexicon and a machine learning based approach to quantify characteristics of
emotion dynamics determined from poems written by children of various ages. We
show that both approaches point to similar trends: consistent increasing
intensities for some emotions (e.g., anger, fear, joy, sadness, arousal, and
dominance) with age and a consistent decreasing valence with age. We also find
increasing emotional variability, rise rates (i.e., emotional reactivity), and
recovery rates (i.e., emotional regulation) with age. These results act as a
useful baselines for further research in how patterns of emotions expressed by
children change with age, and their association with mental health.
|
[
"cs.CL"
] | false |
2306.05392
|
2023-06-08T17:45:14Z
|
Modular Visual Question Answering via Code Generation
|
[
"Sanjay Subramanian",
"Medhini Narasimhan",
"Kushal Khangaonkar",
"Kevin Yang",
"Arsha Nagrani",
"Cordelia Schmid",
"Andy Zeng",
"Trevor Darrell",
"Dan Klein"
] |
We present a framework that formulates visual question answering as modular
code generation. In contrast to prior work on modular approaches to VQA, our
approach requires no additional training and relies on pre-trained language
models (LMs), visual models pre-trained on image-caption pairs, and fifty VQA
examples used for in-context learning. The generated Python programs invoke and
compose the outputs of the visual models using arithmetic and conditional
logic. Our approach improves accuracy on the COVR dataset by at least 3% and on
the GQA dataset by roughly 2% compared to the few-shot baseline that does not
employ code generation.
|
[
"cs.CL"
] | true |
2306.05406
|
2023-06-08T17:54:36Z
|
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to
Pre-trained Language Models Memories
|
[
"Shizhe Diao",
"Tianyang Xu",
"Ruijia Xu",
"Jiawei Wang",
"Tong Zhang"
] |
Pre-trained language models (PLMs) demonstrate excellent abilities to
understand texts in the generic domain while struggling in a specific domain.
Although continued pre-training on a large domain-specific corpus is effective,
it is costly to tune all the parameters on the domain. In this paper, we
investigate whether we can adapt PLMs both effectively and efficiently by only
tuning a few parameters. Specifically, we decouple the feed-forward networks
(FFNs) of the Transformer architecture into two parts: the original pre-trained
FFNs to maintain the old-domain knowledge and our novel domain-specific
adapters to inject domain-specific knowledge in parallel. Then we adopt a
mixture-of-adapters gate to fuse the knowledge from different domain adapters
dynamically. Our proposed Mixture-of-Domain-Adapters (MixDA) employs a
two-stage adapter-tuning strategy that leverages both unlabeled data and
labeled data to help the domain adaptation: i) domain-specific adapter on
unlabeled data; followed by ii) the task-specific adapter on labeled data.
MixDA can be seamlessly plugged into the pretraining-finetuning paradigm and
our experiments demonstrate that MixDA achieves superior performance on
in-domain tasks (GLUE), out-of-domain tasks (ChemProt, RCT, IMDB, Amazon), and
knowledge-intensive tasks (KILT). Further analyses demonstrate the reliability,
scalability, and efficiency of our method. The code is available at
https://github.com/Amano-Aki/Mixture-of-Domain-Adapters.
