83 research outputs found
La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.
Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
A review on visual privacy preservation techniques for active and assisted living
This paper reviews the state of the art in visual privacy protection techniques, with particular attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A novel taxonomy with which state-of-the-art visual privacy protection methods can be classified is introduced. Perceptual obfuscation methods, a category in this taxonomy, is highlighted. These are a category of visual privacy preservation techniques, particularly relevant when considering scenarios that come under video-based AAL monitoring. Obfuscation against machine learning models is also explored. A high-level classification scheme of privacy by design, as defined by experts in privacy and data protection law, is connected to the proposed taxonomy of visual privacy preservation techniques. Finally, we note open questions that exist in the field and introduce the reader to some exciting avenues for future research in the area of visual privacy.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is part of the visuAAL project on Privacy-Aware and Acceptable Video-Based Technologies and Services for Active and Assisted Living (https://www.visuaal-itn.eu/). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 861091. The authors would also like to acknowledge the contribution of COST Action CA19121 - GoodBrother, Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living (https://goodbrother.eu/), supported by COST (European Cooperation in Science and Technology) (https://www.cost.eu/)
Towards Generalizable Deep Image Matting: Decomposition, Interaction, and Merging
Image matting refers to extracting the precise alpha mattes from images, playing a critical role in many downstream applications. Despite extensive attention, key challenges persist and motivate the research presented in this thesis.
One major challenge is the reliance of auxiliary inputs in previous methods, hindering real-time practicality. To address this, we introduce fully automatic image matting by decomposing the task into high-level semantic segmentation and low-level details matting. We then incorporate plug-in modules to enhance the interaction between the sub-tasks through feature integration. Furthermore, we propose an attention-based mechanism to guide the matting process through collaboration merging.
Another challenge lies in limited matting datasets, resulting in reliance on composite images and inferior performance on images in the wild. In response, our research proposes a composition route to mitigate the discrepancies and result in remarkable generalization ability. Additionally, we construct numerous large datasets of high-quality real-world images with manually labeled alpha mattes, providing a solid foundation for training and evaluation.
Moreover, our research uncovers new observations that warrant further investigation. Firstly, we systematically analyze and address privacy issues that have been neglected in previous portrait matting research. Secondly, we explore the adaptation of automatic matting methods to non-salient or transparent categories beyond salient ones. Furthermore, we collaborate with language modality to achieve a more controllable matting process, enabling specific target selection at a low cost. To validate our studies, we conduct extensive experiments and provide all codes and datasets through the link (https://github.com/JizhiziLi/).
We believe that the analyses, methods, and datasets presented in this thesis will offer valuable insights for future research endeavors in the field of image matting
Does Image Anonymization Impact Computer Vision Training?
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.Comment: Accepted at CVPR Workshop on Autonomous Driving 202
StyleGAN as a Utility-Preserving Face De-identification Method
Face de-identification methods have been proposed to preserve users' privacy
by obscuring their faces. These methods, however, can degrade the quality of
photos, and they usually do not preserve the utility of faces, i.e., their age,
gender, pose, and facial expression. Recently, GANs, such as StyleGAN, have
been proposed, which generate realistic, high-quality imaginary faces. In this
paper, we investigate the use of StyleGAN in generating de-identified faces
through style mixing. We examined this de-identification method for preserving
utility and privacy by implementing several face detection, verification, and
identification attacks and conducting a user study. The results from our
extensive experiments, human evaluation, and comparison with two
state-of-the-art methods, i.e., CIAGAN and DeepPrivacy, show that StyleGAN
performs on par or better than these methods, preserving users' privacy and
images' utility. In particular, the results of the machine learning-based
experiments show that StyleGAN0-4 preserves utility better than CIAGAN and
DeepPrivacy while preserving privacy at the same level. StyleGAN0-3 preserves
utility at the same level while providing more privacy. In this paper, for the
first time, we also performed a carefully designed user study to examine both
privacy and utility-preserving properties of StyleGAN0-3, 0-4, and 0-5, as well
as CIAGAN and DeepPrivacy from the human observers' perspectives. Our
statistical tests showed that participants tend to verify and identify
StyleGAN0-5 images more easily than DeepPrivacy images. All the methods but
StyleGAN0-5 had significantly lower identification rates than CIAGAN. Regarding
utility, as expected, StyleGAN0-5 performed significantly better in preserving
some attributes. Among all methods, on average, participants believe gender has
been preserved the most while naturalness has been preserved the least
HFORD: High-Fidelity and Occlusion-Robust De-identification for Face Privacy Protection
With the popularity of smart devices and the development of computer vision
technology, concerns about face privacy protection are growing. The face
de-identification technique is a practical way to solve the identity protection
problem. The existing facial de-identification methods have revealed several
problems, including the impact on the realism of anonymized results when faced
with occlusions and the inability to maintain identity-irrelevant details in
anonymized results. We present a High-Fidelity and Occlusion-Robust
De-identification (HFORD) method to deal with these issues. This approach can
disentangle identities and attributes while preserving image-specific details
such as background, facial features (e.g., wrinkles), and lighting, even in
occluded scenes. To disentangle the latent codes in the GAN inversion space, we
introduce an Identity Disentanglement Module (IDM). This module selects the
latent codes that are closely related to the identity. It further separates the
latent codes into identity-related codes and attribute-related codes, enabling
the network to preserve attributes while only modifying the identity. To ensure
the preservation of image details and enhance the network's robustness to
occlusions, we propose an Attribute Retention Module (ARM). This module
adaptively preserves identity-irrelevant details and facial occlusions and
blends them into the generated results in a modulated manner. Extensive
experiments show that our method has higher quality, better detail fidelity,
and stronger occlusion robustness than other face de-identification methods
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Robust Machine Learning by Integrating Context
Intelligent software has the potential to transform our society. It is becoming the building block for many systems in the real world. However, despite the excellent performance of machine learning models on benchmarks, state-of-the-art methods like neural networks often fail once they encounter realistic settings. Since neural networks often learn correlations without reasoning with the right signals and knowledge, they fail when facing shifting distributions, unforeseen corruptions, and worst-case scenarios. Since neural networks are black-box models, they are not interpretable or trusted by the user. We need to build robust models for machine learning to be confidently and responsibly deployed in the most critical applications and systems.
