931 research outputs found
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks
This work shows that it is possible to fool/attack recent state-of-the-art
face detectors which are based on the single-stage networks. Successfully
attacking face detectors could be a serious malware vulnerability when
deploying a smart surveillance system utilizing face detectors. We show that
existing adversarial perturbation methods are not effective to perform such an
attack, especially when there are multiple faces in the input image. This is
because the adversarial perturbation specifically generated for one face may
disrupt the adversarial perturbation for another face. In this paper, we call
this problem the Instance Perturbation Interference (IPI) problem. This IPI
problem is addressed by studying the relationship between the deep neural
network receptive field and the adversarial perturbation. As such, we propose
the Localized Instance Perturbation (LIP) that uses adversarial perturbation
constrained to the Effective Receptive Field (ERF) of a target to perform the
attack. Experiment results show the LIP method massively outperforms existing
adversarial perturbation generation methods -- often by a factor of 2 to 10.Comment: to appear ECCV 2018 (accepted version
A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation
Body language (BL) refers to the non-verbal communication expressed through
physical movements, gestures, facial expressions, and postures. It is a form of
communication that conveys information, emotions, attitudes, and intentions
without the use of spoken or written words. It plays a crucial role in
interpersonal interactions and can complement or even override verbal
communication. Deep multi-modal learning techniques have shown promise in
understanding and analyzing these diverse aspects of BL. The survey emphasizes
their applications to BL generation and recognition. Several common BLs are
considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and
Talking Head (TH), and we have conducted an analysis and established the
connections among these four BL for the first time. Their generation and
recognition often involve multi-modal approaches. Benchmark datasets for BL
research are well collected and organized, along with the evaluation of SOTA
methods on these datasets. The survey highlights challenges such as limited
labeled data, multi-modal learning, and the need for domain adaptation to
generalize models to unseen speakers or languages. Future research directions
are presented, including exploring self-supervised learning techniques,
integrating contextual information from other modalities, and exploiting
large-scale pre-trained multi-modal models. In summary, this survey paper
provides a comprehensive understanding of deep multi-modal learning for various
BL generations and recognitions for the first time. By analyzing advancements,
challenges, and future directions, it serves as a valuable resource for
researchers and practitioners in advancing this field. n addition, we maintain
a continuously updated paper list for deep multi-modal learning for BL
recognition and generation: https://github.com/wentaoL86/awesome-body-language
BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues
Recent progress in fine-grained gesture and action classification, and
machine translation, point to the possibility of automated sign language
recognition becoming a reality. A key stumbling block in making progress
towards this goal is a lack of appropriate training data, stemming from the
high complexity of sign annotation and a limited supply of qualified
annotators. In this work, we introduce a new scalable approach to data
collection for sign recognition in continuous videos. We make use of
weakly-aligned subtitles for broadcast footage together with a keyword spotting
method to automatically localise sign-instances for a vocabulary of 1,000 signs
in 1,000 hours of video. We make the following contributions: (1) We show how
to use mouthing cues from signers to obtain high-quality annotations from video
data - the result is the BSL-1K dataset, a collection of British Sign Language
(BSL) signs of unprecedented scale; (2) We show that we can use BSL-1K to train
strong sign recognition models for co-articulated signs in BSL and that these
models additionally form excellent pretraining for other sign languages and
benchmarks - we exceed the state of the art on both the MSASL and WLASL
benchmarks. Finally, (3) we propose new large-scale evaluation sets for the
tasks of sign recognition and sign spotting and provide baselines which we hope
will serve to stimulate research in this area.Comment: Appears in: European Conference on Computer Vision 2020 (ECCV 2020).
28 page
Data and methods for a visual understanding of sign languages
Signed languages are complete and natural languages used as the first or preferred mode of communication by millions of people worldwide. However, they, unfortunately, continue to be marginalized languages. Designing, building, and evaluating models that work on sign languages presents compelling research challenges and requires interdisciplinary and collaborative efforts. The recent advances in Machine Learning (ML) and Artificial Intelligence (AI) has the power to enable better accessibility to sign language users and narrow down the existing communication barrier between the Deaf community and non-sign language users. However, recent AI-powered technologies still do not account for sign language in their pipelines. This is mainly because sign languages are visual languages, that use manual and non-manual features to convey information, and do not have a standard written form. Thus, the goal of this thesis is to contribute to the development of new technologies that account for sign language by creating large-scale multimodal resources suitable for training modern data-hungry machine learning models and developing automatic systems that focus on computer vision tasks related to sign language that aims at learning better visual understanding of sign languages.