|
[
"cs.CL"
] | false |
2306.05561
|
2023-06-08T21:06:19Z
|
Privacy- and Utility-Preserving NLP with Anonymized Data: A case study
of Pseudonymization
|
[
"Oleksandr Yermilov",
"Vipul Raheja",
"Artem Chernodub"
] |
This work investigates the effectiveness of different pseudonymization
techniques, ranging from rule-based substitutions to using pre-trained Large
Language Models (LLMs), on a variety of datasets and models used for two widely
used NLP tasks: text classification and summarization. Our work provides
crucial insights into the gaps between original and anonymized data (focusing
on the pseudonymization technique) and model quality and fosters future
research into higher-quality anonymization techniques to better balance the
trade-offs between data protection and utility preservation. We make our code,
pseudonymized datasets, and downstream models publicly available
|
[
"cs.CL"
] | false |
2306.05596
|
2023-06-08T23:52:35Z
|
LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts
|
[
"Muskan Garg",
"Manas Gaur",
"Raxit Goswami",
"Sunghwan Sohn"
] |
Low self-esteem and interpersonal needs (i.e., thwarted belongingness (TB)
and perceived burdensomeness (PB)) have a major impact on depression and
suicide attempts. Individuals seek social connectedness on social media to
boost and alleviate their loneliness. Social media platforms allow people to
express their thoughts, experiences, beliefs, and emotions. Prior studies on
mental health from social media have focused on symptoms, causes, and
disorders. Whereas an initial screening of social media content for
interpersonal risk factors and low self-esteem may raise early alerts and
assign therapists to at-risk users of mental disturbance. Standardized scales
measure self-esteem and interpersonal needs from questions created using
psychological theories. In the current research, we introduce a
psychology-grounded and expertly annotated dataset, LoST: Low Self esTeem, to
study and detect low self-esteem on Reddit. Through an annotation approach
involving checks on coherence, correctness, consistency, and reliability, we
ensure gold-standard for supervised learning. We present results from different
deep language models tested using two data augmentation techniques. Our
findings suggest developing a class of language models that infuses
psychological and clinical knowledge.
|
[
"cs.CL"
] | false |
2306.04925
|
2023-06-08T04:04:47Z
|
Prefer to Classify: Improving Text Classifiers via Auxiliary Preference
Learning
|
[
"Jaehyung Kim",
"Jinwoo Shin",
"Dongyeop Kang"
] |
The development of largely human-annotated benchmarks has driven the success
of deep neural networks in various NLP tasks. To enhance the effectiveness of
existing benchmarks, collecting new additional input-output pairs is often too
costly and challenging, particularly considering their marginal impact on
improving the current model accuracy. Instead, additional or complementary
annotations on the existing input texts in the benchmarks can be preferable as
an efficient way to pay the additional human cost. In this paper, we
investigate task-specific preferences between pairs of input texts as a new
alternative way for such auxiliary data annotation. From 'pair-wise'
comparisons with respect to the task, the auxiliary preference learning enables
the model to learn an additional informative training signal that cannot be
captured with 'instance-wise' task labels. To this end, we propose a novel
multi-task learning framework, called prefer-to-classify (P2C), which can enjoy
the cooperative effect of learning both the given classification task and the
auxiliary preferences. Here, we provide three different ways to collect
preference signals in practice: (a) implicitly extracting from annotation
records (for free, but often unavailable), (b) collecting explicitly from crowd
workers (high paid), or (c) pre-trained large language models such as GPT-3
(low paid). Given existing classification NLP benchmarks, we demonstrate that
the proposed auxiliary preference learning via P2C on them is effective in
improving text classifiers. Our codes are publicly available.
|
[
"cs.CL",
"cs.LG"
] | false |
2306.04941
|
2023-06-08T05:17:03Z
|
A modified model for topic detection from a corpus and a new metric
evaluating the understandability of topics
|
[
"Tomoya Kitano",
"Yuto Miyatake",
"Daisuke Furihata"
] |
This paper presents a modified neural model for topic detection from a corpus
and proposes a new metric to evaluate the detected topics. The new model builds
upon the embedded topic model incorporating some modifications such as document
clustering. Numerical experiments suggest that the new model performs
favourably regardless of the document's length. The new metric, which can be
computed more efficiently than widely-used metrics such as topic coherence,
provides variable information regarding the understandability of the detected
topics.
