Answering visual questions with conversational crowd assistants

By Walter S. Lasecki, Phyo Thiha, Yu Zhong, Erin L. Brady, Jeffrey P. Bigham. 2013

87 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Science Applications, Electrical and Electronic Engineering, Multimodal Learning, Question Answering & Reading Comprehension, Dialogue & Conversational AI

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

1. Abigale Stangl
2. Jeffrey P. Bigham
3. Kristen Grauman
4. Jiebo Luo
5. Hernisa Kacorri

In Computer Science Applications

1. Abigale Stangl
2. Danna Gurari
3. Jeffrey P. Bigham
4. Daniel S. Weld
5. Kristen Grauman
6. Jonathan Bragg
7. C. H. Lin

In Electrical and Electronic Engineering

1. Kristen Grauman

In Multimodal Learning

1. Jeffrey P. Bigham
2. Daniel S. Weld
3. Kristen Grauman
4. Danna Gurari
5. Chi Lin
6. Qing Li
7. Anhong Guo
8. Jiebo Luo

In Question Answering & Reading Comprehension

1. Jeffrey P. Bigham
2. Daniel S. Weld
3. Danna Gurari
4. Qing Li
5. Jiebo Luo
6. Ana Marasovic

Citations in the corpus

• VizWiz Grand Challenge: Answering Visual Questions from Blind People (2018, background): “Success in developing automated methods would mitigate concerns about the many undesired consequences from today’s status quo for blind people of relying on humans to answer visual questions [8, 11, 24]; e.”
• Artificial Intelligence and Collective Intelligence (2014, background): “For example, when crowdsourced workers are used to interpret a smartphone photo of a street sign for visuallychallenged users (Lasecki et al. 2013), a quick response is essential.”