“So What? What's That to Do With Me?” Expectations of People With Visual Impairments for Image Descriptions in Their Personal Photo Activities

Jung, Ju Yeon; Steinberger, Tom; Kim, Junbeom; Ackerman, Mark S.

doi:10.1145/3532106.3533522

Cited by 8 publications

(3 citation statements)

References 70 publications

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“…For instance, a noteworthy challenge for accessibility arises from screen readers struggling to identify unlabeled buttons, an aspect that is ignored by developers when designing interface buttons [69]. Additionally, researchers have observed that visually impaired individuals' engagement with images is context-dependent, and the provided textual descriptions do not consistently align with their requirements [1,43,82]. These accessibility challenges indicate that accessibility is not always a technical problem but also a social issue.…”

Section: Blind People and Social Media Accessibilitymentioning

confidence: 99%

"I Got Flagged for Supposed Bullying, Even Though It Was in Response to Someone Harassing Me About My Disability.": A Study of Blind TikTokers’ Content Moderation Experiences

Lyu,

Cai,

Callis

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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The Human-Computer Interaction (HCI) community has consistently focused on the experiences of users moderated by social media platforms. Recently, scholars have noticed that moderation practices could perpetuate biases, resulting in the marginalization of user groups undergoing moderation. However, most studies have primarily addressed marginalization related to issues such as racism or sexism, with little attention given to the experiences of people with disabilities. In this paper, we present a study on the moderation experiences of blind users on TikTok, also known as "BlindToker, " to address this gap. We conducted semi-structured interviews with 20 BlindTokers and used thematic analysis to analyze the data. Two main themes emerged: BlindTokers' situated content moderation experiences and their reactions to content moderation. We reported on the lack of accessibility on TikTok's platform, contributing to the moderation and marginalization of BlindTokers. Additionally, we discovered instances of harassment from trolls that prompted BlindTokers to respond with harsh language, triggering further moderation. We discussed these findings in the context of the literature on moderation, marginalization, and transformative justice, seeking solutions to address such issues.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI.

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Section: Blind People and Social Media Accessibilitymentioning

confidence: 99%

"I Got Flagged for Supposed Bullying, Even Though It Was in Response to Someone Harassing Me About My Disability.": A Study of Blind TikTokers’ Content Moderation Experiences

Lyu,

Cai,

Callis

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…Prior work has also emphasized that BLV people's preferences for image descriptions vary based on an image's context [17]. Specifically, preferences differ based on the source or content of the image [4,21,49,60,63,78,79,111,114]. For example, Stangl et al [111,114] found that BLV people wanted different details for images associated with different sources and user goals.…”

Section: Image Accessibilitymentioning

confidence: 99%

“It’s Kind of Context Dependent”: Understanding Blind and Low Vision People’s Video Accessibility Preferences Across Viewing Scenarios

Jiang,

Jung,

Phutane

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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While audio description (AD) is the standard approach for making videos accessible to blind and low vision (BLV) people, existing AD guidelines do not consider BLV users' varied preferences across viewing scenarios. These scenarios range from how-to videos on YouTube, where users seek to learn new skills, to historical dramas on Netflix, where a user's goal is entertainment. Additionally, the increase in video watching on mobile devices provides an opportunity to integrate nonverbal output modalities (e.g., audio cues, tactile elements, and visual enhancements). Through a formative survey and 15 semi-structured interviews, we identified BLV people's video accessibility preferences across diverse scenarios. For example, participants valued action and equipment details for howto videos, tactile graphics for learning scenarios, and 3D models for fantastical content. We define a six-dimensional video accessibility design space to guide future innovation and discuss how to move from "one-size-fits-all" paradigms to scenario-specific approaches. CCS CONCEPTS• Human-centered computing → Accessibility.

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“…This task represents a standard example of multi-modal learning, bridging the domains of Computer Vision (CV) and Natural Language Processing (NLP). Image captioning models have utility across diverse domains, with application including assistance to individuals with visual impairments [ 1 , 2 ], automatic medical image captioning [ 3 ] and diagnosis [ 4 ], and enhancing human–computer interactions [ 5 ]. Motivated by the achievements of deep learning techniques in machine translation [ 6 ], the majority of image captioning models adopt the encoder–decoder framework coupled with a visual attention mechanism [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Insights into Object Semantics: Leveraging Transformer Networks for Advanced Image Captioning

Abdal Hafeth,

Kollias

2024

Sensors

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Image captioning is a technique used to generate descriptive captions for images. Typically, it involves employing a Convolutional Neural Network (CNN) as the encoder to extract visual features, and a decoder model, often based on Recurrent Neural Networks (RNNs), to generate the captions. Recently, the encoder–decoder architecture has witnessed the widespread adoption of the self-attention mechanism. However, this approach faces certain challenges that require further research. One such challenge is that the extracted visual features do not fully exploit the available image information, primarily due to the absence of semantic concepts. This limitation restricts the ability to fully comprehend the content depicted in the image. To address this issue, we present a new image-Transformer-based model boosted with image object semantic representation. Our model incorporates semantic representation in encoder attention, enhancing visual features by integrating instance-level concepts. Additionally, we employ Transformer as the decoder in the language generation module. By doing so, we achieve improved performance in generating accurate and diverse captions. We evaluated the performance of our model on the MS-COCO and novel MACE datasets. The results illustrate that our model aligns with state-of-the-art approaches in terms of caption generation.

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“So What? What's That to Do With Me?” Expectations of People With Visual Impairments for Image Descriptions in Their Personal Photo Activities

Cited by 8 publications

References 70 publications

"I Got Flagged for Supposed Bullying, Even Though It Was in Response to Someone Harassing Me About My Disability.": A Study of Blind TikTokers’ Content Moderation Experiences

"I Got Flagged for Supposed Bullying, Even Though It Was in Response to Someone Harassing Me About My Disability.": A Study of Blind TikTokers’ Content Moderation Experiences

“It’s Kind of Context Dependent”: Understanding Blind and Low Vision People’s Video Accessibility Preferences Across Viewing Scenarios

Insights into Object Semantics: Leveraging Transformer Networks for Advanced Image Captioning

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