6,254 research outputs found
Enhancing Face Recognition with Deep Learning Architectures: A Comprehensive Review
The progression of information discernment via facial identification and the emergence of innovative frameworks has exhibited remarkable strides in recent years. This phenomenon has been particularly pronounced within the realm of verifying individual credentials, a practice prominently harnessed by law enforcement agencies to advance the field of forensic science. A multitude of scholarly endeavors have been dedicated to the application of deep learning techniques within machine learning models. These endeavors aim to facilitate the extraction of distinctive features and subsequent classification, thereby elevating the precision of unique individual recognition. In the context of this scholarly inquiry, the focal point resides in the exploration of deep learning methodologies tailored for the realm of facial recognition and its subsequent matching processes. This exploration centers on the augmentation of accuracy through the meticulous process of training models with expansive datasets. Within the confines of this research paper, a comprehensive survey is conducted, encompassing an array of diverse strategies utilized in facial recognition. This survey, in turn, delves into the intricacies and challenges that underlie the intricate field of facial recognition within imagery analysis
ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?
ChatGPT embarks on a new era of artificial intelligence and will
revolutionize the way we approach intelligent traffic safety systems. This
paper begins with a brief introduction about the development of large language
models (LLMs). Next, we exemplify using ChatGPT to address key traffic safety
issues. Furthermore, we discuss the controversies surrounding LLMs, raise
critical questions for their deployment, and provide our solutions. Moreover,
we propose an idea of multi-modality representation learning for smarter
traffic safety decision-making and open more questions for application
improvement. We believe that LLM will both shape and potentially facilitate
components of traffic safety research.Comment: Submitted to Nature - Machine Intelligence (Revised and Extended
Proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET 2013)
"This book contains the proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET) 2013 which was held on 16.-17.September 2013 in Paphos (Cyprus) in conjunction with the EC-TEL conference. The workshop and hence the proceedings are divided in two parts: on Day 1 the EuroPLOT project and its results are introduced, with papers about the specific case studies and their evaluation. On Day 2, peer-reviewed papers are presented which address specific topics and issues going beyond the EuroPLOT scope. This workshop is one of the deliverables (D 2.6) of the EuroPLOT project, which has been funded from November 2010 – October 2013 by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission through the Lifelong Learning Programme (LLL) by grant #511633. The purpose of this project was to develop and evaluate Persuasive Learning Objects and Technologies (PLOTS), based on ideas of BJ Fogg. The purpose of this workshop is to summarize the findings obtained during this project and disseminate them to an interested audience. Furthermore, it shall foster discussions about the future of persuasive technology and design in the context of learning, education and teaching. The international community working in this area of research is relatively small. Nevertheless, we have received a number of high-quality submissions which went through a peer-review process before being selected for presentation and publication. We hope that the information found in this book is useful to the reader and that more interest in this novel approach of persuasive design for teaching/education/learning is stimulated. We are very grateful to the organisers of EC-TEL 2013 for allowing to host IWEPLET 2013 within their organisational facilities which helped us a lot in preparing this event. I am also very grateful to everyone in the EuroPLOT team for collaborating so effectively in these three years towards creating excellent outputs, and for being such a nice group with a very positive spirit also beyond work. And finally I would like to thank the EACEA for providing the financial resources for the EuroPLOT project and for being very helpful when needed. This funding made it possible to organise the IWEPLET workshop without charging a fee from the participants.
Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving
Collaborative perception has recently gained significant attention in
autonomous driving, improving perception quality by enabling the exchange of
additional information among vehicles. However, deploying collaborative
perception systems can lead to domain shifts due to diverse environmental
conditions and data heterogeneity among connected and autonomous vehicles
(CAVs). To address these challenges, we propose a unified domain generalization
framework applicable in both training and inference stages of collaborative
perception. In the training phase, we introduce an Amplitude Augmentation
(AmpAug) method to augment low-frequency image variations, broadening the
model's ability to learn across various domains. We also employ a
meta-consistency training scheme to simulate domain shifts, optimizing the
model with a carefully designed consistency loss to encourage domain-invariant
representations. In the inference phase, we introduce an intra-system domain
alignment mechanism to reduce or potentially eliminate the domain discrepancy
among CAVs prior to inference. Comprehensive experiments substantiate the
effectiveness of our method in comparison with the existing state-of-the-art
works. Code will be released at https://github.com/DG-CAVs/DG-CoPerception.git
Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery
Understanding urban dynamics and promoting sustainable development requires
comprehensive insights about buildings. While geospatial artificial
intelligence has advanced the extraction of such details from Earth
observational data, existing methods often suffer from computational
inefficiencies and inconsistencies when compiling unified building-related
datasets for practical applications. To bridge this gap, we introduce the
Multi-task Building Refiner (MT-BR), an adaptable neural network tailored for
simultaneous extraction of spatial and attributional building details from
high-resolution satellite imagery, exemplified by building rooftops, urban
functional types, and roof architectural types. Notably, MT-BR can be
fine-tuned to incorporate additional building details, extending its
applicability. For large-scale applications, we devise a novel spatial sampling
scheme that strategically selects limited but representative image samples.
This process optimizes both the spatial distribution of samples and the urban
environmental characteristics they contain, thus enhancing extraction
effectiveness while curtailing data preparation expenditures. We further
enhance MT-BR's predictive performance and generalization capabilities through
the integration of advanced augmentation techniques. Our quantitative results
highlight the efficacy of the proposed methods. Specifically, networks trained
with datasets curated via our sampling method demonstrate improved predictive
accuracy relative to those using alternative sampling approaches, with no
alterations to network architecture. Moreover, MT-BR consistently outperforms
other state-of-the-art methods in extracting building details across various
metrics. The real-world practicality is also demonstrated in an application
across Shanghai, generating a unified dataset that encompasses both the spatial
and attributional details of buildings
Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models
Machine learning techniques are now integral to the advancement of
intelligent urban services, playing a crucial role in elevating the efficiency,
sustainability, and livability of urban environments. The recent emergence of
foundation models such as ChatGPT marks a revolutionary shift in the fields of
machine learning and artificial intelligence. Their unparalleled capabilities
in contextual understanding, problem solving, and adaptability across a wide
range of tasks suggest that integrating these models into urban domains could
have a transformative impact on the development of smart cities. Despite
growing interest in Urban Foundation Models~(UFMs), this burgeoning field faces
challenges such as a lack of clear definitions, systematic reviews, and
universalizable solutions. To this end, this paper first introduces the concept
of UFM and discusses the unique challenges involved in building them. We then
propose a data-centric taxonomy that categorizes current UFM-related works,
based on urban data modalities and types. Furthermore, to foster advancement in
this field, we present a promising framework aimed at the prospective
realization of UFMs, designed to overcome the identified challenges.
Additionally, we explore the application landscape of UFMs, detailing their
potential impact in various urban contexts. Relevant papers and open-source
resources have been collated and are continuously updated at
https://github.com/usail-hkust/Awesome-Urban-Foundation-Models
Thresholded Covering Algorithms for Robust and Max-Min Optimization
The general problem of robust optimization is this: one of several possible
scenarios will appear tomorrow, but things are more expensive tomorrow than
they are today. What should you anticipatorily buy today, so that the
worst-case cost (summed over both days) is minimized? Feige et al. and
Khandekar et al. considered the k-robust model where the possible outcomes
tomorrow are given by all demand-subsets of size k, and gave algorithms for the
set cover problem, and the Steiner tree and facility location problems in this
model, respectively.
In this paper, we give the following simple and intuitive template for
k-robust problems: "having built some anticipatory solution, if there exists a
single demand whose augmentation cost is larger than some threshold, augment
the anticipatory solution to cover this demand as well, and repeat". In this
paper we show that this template gives us improved approximation algorithms for
k-robust Steiner tree and set cover, and the first approximation algorithms for
k-robust Steiner forest, minimum-cut and multicut. All our approximation ratios
(except for multicut) are almost best possible.
As a by-product of our techniques, we also get algorithms for max-min
problems of the form: "given a covering problem instance, which k of the
elements are costliest to cover?".Comment: 24 page
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