|
[
"cs.CL",
"cs.LG"
] | false |
2306.04964
|
2023-06-08T06:43:10Z
|
Leveraging Language Identification to Enhance Code-Mixed Text
Classification
|
[
"Gauri Takawane",
"Abhishek Phaltankar",
"Varad Patwardhan",
"Aryan Patil",
"Raviraj Joshi",
"Mukta S. Takalikar"
] |
The usage of more than one language in the same text is referred to as Code
Mixed. It is evident that there is a growing degree of adaption of the use of
code-mixed data, especially English with a regional language, on social media
platforms. Existing deep-learning models do not take advantage of the implicit
language information in the code-mixed text. Our study aims to improve
BERT-based models performance on low-resource Code-Mixed Hindi-English Datasets
by experimenting with language augmentation approaches. We propose a pipeline
to improve code-mixed systems that comprise data preprocessing, word-level
language identification, language augmentation, and model training on
downstream tasks like sentiment analysis. For language augmentation in BERT
models, we explore word-level interleaving and post-sentence placement of
language information. We have examined the performance of vanilla BERT-based
models and their code-mixed HingBERT counterparts on respective benchmark
datasets, comparing their results with and without using word-level language
information. The models were evaluated using metrics such as accuracy,
precision, recall, and F1 score. Our findings show that the proposed language
augmentation approaches work well across different BERT models. We demonstrate
the importance of augmenting code-mixed text with language information on five
different code-mixed Hindi-English downstream datasets based on sentiment
analysis, hate speech detection, and emotion detection.
|
[
"cs.CL",
"cs.LG"
] | false |
2306.05087
|
2023-06-08T10:41:56Z
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning
Optimization
|
[
"Yidong Wang",
"Zhuohao Yu",
"Zhengran Zeng",
"Linyi Yang",
"Cunxiang Wang",
"Hao Chen",
"Chaoya Jiang",
"Rui Xie",
"Jindong Wang",
"Xing Xie",
"Wei Ye",
"Shikun Zhang",
"Yue Zhang"
] |
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
[
"cs.CL",
"cs.AI"
] | true |
2306.05115
|
2023-06-08T11:29:58Z
|
Closing the Loop: Testing ChatGPT to Generate Model Explanations to
Improve Human Labelling of Sponsored Content on Social Media
|
[
"Thales Bertaglia",
"Stefan Huber",
"Catalina Goanta",
"Gerasimos Spanakis",
"Adriana Iamnitchi"
] |
Regulatory bodies worldwide are intensifying their efforts to ensure
transparency in influencer marketing on social media through instruments like
the Unfair Commercial Practices Directive (UCPD) in the European Union, or
Section 5 of the Federal Trade Commission Act. Yet enforcing these obligations
has proven to be highly problematic due to the sheer scale of the influencer
market. The task of automatically detecting sponsored content aims to enable
the monitoring and enforcement of such regulations at scale. Current research
in this field primarily frames this problem as a machine learning task,
focusing on developing models that achieve high classification performance in
detecting ads. These machine learning tasks rely on human data annotation to
provide ground truth information. However, agreement between annotators is
often low, leading to inconsistent labels that hinder the reliability of
models. To improve annotation accuracy and, thus, the detection of sponsored
content, we propose using chatGPT to augment the annotation process with
phrases identified as relevant features and brief explanations. Our experiments
show that this approach consistently improves inter-annotator agreement and
annotation accuracy. Additionally, our survey of user experience in the
annotation task indicates that the explanations improve the annotators'
confidence and streamline the process. Our proposed methods can ultimately lead
to more transparency and alignment with regulatory requirements in sponsored
content detection.
