ReCog: Supporting Blind People in Recognizing Personal Objects

Ahmetovic, Dragan; Sato, Daisuke; Oh, Uran; Ishihara, Tatsuya; Asakawa, Chieko

doi:10.1145/3313831.3376143

Cited by 43 publications

(24 citation statements)

References 16 publications

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“…Wu et al [ 116 ] developed a system for acoustic activity recognition targeting low user burden by using self-supervised learning techniques. ReCog [ 117 ] is a mobile app for blind users to recognize personal objects by letting them train a neural network with their photos. Mirror Ritual [ 118 ] is an effective mirror that displays a generated poem to engage the user while conceptualizing an emotional state.…”

Section: Classifying Hcml Researchmentioning

confidence: 99%

“…Given that the majority in those sectors are not AI experts developing AI systems for them requires us to investigate the human aspect of such systems. Our analysis shows that application domains have specifically targeted gaming [ 63 , 70 , 165 , 174 , 211 ], interactive technologies [ 69 , 112 , 113 , 118 , 130 , 131 , 133 , 134 , 135 , 137 , 140 , 144 , 152 , 153 , 155 , 212 , 213 ], medicine [ 49 , 57 , 67 , 110 , 114 , 180 , 192 , 193 , 203 , 214 , 215 ], psychiatry [ 98 ], music [ 65 , 83 , 85 , 102 , 153 ], sports [ 126 ], dating [ 60 ], video production [ 84 ], assistive technologies [ 2 , 80 , 86 , 88 , 89 , 96 , 117 , 141 ,…”

Section: Classifying Hcml Researchmentioning

confidence: 99%

“…Considering the works that focused on the user side, some researchers catered to general end-users or consumers [83,101,200,210], while others on specific end-users. Examples for these include people who need assistance [2,80,[86][87][88][89]96,117,147], medical professionals [57,67,110,192,193], international travelers [50], Amazon Mechanical Turk [60,99], drivers [161,162], musicians [102], teachers [124], students [128], children [72,125], UX designers [65,115,206,209], UI designers [103,111,173], data analysts [97], video creators [84], and game designers [70,165,174,211]. Apart from focusing on a specific user group, some have tried to understand multiple user-perspectives from ML engineers to the end-user [48].…”

Section: The 'Human' In Hcmlmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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Section: Classifying Hcml Researchmentioning

confidence: 99%

Section: Classifying Hcml Researchmentioning

confidence: 99%

Section: The 'Human' In Hcmlmentioning

confidence: 99%

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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“…Standing in the way of collecting suitable datasets for teachable object recognisers involving the blind and low vision community are challenges around quality and privacy of the data. In terms of quality of the data, existing research developing teachable object recognisers with blind and low vision collectors [28] shows that one of the main reasons for performance degradation is the absence of the object of interest from the training examples due to challenges in photo-taking by people who are blind or have low vision [4,37]. Researchers have described many of the challenges that blind people encounter while taking photos [1,2,6,24,27,58].…”

Section: Datasets For Teachable Object Recognisersmentioning

confidence: 99%

Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors

Theodorou

Massiceti

Zintgraf

et al. 2021

Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility

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Artificial Intelligence (AI) for accessibility is a rapidly growing area, requiring datasets that are inclusive of the disabled users that assistive technology aims to serve. We offer insights from a multi-disciplinary project that constructed a dataset for teachable object recognition with people who are blind or low vision. Teachable object recognition enables users to teach a model objects that are of interest to them, e.g., their white cane or own sunglasses, by providing example images or videos of objects. In this paper, we make the following contributions: 1) a disability-first procedure to support blind and low vision data collectors to produce good quality data, using video rather than images; 2) a validation and evolution of this procedure through a series of data collection phases and 3) a set of questions to orient researchers involved in creating datasets toward reflecting on the needs of their participant community.CCS Concepts: • Human-centered computing → Accessibility; accessibility systems and tools; accessibility technologies; • Computing methodologies → Machine learning.

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“…Especially, such audio recording and re-playing are essential for visually-impaired people. Many people with visual impairment benefit from auditory guidance for their daily activities such as studying [1], filling out a form [2], taking a pictures [3,4]. There have been many studies to support information access of visually-impaired people.…”

Section: Introductionmentioning

confidence: 99%

HearIt: Auditory-Cue-Based Audio Playback Control to Facilitate Information Browsing in Lecture Audio

et al. 2021

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Students often utilize audio media during online or offline courses. However, lecture audio data are mostly unstructured and extensive, so they are more challenging in information browsing (i.e., chaining, linking, extraction, and evaluation of relevant information). Conventional time-level skip control is limited in auditory information browsing because it is hard to identify the current position and context. This paper presents HearIt, which provides semantic-level skip control with auditory cues for auditory information browsing. With HearIt, users can efficiently change the playback position in the paragraph-level. Furthermore, two auditory cues (positional cue and topical cue) help grasp the current playback and its context without additional visual support. We conducted a pilot study with the prototype of HearIt, and the results show its feasibility and design implications for future research.

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ReCog: Supporting Blind People in Recognizing Personal Objects

Cited by 43 publications

References 16 publications

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors

HearIt: Auditory-Cue-Based Audio Playback Control to Facilitate Information Browsing in Lecture Audio

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