VizWiz::LocateIt - enabling blind people to locate objects in their environment
By Jeffrey P. Bigham, Chandrika Jayant, Andrew Miller, Brandyn Allen White, Tom Yeh. 2010
145 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Computer Networks and Communications, Computer Science Applications, Computer Vision and Pattern Recognition, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Computer Networks and Communications
In Computer Science Applications
In Computer Vision and Pattern Recognition
- Jiebo Luo
- Kristen Grauman
- Luc Van Gool
- Danna Gurari
- Trevor Darrell
- Dacheng Tao
- Andrew Zisserman
- Ming-Hsuan Yang
In Human-Computer Interaction
Citations in the corpus
- VizWiz Grand Challenge: Answering Visual Questions from Blind People (2018, background): “Such VQA systems have been shown to be valuable for many daily tasks including grocery shopping [8], locating a specific object in a complex scene [9], and choosing clothes to wear [11].”
- “Hands On” Visual Recognition for Visually Impaired Users (2017, background): “VizWiz:LocateIt (Bigham et al. 2010) is a solution for assisting BVI users to locate objects in their environments where recognition is not performed automatically but with the support of a remote Mechanical Turk service (https://www.mturk.com) (Buhrmester et al. ACM Transactions on Accessible…”
- Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors (2021, unknown): “In the object recognition domain, there is some work that is using ML models to help users take good images, for example by indicating whether any object is detected at all or where it is located in the current frame, or using the presence of hands as an indication of where the object is located [4,”