|
[
"cs.CL",
"cs.SI"
] | false |
2306.05276
|
2023-06-08T15:25:24Z
|
Extensive Evaluation of Transformer-based Architectures for Adverse Drug
Events Extraction
|
[
"Simone Scaboro",
"Beatrice Portellia",
"Emmanuele Chersoni",
"Enrico Santus",
"Giuseppe Serra"
] |
Adverse Event (ADE) extraction is one of the core tasks in digital
pharmacovigilance, especially when applied to informal texts. This task has
been addressed by the Natural Language Processing community using large
pre-trained language models, such as BERT. Despite the great number of
Transformer-based architectures used in the literature, it is unclear which of
them has better performances and why. Therefore, in this paper we perform an
extensive evaluation and analysis of 19 Transformer-based models for ADE
extraction on informal texts. We compare the performance of all the considered
models on two datasets with increasing levels of informality (forums posts and
tweets). We also combine the purely Transformer-based models with two
commonly-used additional processing layers (CRF and LSTM), and analyze their
effect on the models performance. Furthermore, we use a well-established
feature importance technique (SHAP) to correlate the performance of the models
with a set of features that describe them: model category (AutoEncoding,
AutoRegressive, Text-to-Text), pretraining domain, training from scratch, and
model size in number of parameters. At the end of our analyses, we identify a
list of take-home messages that can be derived from the experimental data.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.05443
|
2023-06-08T14:20:29Z
|
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark
for Finance
|
[
"Qianqian Xie",
"Weiguang Han",
"Xiao Zhang",
"Yanzhao Lai",
"Min Peng",
"Alejandro Lopez-Lira",
"Jimin Huang"
] |
Although large language models (LLMs) has shown great performance on natural
language processing (NLP) in the financial domain, there are no publicly
available financial tailtored LLMs, instruction tuning datasets, and evaluation
benchmarks, which is critical for continually pushing forward the open-source
development of financial artificial intelligence (AI). This paper introduces
PIXIU, a comprehensive framework including the first financial LLM based on
fine-tuning LLaMA with instruction data, the first instruction data with 136K
data samples to support the fine-tuning, and an evaluation benchmark with 5
tasks and 9 datasets. We first construct the large-scale multi-task instruction
data considering a variety of financial tasks, financial document types, and
financial data modalities. We then propose a financial LLM called FinMA by
fine-tuning LLaMA with the constructed dataset to be able to follow
instructions for various financial tasks. To support the evaluation of
financial LLMs, we propose a standardized benchmark that covers a set of
critical financial tasks, including five financial NLP tasks and one financial
prediction task. With this benchmark, we conduct a detailed analysis of FinMA
and several existing LLMs, uncovering their strengths and weaknesses in
handling critical financial tasks. The model, datasets, benchmark, and
experimental results are open-sourced to facilitate future research in
financial AI.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.05477
|
2023-06-08T18:02:07Z
|
Hexatagging: Projective Dependency Parsing as Tagging
|
[
"Afra Amini",
"Tianyu Liu",
"Ryan Cotterell"
] |
We introduce a novel dependency parser, the hexatagger, that constructs
dependency trees by tagging the words in a sentence with elements from a finite
set of possible tags. In contrast to many approaches to dependency parsing, our
approach is fully parallelizable at training time, i.e., the structure-building
actions needed to build a dependency parse can be predicted in parallel to each
other. Additionally, exact decoding is linear in time and space complexity.
Furthermore, we derive a probabilistic dependency parser that predicts hexatags
using no more than a linear model with features from a pretrained language
model, i.e., we forsake a bespoke architecture explicitly designed for the
task. Despite the generality and simplicity of our approach, we achieve
state-of-the-art performance of 96.4 LAS and 97.4 UAS on the Penn Treebank test
set. Additionally, our parser's linear time complexity and parallelism
significantly improve computational efficiency, with a roughly 10-times
speed-up over previous state-of-the-art models during decoding.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.05556
|
2023-06-08T20:59:40Z
|
Emotion and Sentiment Guided Paraphrasing
|
[
"Justin J. Xie",
"Ameeta Agrawal"
] |
Paraphrase generation, a.k.a. paraphrasing, is a common and important task in
natural language processing. Emotional paraphrasing, which changes the emotion
embodied in a piece of text while preserving its meaning, has many potential
applications, including moderating online dialogues and preventing
cyberbullying. We introduce a new task of fine-grained emotional paraphrasing
along emotion gradients, that is, altering the emotional intensities of the
paraphrases in fine-grained settings following smooth variations in affective
dimensions while preserving the meaning of the original text. We reconstruct
several widely used paraphrasing datasets by augmenting the input and target
texts with their fine-grained emotion labels. Then, we propose a framework for
emotion and sentiment guided paraphrasing by leveraging pre-trained language
models for conditioned text generation. Extensive evaluation of the fine-tuned
models suggests that including fine-grained emotion labels in the paraphrase
task significantly improves the likelihood of obtaining high-quality
paraphrases that reflect the desired emotions while achieving consistently
better scores in paraphrase metrics such as BLEU, ROUGE, and METEOR.
