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Exploring the Design of Vibrotactile Cues for Visio-Haptic Sensory Substitution

Description

This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions.

This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions. Pancake shaftless vibration motors are mounted on the back of a chair to provide vibrotactile stimulation in the context of a dyadic (one-on-one) interaction across a table. This work explores the design of spatiotemporal vibration patterns that can be used to convey the basic building blocks of facial movements according to the Facial Action Unit Coding System. A behavioral study was conducted to explore the factors that influence the naturalness of conveying affect using vibrotactile cues.

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2014-05

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Domain Adaptive Computational Models for Computer Vision

Description

The widespread adoption of computer vision models is often constrained by the issue of domain mismatch. Models that are trained with data belonging to one distribution, perform poorly when tested with data from a different distribution. Variations in vision based

The widespread adoption of computer vision models is often constrained by the issue of domain mismatch. Models that are trained with data belonging to one distribution, perform poorly when tested with data from a different distribution. Variations in vision based data can be attributed to the following reasons, viz., differences in image quality (resolution, brightness, occlusion and color), changes in camera perspective, dissimilar backgrounds and an inherent diversity of the samples themselves. Machine learning techniques like transfer learning are employed to adapt computational models across distributions. Domain adaptation is a special case of transfer learning, where knowledge from a source domain is transferred to a target domain in the form of learned models and efficient feature representations.

The dissertation outlines novel domain adaptation approaches across different feature spaces; (i) a linear Support Vector Machine model for domain alignment; (ii) a nonlinear kernel based approach that embeds domain-aligned data for enhanced classification; (iii) a hierarchical model implemented using deep learning, that estimates domain-aligned hash values for the source and target data, and (iv) a proposal for a feature selection technique to reduce cross-domain disparity. These adaptation procedures are tested and validated across a range of computer vision applications like object classification, facial expression recognition, digit recognition, and activity recognition. The dissertation also provides a unique perspective of domain adaptation literature from the point-of-view of linear, nonlinear and hierarchical feature spaces. The dissertation concludes with a discussion on the future directions for research that highlight the role of domain adaptation in an era of rapid advancements in artificial intelligence.

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2017

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Language Image Transformer

Description

Humans perceive the environment using multiple modalities like vision, speech (language), touch, taste, and smell. The knowledge obtained from one modality usually complements the other. Learning through several modalities helps in constructing an accurate model of the environment. Most of

Humans perceive the environment using multiple modalities like vision, speech (language), touch, taste, and smell. The knowledge obtained from one modality usually complements the other. Learning through several modalities helps in constructing an accurate model of the environment. Most of the current vision and language models are modality-specific and, in many cases, extensively use deep-learning based attention mechanisms for learning powerful representations. This work discusses the role of attention in associating vision and language for generating shared representation. Language Image Transformer (LIT) is proposed for learning multi-modal representations of the environment. It uses a training objective based on Contrastive Predictive Coding (CPC) to maximize the Mutual Information (MI) between the visual and linguistic representations. It learns the relationship between the modalities using the proposed cross-modal attention layers. It is trained and evaluated using captioning datasets, MS COCO, and Conceptual Captions. The results and the analysis offers a perspective on the use of Mutual Information Maximisation (MIM) for generating generalizable representations across multiple modalities.

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2020

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Generalized Domain Adaptation for Visual Domains

Description

Humans have a great ability to recognize objects in different environments irrespective of their variations. However, the same does not apply to machine learning models which are unable to generalize to images of objects from different domains. The generalization of

Humans have a great ability to recognize objects in different environments irrespective of their variations. However, the same does not apply to machine learning models which are unable to generalize to images of objects from different domains. The generalization of these models to new data is constrained by the domain gap. Many factors such as image background, image resolution, color, camera perspective and variations in the objects are responsible for the domain gap between the training data (source domain) and testing data (target domain). Domain adaptation algorithms aim to overcome the domain gap between the source and target domains and learn robust models that can perform well across both the domains.

This thesis provides solutions for the standard problem of unsupervised domain adaptation (UDA) and the more generic problem of generalized domain adaptation (GDA). The contributions of this thesis are as follows. (1) Certain and Consistent Domain Adaptation model for closed-set unsupervised domain adaptation by aligning the features of the source and target domain using deep neural networks. (2) A multi-adversarial deep learning model for generalized domain adaptation. (3) A gating model that detects out-of-distribution samples for generalized domain adaptation.