Thus, in Part I, we introduce the How2Sign dataset, which is a large-scale collection of multimodal and multiview sign language videos in American Sign Language. In Part II, we contribute to the development of technologies that account for sign languages by presenting in Chapter 4 a framework called Spot-Align, based on sign spotting methods, to automatically annotate sign instances in continuous sign language. We further present the benefits of this framework and establish a baseline for the sign language recognition task on the How2Sign dataset. In addition to that, in Chapter 5 we benefit from the different annotations and modalities of the How2Sign to explore sign language video retrieval by learning cross-modal embeddings. Later in Chapter 6, we explore sign language video generation by applying Generative Adversarial Networks to the sign language domain and assess if and how well sign language users can understand automatically generated sign language videos by proposing an evaluation protocol based on How2Sign topics and English translationLes llengües de signes són llengües completes i naturals que utilitzen milions de persones de tot el món com mode de comunicació primer o preferit. Tanmateix, malauradament, continuen essent llengües marginades. Dissenyar, construir i avaluar tecnologies que funcionin amb les llengües de signes presenta reptes de recerca que requereixen d’esforços interdisciplinaris i col·laboratius. Els avenços recents en l’aprenentatge automà tic i la intel·ligència artificial (IA) poden millorar l’accessibilitat tecnològica dels signants, i alhora reduir la barrera de comunicació existent entre la comunitat sorda i les persones no-signants. Tanmateix, les tecnologies més modernes en IA encara no consideren les llengües de signes en les seves interfÃcies amb l’usuari. Això es deu principalment a que les llengües de signes són llenguatges visuals, que utilitzen caracterÃstiques manuals i no manuals per transmetre informació, i no tenen una forma escrita està ndard. Els objectius principals d’aquesta tesi són la creació de recursos multimodals a gran escala adequats per entrenar models d’aprenentatge automà tic per a llengües de signes, i desenvolupar sistemes de visió per computador adreçats a una millor comprensió automà tica de les llengües de signes. AixÃ, a la Part I presentem la base de dades How2Sign, una gran col·lecció multimodal i multivista de vÃdeos de la llengua de signes nord-americana. A la Part II, contribuïm al desenvolupament de tecnologia per a llengües de signes, presentant al capÃtol 4 una solució per anotar signes automà ticament anomenada Spot-Align, basada en mètodes de localització de signes en seqüències contÃnues de signes. Després, presentem els avantatges d’aquesta solució i proporcionem uns primers resultats per la tasca de reconeixement de la llengua de signes a la base de dades How2Sign. A continuació, al capÃtol 5 aprofitem de les anotacions i diverses modalitats de How2Sign per explorar la cerca de vÃdeos en llengua de signes a partir de l’entrenament d’incrustacions multimodals. Finalment, al capÃtol 6, explorem la generació de vÃdeos en llengua de signes aplicant xarxes adversà ries generatives al domini de la llengua de signes. Avaluem fins a quin punt els signants poden entendre els vÃdeos generats automà ticament, proposant un nou protocol d’avaluació basat en les categories dins de How2Sign i la traducció dels vÃdeos a l’anglès escritLas lenguas de signos son lenguas completas y naturales que utilizan millones de personas
de todo el mundo como modo de comunicación primero o preferido. Sin embargo, desgraciadamente,
siguen siendo lenguas marginadas. Diseñar, construir y evaluar tecnologÃas
que funcionen con las lenguas de signos presenta retos de investigación que requieren
esfuerzos interdisciplinares y colaborativos. Los avances recientes en el aprendizaje automático
y la inteligencia artificial (IA) pueden mejorar la accesibilidad tecnológica de
los signantes, al tiempo que reducir la barrera de comunicación existente entre la comunidad
sorda y las personas no signantes. Sin embargo, las tecnologÃas más modernas en
IA todavÃa no consideran las lenguas de signos en sus interfaces con el usuario. Esto
se debe principalmente a que las lenguas de signos son lenguajes visuales, que utilizan
caracterÃsticas manuales y no manuales para transmitir información, y carecen de una
forma escrita estándar. Los principales objetivos de esta tesis son la creación de recursos
multimodales a gran escala adecuados para entrenar modelos de aprendizaje automático
para lenguas de signos, y desarrollar sistemas de visión por computador dirigidos a una
mejor comprensión automática de las lenguas de signos.