|
[
"cs.CL",
"cs.LG"
] | false |
2306.07402
|
2023-06-08T20:35:53Z
|
The economic trade-offs of large language models: A case study
|
[
"Kristen Howell",
"Gwen Christian",
"Pavel Fomitchov",
"Gitit Kehat",
"Julianne Marzulla",
"Leanne Rolston",
"Jadin Tredup",
"Ilana Zimmerman",
"Ethan Selfridge",
"Joseph Bradley"
] |
Contacting customer service via chat is a common practice. Because employing
customer service agents is expensive, many companies are turning to NLP that
assists human agents by auto-generating responses that can be used directly or
with modifications. Large Language Models (LLMs) are a natural fit for this use
case; however, their efficacy must be balanced with the cost of training and
serving them. This paper assesses the practical cost and impact of LLMs for the
enterprise as a function of the usefulness of the responses that they generate.
We present a cost framework for evaluating an NLP model's utility for this use
case and apply it to a single brand as a case study in the context of an
existing agent assistance product. We compare three strategies for specializing
an LLM - prompt engineering, fine-tuning, and knowledge distillation - using
feedback from the brand's customer service agents. We find that the usability
of a model's responses can make up for a large difference in inference cost for
our case study brand, and we extrapolate our findings to the broader enterprise
space.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.04874
|
2023-06-08T02:07:49Z
|
Expanding Scope: Adapting English Adversarial Attacks to Chinese
|
[
"Hanyu Liu",
"Chengyuan Cai",
"Yanjun Qi"
] |
Recent studies have revealed that NLP predictive models are vulnerable to
adversarial attacks. Most existing studies focused on designing attacks to
evaluate the robustness of NLP models in the English language alone. Literature
has seen an increasing need for NLP solutions for other languages. We,
therefore, ask one natural question: whether state-of-the-art (SOTA) attack
methods generalize to other languages. This paper investigates how to adapt
SOTA adversarial attack algorithms in English to the Chinese language. Our
experiments show that attack methods previously applied to English NLP can
generate high-quality adversarial examples in Chinese when combined with proper
text segmentation and linguistic constraints. In addition, we demonstrate that
the generated adversarial examples can achieve high fluency and semantic
consistency by focusing on the Chinese language's morphology and phonology,
which in turn can be used to improve the adversarial robustness of Chinese NLP
models.
|
[
"cs.CL",
"cs.AI",
"cs.CR",
"cs.LG"
] | false |
2306.04926
|
2023-06-08T04:08:32Z
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
[
"Yousuf A. Khan",
"Clarisse Hokia",
"Jennifer Xu",
"Ben Ehlert"
] |
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2306.04933
|
2023-06-08T04:31:48Z
|
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural
Language Understanding
|
[
"Junda Wu",
"Tong Yu",
"Rui Wang",
"Zhao Song",
"Ruiyi Zhang",
"Handong Zhao",
"Chaochao Lu",
"Shuai Li",
"Ricardo Henao"
] |
Soft prompt tuning achieves superior performances across a wide range of
few-shot tasks. However, the performances of prompt tuning can be highly
sensitive to the initialization of the prompts. We also empirically observe
that conventional prompt tuning methods cannot encode and learn sufficient
task-relevant information from prompt tokens. In this work, we develop an
information-theoretic framework that formulates soft prompt tuning as
maximizing mutual information between prompts and other model parameters (or
encoded representations). This novel view helps us to develop a more efficient,
accurate and robust soft prompt tuning method InfoPrompt. With this framework,
we develop two novel mutual information based loss functions, to (i) discover
proper prompt initialization for the downstream tasks and learn sufficient
task-relevant information from prompt tokens and (ii) encourage the output
representation from the pretrained language model to be more aware of the
task-relevant information captured in the learnt prompt. Extensive experiments
validate that InfoPrompt can significantly accelerate the convergence of the
prompt tuning and outperform traditional prompt tuning methods. Finally, we
provide a formal theoretical result for showing to show that gradient descent
type algorithm can be used to train our mutual information loss.