In this dissertation, I introduce our robust machine learning systems advancements by tightly integrating context into algorithms. The context has two aspects: the intrinsic structure of natural data, and the extrinsic structure from domain knowledge. Both are crucial: By capitalizing on the intrinsic structure in natural data, my work has shown that we can create robust machine learning systems, even in the worst case, an analytical result that also enjoys strong empirical gains.
Through integrating external knowledge, such as the association between tasks and causal structure, my framework can instruct models to use the right signals for inference, enabling new opportunities for controllable and interpretable models.
This thesis consists of three parts. In the first part, I aim to cover three works that use the intrinsic structure as a constraint to achieve robust inference. I present our framework that performs test-time optimization to respect the natural constraint, which is captured by self-supervised tasks. I illustrate that test-time optimization improves out-of-distribution generalization and adversarial robustness. Besides the inference algorithm, I show that intrinsic structure through discrete representations also improves out-of-distribution robustness.
In the second part of the thesis, I then detail my work using external domain knowledge. I first introduce using causal structure from external domain knowledge to improve domain generalization robustness. I then show how the association of multiple tasks and regularization objectives helps robustness.
In the final part of this dissertation, I show three works on trustworthy and reliable foundation models, a general-purpose model that will be the foundation for many AI applications. I show a framework that uses context to secure, interpret, and control foundation models
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images
Self-supervised pretraining has been observed to be effective at improving
feature representations for transfer learning, leveraging large amounts of
unlabelled data. This review summarizes recent research into its usage in
X-ray, computed tomography, magnetic resonance, and ultrasound imaging,
concentrating on studies that compare self-supervised pretraining to fully
supervised learning for diagnostic tasks such as classification and
segmentation. The most pertinent finding is that self-supervised pretraining
generally improves downstream task performance compared to full supervision,
most prominently when unlabelled examples greatly outnumber labelled examples.
Based on the aggregate evidence, recommendations are provided for practitioners
considering using self-supervised learning. Motivated by limitations identified
in current research, directions and practices for future study are suggested,
such as integrating clinical knowledge with theoretically justified
self-supervised learning methods, evaluating on public datasets, growing the
modest body of evidence for ultrasound, and characterizing the impact of
self-supervised pretraining on generalization.Comment: 32 pages, 6 figures, a literature survey submitted to BMC Medical
Imagin
Visual Content Privacy Protection: A Survey
Vision is the most important sense for people, and it is also one of the main
ways of cognition. As a result, people tend to utilize visual content to
capture and share their life experiences, which greatly facilitates the
transfer of information. Meanwhile, it also increases the risk of privacy
violations, e.g., an image or video can reveal different kinds of
privacy-sensitive information. Researchers have been working continuously to
develop targeted privacy protection solutions, and there are several surveys to
summarize them from certain perspectives. However, these surveys are either
problem-driven, scenario-specific, or technology-specific, making it difficult
for them to summarize the existing solutions in a macroscopic way. In this
survey, a framework that encompasses various concerns and solutions for visual
privacy is proposed, which allows for a macro understanding of privacy concerns
from a comprehensive level. It is based on the fact that privacy concerns have
corresponding adversaries, and divides privacy protection into three
categories, based on computer vision (CV) adversary, based on human vision (HV)
adversary, and based on CV \& HV adversary. For each category, we analyze the
characteristics of the main approaches to privacy protection, and then
systematically review representative solutions. Open challenges and future
directions for visual privacy protection are also discussed.Comment: 24 pages, 13 figure
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