AsÃ, en la Parte I presentamos la base de datos How2Sign, una gran colección multimodal
y multivista de vÃdeos de lenguaje la lengua de signos estadounidense. En la Part II,
contribuimos al desarrollo de tecnologÃa para lenguas de signos, presentando en el capÃtulo
4 una solución para anotar signos automáticamente llamada Spot-Align, basada en
métodos de localización de signos en secuencias continuas de signos. Después, presentamos
las ventajas de esta solución y proporcionamos unos primeros resultados por la tarea
de reconocimiento de la lengua de signos en la base de datos How2Sign. A continuación,
en el capÃtulo 5 aprovechamos de las anotaciones y diversas modalidades de How2Sign
para explorar la búsqueda de vÃdeos en lengua de signos a partir del entrenamiento de
incrustaciones multimodales. Finalmente, en el capÃtulo 6, exploramos la generación
de vÃdeos en lengua de signos aplicando redes adversarias generativas al dominio de la
lengua de signos. Evaluamos hasta qué punto los signantes pueden entender los vÃdeos
generados automáticamente, proponiendo un nuevo protocolo de evaluación basado en
las categorÃas dentro de How2Sign y la traducción de los vÃdeos al inglés escrito.Teoria del Senyal i Comunicacion
Data and methods for a visual understanding of sign languages
Signed languages are complete and natural languages used as the first or preferred mode of communication by millions of people worldwide. However, they, unfortunately, continue to be marginalized languages. Designing, building, and evaluating models that work on sign languages presents compelling research challenges and requires interdisciplinary and collaborative efforts. The recent advances in Machine Learning (ML) and Artificial Intelligence (AI) has the power to enable better accessibility to sign language users and narrow down the existing communication barrier between the Deaf community and non-sign language users. However, recent AI-powered technologies still do not account for sign language in their pipelines. This is mainly because sign languages are visual languages, that use manual and non-manual features to convey information, and do not have a standard written form. Thus, the goal of this thesis is to contribute to the development of new technologies that account for sign language by creating large-scale multimodal resources suitable for training modern data-hungry machine learning models and developing automatic systems that focus on computer vision tasks related to sign language that aims at learning better visual understanding of sign languages.
Thus, in Part I, we introduce the How2Sign dataset, which is a large-scale collection of multimodal and multiview sign language videos in American Sign Language. In Part II, we contribute to the development of technologies that account for sign languages by presenting in Chapter 4 a framework called Spot-Align, based on sign spotting methods, to automatically annotate sign instances in continuous sign language. We further present the benefits of this framework and establish a baseline for the sign language recognition task on the How2Sign dataset. In addition to that, in Chapter 5 we benefit from the different annotations and modalities of the How2Sign to explore sign language video retrieval by learning cross-modal embeddings. Later in Chapter 6, we explore sign language video generation by applying Generative Adversarial Networks to the sign language domain and assess if and how well sign language users can understand automatically generated sign language videos by proposing an evaluation protocol based on How2Sign topics and English translationLes llengües de signes són llengües completes i naturals que utilitzen milions de persones de tot el món com mode de comunicació primer o preferit. Tanmateix, malauradament, continuen essent llengües marginades. Dissenyar, construir i avaluar tecnologies que funcionin amb les llengües de signes presenta reptes de recerca que requereixen d’esforços interdisciplinaris i col·laboratius. Els avenços recents en l’aprenentatge automà tic i la intel·ligència artificial (IA) poden millorar l’accessibilitat tecnològica dels signants, i alhora reduir la barrera de comunicació existent entre la comunitat sorda i les persones no-signants. Tanmateix, les tecnologies més modernes en IA encara no consideren les llengües de signes en les seves interfÃcies amb l’usuari. Això es deu principalment a que les llengües de signes són llenguatges visuals, que utilitzen caracterÃstiques manuals i no manuals per transmetre informació, i no tenen una forma escrita està ndard. Els objectius principals d’aquesta tesi són la creació de recursos