|
[
"cs.CL",
"cs.LG",
"stat.ML"
] | false |
2306.04980
|
2023-06-08T07:10:39Z
|
Assessing Phrase Break of ESL Speech with Pre-trained Language Models
and Large Language Models
|
[
"Zhiyi Wang",
"Shaoguang Mao",
"Wenshan Wu",
"Yan Xia",
"Yan Deng",
"Jonathan Tien"
] |
This work introduces approaches to assessing phrase breaks in ESL learners'
speech using pre-trained language models (PLMs) and large language models
(LLMs). There are two tasks: overall assessment of phrase break for a speech
clip and fine-grained assessment of every possible phrase break position. To
leverage NLP models, speech input is first force-aligned with texts, and then
pre-processed into a token sequence, including words and phrase break
information. To utilize PLMs, we propose a pre-training and fine-tuning
pipeline with the processed tokens. This process includes pre-training with a
replaced break token detection module and fine-tuning with text classification
and sequence labeling. To employ LLMs, we design prompts for ChatGPT. The
experiments show that with the PLMs, the dependence on labeled training data
has been greatly reduced, and the performance has improved. Meanwhile, we
verify that ChatGPT, a renowned LLM, has potential for further advancement in
this area.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2306.05052
|
2023-06-08T09:12:28Z
|
Interpretable Medical Diagnostics with Structured Data Extraction by
Large Language Models
|
[
"Aleksa Bisercic",
"Mladen Nikolic",
"Mihaela van der Schaar",
"Boris Delibasic",
"Pietro Lio",
"Andrija Petrovic"
] |
Tabular data is often hidden in text, particularly in medical diagnostic
reports. Traditional machine learning (ML) models designed to work with tabular
data, cannot effectively process information in such form. On the other hand,
large language models (LLMs) which excel at textual tasks, are probably not the
best tool for modeling tabular data. Therefore, we propose a novel, simple, and
effective methodology for extracting structured tabular data from textual
medical reports, called TEMED-LLM. Drawing upon the reasoning capabilities of
LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately
inferring tabular features, even when their names are not explicitly mentioned
in the text. This is achieved by combining domain-specific reasoning guidelines
with a proposed data validation and reasoning correction feedback loop. By
applying interpretable ML models such as decision trees and logistic regression
over the extracted and validated data, we obtain end-to-end interpretable
predictions. We demonstrate that our approach significantly outperforms
state-of-the-art text classification models in medical diagnostics. Given its
predictive performance, simplicity, and interpretability, TEMED-LLM underscores
the potential of leveraging LLMs to improve the performance and trustworthiness
of ML models in medical applications.
|
[
"cs.LG",
"cs.AI",
"cs.CL"
] | false |
2306.05077
|
2023-06-08T10:00:19Z
|
Improving Language Model Integration for Neural Machine Translation
|
[
"Christian Herold",
"Yingbo Gao",
"Mohammad Zeineldeen",
"Hermann Ney"
] |
The integration of language models for neural machine translation has been
extensively studied in the past. It has been shown that an external language
model, trained on additional target-side monolingual data, can help improve
translation quality. However, there has always been the assumption that the
translation model also learns an implicit target-side language model during
training, which interferes with the external language model at decoding time.