The models were tested across multiple computer vision datasets for domain adaptation.

The dissertation concludes with a discussion on the proposed approaches and future directions for research in closed set and generalized domain adaptation.

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Date Created
2020

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Modern Sensory Substitution for Vision in Dynamic Environments

Description

Societal infrastructure is built with vision at the forefront of daily life. For those with

severe visual impairments, this creates countless barriers to the participation and

enjoyment of life’s opportunities. Technological progress has been both a blessing and

a curse in this regard.

Societal infrastructure is built with vision at the forefront of daily life. For those with

severe visual impairments, this creates countless barriers to the participation and

enjoyment of life’s opportunities. Technological progress has been both a blessing and

a curse in this regard. Digital text together with screen readers and refreshable Braille

displays have made whole libraries readily accessible and rideshare tech has made

independent mobility more attainable. Simultaneously, screen-based interactions and

experiences have only grown in pervasiveness and importance, precluding many of

those with visual impairments.

Sensory Substituion, the process of substituting an unavailable modality with

another one, has shown promise as an alternative to accomodation, but in recent

years meaningful strides in Sensory Substitution for vision have declined in frequency.

Given recent advances in Computer Vision, this stagnation is especially disconcerting.

Designing Sensory Substitution Devices (SSDs) for vision for use in interactive settings

that leverage modern Computer Vision techniques presents a variety of challenges

including perceptual bandwidth, human-computer-interaction, and person-centered

machine learning considerations. To surmount these barriers an approach called Per-

sonal Foveated Haptic Gaze (PFHG), is introduced. PFHG consists of two primary

components: a human visual system inspired interaction paradigm that is intuitive

and flexible enough to generalize to a variety of applications called Foveated Haptic

Gaze (FHG), and a person-centered learning component to address the expressivity

limitations of most SSDs. This component is called One-Shot Object Detection by

Data Augmentation (1SODDA), a one-shot object detection approach that allows a

user to specify the objects they are interested in locating visually and with minimal

effort realizing an object detection model that does so effectively.

The Personal Foveated Haptic Gaze framework was realized in a virtual and real-

world application: playing a 3D, interactive, first person video game (DOOM) and

finding user-specified real-world objects. User study results found Foveated Haptic

Gaze to be an effective and intuitive interface for interacting with dynamic visual

world using solely haptics. Additionally, 1SODDA achieves competitive performance

among few-shot object detection methods and high-framerate many-shot object de-

tectors. The combination of which paves the way for modern Sensory Substitution

Devices for vision.

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Created

Date Created
2020

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Incremental Learning With Sample Generation From Pretrained Networks

Description

In the last decade deep learning based models have revolutionized machine learning and computer vision applications. However, these models are data-hungry and training them is a time-consuming process. In addition, when deep neural networks are updated to augment their prediction

In the last decade deep learning based models have revolutionized machine learning and computer vision applications. However, these models are data-hungry and training them is a time-consuming process. In addition, when deep neural networks are updated to augment their prediction space with new data, they run into the problem of catastrophic forgetting, where the model forgets previously learned knowledge as it overfits to the newly available data. Incremental learning algorithms enable deep neural networks to prevent catastrophic forgetting by retaining knowledge of previously observed data while also learning from newly available data.

This thesis presents three models for incremental learning; (i) Design of an algorithm for generative incremental learning using a pre-trained deep neural network classifier; (ii) Development of a hashing based clustering algorithm for efficient incremental learning; (iii) Design of a student-teacher coupled neural network to distill knowledge for incremental learning. The proposed algorithms were evaluated using popular vision datasets for classification tasks. The thesis concludes with a discussion about the feasibility of using these techniques to transfer information between networks and also for incremental learning applications.

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Date Created
2020

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Deep domain fusion for adaptive image classification

Description

Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large

Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to overcome these challenges by facilitating transfer learning between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated unprecedented potential for utilizing unlabeled data to train classification models in defiance of discouragingly meager sets of annotated data.

In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.

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Date Created
2019