multimodals a gran escala adequats per entrenar models d’aprenentatge automà tic per a llengües de signes, i desenvolupar sistemes de visió per computador adreçats a una millor comprensió automà tica de les llengües de signes. AixÃ, a la Part I presentem la base de dades How2Sign, una gran col·lecció multimodal i multivista de vÃdeos de la llengua de signes nord-americana. A la Part II, contribuïm al desenvolupament de tecnologia per a llengües de signes, presentant al capÃtol 4 una solució per anotar signes automà ticament anomenada Spot-Align, basada en mètodes de localització de signes en seqüències contÃnues de signes. Després, presentem els avantatges d’aquesta solució i proporcionem uns primers resultats per la tasca de reconeixement de la llengua de signes a la base de dades How2Sign. A continuació, al capÃtol 5 aprofitem de les anotacions i diverses modalitats de How2Sign per explorar la cerca de vÃdeos en llengua de signes a partir de l’entrenament d’incrustacions multimodals. Finalment, al capÃtol 6, explorem la generació de vÃdeos en llengua de signes aplicant xarxes adversà ries generatives al domini de la llengua de signes. Avaluem fins a quin punt els signants poden entendre els vÃdeos generats automà ticament, proposant un nou protocol d’avaluació basat en les categories dins de How2Sign i la traducció dels vÃdeos a l’anglès escritLas lenguas de signos son lenguas completas y naturales que utilizan millones de personas
de todo el mundo como modo de comunicación primero o preferido. Sin embargo, desgraciadamente,
siguen siendo lenguas marginadas. Diseñar, construir y evaluar tecnologÃas
que funcionen con las lenguas de signos presenta retos de investigación que requieren
esfuerzos interdisciplinares y colaborativos. Los avances recientes en el aprendizaje automático
y la inteligencia artificial (IA) pueden mejorar la accesibilidad tecnológica de
los signantes, al tiempo que reducir la barrera de comunicación existente entre la comunidad
sorda y las personas no signantes. Sin embargo, las tecnologÃas más modernas en
IA todavÃa no consideran las lenguas de signos en sus interfaces con el usuario. Esto
se debe principalmente a que las lenguas de signos son lenguajes visuales, que utilizan
caracterÃsticas manuales y no manuales para transmitir información, y carecen de una
forma escrita estándar. Los principales objetivos de esta tesis son la creación de recursos
multimodales a gran escala adecuados para entrenar modelos de aprendizaje automático
para lenguas de signos, y desarrollar sistemas de visión por computador dirigidos a una
mejor comprensión automática de las lenguas de signos.
AsÃ, en la Parte I presentamos la base de datos How2Sign, una gran colección multimodal
y multivista de vÃdeos de lenguaje la lengua de signos estadounidense. En la Part II,
contribuimos al desarrollo de tecnologÃa para lenguas de signos, presentando en el capÃtulo
4 una solución para anotar signos automáticamente llamada Spot-Align, basada en
métodos de localización de signos en secuencias continuas de signos. Después, presentamos
las ventajas de esta solución y proporcionamos unos primeros resultados por la tarea
de reconocimiento de la lengua de signos en la base de datos How2Sign. A continuación,
en el capÃtulo 5 aprovechamos de las anotaciones y diversas modalidades de How2Sign
para explorar la búsqueda de vÃdeos en lengua de signos a partir del entrenamiento de
incrustaciones multimodales. Finalmente, en el capÃtulo 6, exploramos la generación
de vÃdeos en lengua de signos aplicando redes adversarias generativas al dominio de la
lengua de signos. Evaluamos hasta qué punto los signantes pueden entender los vÃdeos
generados automáticamente, proponiendo un nuevo protocolo de evaluación basado en
las categorÃas dentro de How2Sign y la traducción de los vÃdeos al inglés escrito.Postprint (published version
Reconhecimento de expressões faciais na lÃngua de sinais brasileira por meio do sistema de códigos de ação facial