Recently, some works on automatic speech recognition have demonstrated that, if
the implicit language model is neutralized in decoding, further improvements
can be gained when integrating an external language model. In this work, we
transfer this concept to the task of machine translation and compare with the
most prominent way of including additional monolingual data - namely
back-translation. We find that accounting for the implicit language model
significantly boosts the performance of language model fusion, although this
approach is still outperformed by back-translation.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2306.05088
|
2023-06-08T10:42:44Z
|
The ART of Conversation: Measuring Phonetic Convergence and Deliberate
Imitation in L2-Speech with a Siamese RNN
|
[
"Zheng Yuan",
"Aldo Pastore",
"Dorina de Jong",
"Hao Xu",
"Luciano Fadiga",
"Alessandro D'Ausilio"
] |
Phonetic convergence describes the automatic and unconscious speech
adaptation of two interlocutors in a conversation. This paper proposes a
Siamese recurrent neural network (RNN) architecture to measure the convergence
of the holistic spectral characteristics of speech sounds in an L2-L2
interaction. We extend an alternating reading task (the ART) dataset by adding
20 native Slovak L2 English speakers. We train and test the Siamese RNN model
to measure phonetic convergence of L2 English speech from three different
native language groups: Italian (9 dyads), French (10 dyads) and Slovak (10
dyads). Our results indicate that the Siamese RNN model effectively captures
the dynamics of phonetic convergence and the speaker's imitation ability.
Moreover, this text-independent model is scalable and capable of handling
L1-induced speaker variability.
|
[
"cs.CL",
"cs.LG",
"cs.SD",
"eess.AS"
] | false |
2306.05116
|
2023-06-08T11:30:43Z
|
On Search Strategies for Document-Level Neural Machine Translation
|
[
"Christian Herold",
"Hermann Ney"
] |
Compared to sentence-level systems, document-level neural machine translation
(NMT) models produce a more consistent output across a document and are able to
better resolve ambiguities within the input. There are many works on
document-level NMT, mostly focusing on modifying the model architecture or
training strategy to better accommodate the additional context-input. On the
other hand, in most works, the question on how to perform search with the
trained model is scarcely discussed, sometimes not mentioned at all. In this
work, we aim to answer the question how to best utilize a context-aware
translation model in decoding. We start with the most popular document-level
NMT approach and compare different decoding schemes, some from the literature
and others proposed by us. In the comparison, we are using both, standard
automatic metrics, as well as specific linguistic phenomena on three standard
document-level translation benchmarks. We find that most commonly used decoding
strategies perform similar to each other and that higher quality context
information has the potential to further improve the translation.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2306.05183
|
2023-06-08T13:28:48Z
|
Improving Long Context Document-Level Machine Translation
|
[
"Christian Herold",
"Hermann Ney"
] |
Document-level context for neural machine translation (NMT) is crucial to
improve the translation consistency and cohesion, the translation of ambiguous
inputs, as well as several other linguistic phenomena. Many works have been
published on the topic of document-level NMT, but most restrict the system to
only local context, typically including just the one or two preceding sentences
as additional information. This might be enough to resolve some ambiguous
inputs, but it is probably not sufficient to capture some document-level
information like the topic or style of a conversation. When increasing the
context size beyond just the local context, there are two challenges: (i)
the~memory usage increases exponentially (ii) the translation performance
starts to degrade. We argue that the widely-used attention mechanism is
responsible for both issues. Therefore, we propose a constrained attention
variant that focuses the attention on the most relevant parts of the sequence,
while simultaneously reducing the memory consumption. For evaluation, we
utilize targeted test sets in combination with novel evaluation techniques to
analyze the translations in regards to specific discourse-related phenomena. We
find that our approach is a good compromise between sentence-level NMT vs
attending to the full context, especially in low resource scenarios.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
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