Orientadores: Paula Dornhofer Paro Costa, Kate Mamhy Oliveira KumadaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Surdos ao redor do mundo usam a lÃngua de sinais para se comunicarem, porém, apesar da ampla disseminação dessas lÃnguas, os surdos ou indivÃduos com deficiência auditiva ainda enfrentam dificuldades na comunicação com ouvintes, na ausência de um intérprete. Tais dificuldades impactam negativamente o acesso dos surdos à educação, ao mercado de trabalho e aos serviços públicos em geral. As tecnologias assistivas, como o Reconhecimento Automático de LÃngua de Sinais, do inglês Automatic Sign Language Recognition (ASLR), visam superar esses obstáculos de comunicação. No entanto, o desenvolvimento de sistemas ASLR confiáveis apresenta vários desafios devido à complexidade linguÃstica das lÃnguas de sinais. As lÃnguas de sinais (LSs) são sistemas linguÃsticos visuoespaciais que, como qualquer outra lÃngua humana, apresentam variações linguÃsticas globais e regionais, além de um sistema gramatical. Além disso, as lÃnguas de sinais não se baseiam apenas em gestos manuais, mas também em marcadores não-manuais, como expressões faciais. Nas lÃnguas de sinais, as expressões faciais podem diferenciar itens lexicais, participar da construção sintática e contribuir para processos de intensificação, entre outras funções gramaticais e afetivas. Associado aos modelos de reconhecimento de gestos, o reconhecimento da expressões faciais é um componente essencial da tecnologia ASLR. Neste trabalho, propomos um sistema automático de reconhecimento de expressões faciais para Libras, a lÃngua brasileira de sinais. A partir de uma pesquisa bibliográfica, apresentamos um estudo da linguagem e uma taxonomia diferente para expressões faciais de Libras associadas ao sistema de codificação de ações faciais. Além disso, um conjunto de dados de expressões faciais em Libras foi criado. Com base em experimentos, a decisão sobre a construção do nosso sistema foi através de pré-processamento e modelos de reconhecimento. Os recursos obtidos para a classificação das ações faciais são resultado da aplicação combinada de uma região de interesse, e informações geométricas da face dado embasamento teórico e a obtenção de desempenho melhor do que outras etapas testadas. Quanto aos classificadores, o SqueezeNet apresentou melhores taxas de precisão. Com isso, o potencial do modelo proposto vem da análise de 77% da acurácia média de reconhecimento das expressões faciais de Libras. Este trabalho contribui para o crescimento dos estudos que envolvem a visão computacional e os aspectos de reconhecimento da estrutura das expressões faciais da lÃngua de sinais, e tem como foco principal a importância da anotação da ação facial de forma automatizadaAbstract: Deaf people around the world use sign languages to communicate but, despite the wide dissemination of such languages, deaf or hard of hearing individuals still face difficulties in communicating with hearing individuals, in the absence of an interpreter. Such difficulties negatively impact the access of deaf individuals to education, to the job market, and to public services in general. Assistive technologies, such as Automatic Sign Language Recognition (ASLR), aim at overcoming such communication obstacles. However, the development of reliable ASLR systems imposes numerous challenges due the linguistic complexity of sign languages. Sign languages (SLs) are visuospatial linguistic systems that, like any other human language, present global and regional linguistic variations, and a grammatical system. Also, sign languages do not rely only on manual gestures but also non-manual markers, such as facial expressions. In SL, facial expressions may differentiate lexical items, participate in syntactic construction, and contribute to change the intensity of a sentence, among other grammatical and affective functions. Associated with the gesture recognition models, facial expression recognition (FER) is an essential component of ASLR technology. In this work, we propose an automatic facial expression recognition (FER) system for Brazilian Sign Language (Libras). Derived from a literature survey, we present a language study and a different taxonomy for facial expressions of Libras associated with the Facial Action Coding System (FACS). Also, a dataset of facial expressions in Libras was created. An experimental setting was done for the construction of our framework for a preprocessing stage and recognizer model. The features for the classification of the facial actions resulted from the application of a combined region of interest and geometric information given a theoretical basis and better performance than other tested steps. As for classifiers, SqueezeNet returned better accuracy rates. With this, the potential of the proposed model comes from the analysis of 77% of the average accuracy of recognition of Libras' facial expressions. This work contributes to the growth of studies that involve the computational vision and recognition aspects of the structure of sign language facial expressions, and its main focus is the importance of facial action annotation in an automated wayDoutoradoEngenharia de ComputaçãoDoutora em Engenharia Elétrica001CAPE
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
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