Persistent Identifier
|
doi:10.18419/darus-3380 |
Publication Date
|
2023-04-03 |
Title
| Supplemental Material for "Which Experimental Design is Better Suited for VQA Tasks? - Eye Tracking Study on Cognitive Load, Performance, and Gaze Allocations" |
Author
| Vriend, Sita (Universität Stuttgart)
Vidyapu, Sandeep (Universität Stuttgart) - ORCID: 0000-0003-3595-5221
Rama, Amer (Universität Stuttgart)
Chen, Kun-Ting (Universität Stuttgart) - ORCID: 0000-0002-3217-5724
Weiskopf, Daniel (Universität Stuttgart) - ORCID: 0000-0003-1174-1026 |
Point of Contact
|
Use email button above to contact.
Vriend, Sita (Universität Stuttgart)
Chen, Kun-Ting (Universität Stuttgart)
Vidyapu, Sandeep (Universität Stuttgart)
Rama, Amer (Universität Stuttgart) |
Description
| We investigated the effect of stimulus-question ordering and modality in which the question is presented of a user study with visual question answering (VQA) tasks. In an eye-tracking user study (N=13), we tested 5 conditions within-subjects. The conditions were counter-balanced to account for order effects. We collected participants' answers to the VQA tasks, responses to the NASA TLX questionnaire after each completed condition, and gaze data was recorded only during exposure to the image stimulus.
We provide the data and scripts used for statistical analysis, the files used for the exploratory analysis in WebVETA, the image stimuli used per condition and training as well as the VQA tasks related to the images. The images and questions used in the user study is a subset of the GQA dataset (Hudson and Manning, 2019). For more information see: https://cs.stanford.edu/people/dorarad/gqa/index.html The mean fixation duration, hit-any-AOI rate and scan paths were generated using gazealytics (https://www2.visus.uni-stuttgart.de/gazealytics/). The Hit-Any-AOI-rate and mean fixation duration was calculated per person per image stimulus. (2023-03-13) |
Subject
| Computer and Information Science; Social Sciences |
Keyword
| Visual Analytics https://www.wikidata.org/wiki/Q2528440 (Wikidata)
Empirical Research https://www.wikidata.org/wiki/Q2155640 (Wikidata)
Human-Centered Computing https://www.wikidata.org/wiki/Q12812953 (Wikidata)
Eye Tracking https://www.wikidata.org/wiki/Q970687 (Wikidata) |
Related Publication
| Sandeep Vidyapu, Sita A. Vriend, Amer Rama, Kun-Ting Chen, Daniel, Weiskopf. 2023. Which Experimental Design is Better Suited for VQA Tasks? - Eye Tracking Study on Cognitive Load, Performance, and Gaze Allocations doi |
Notes
| Users interested in reproducing the results can follow the methodology as reported in the paper and use the image and question stimuli from the Files section. The analysis code as reported in the R scripts and the data are located the Files section. The zip files inside webveta_files.zip should be uploaded to gazealytics (https://www2.visus.uni-stuttgart.de/gazealytics/) as zip-files. |
Depositor
| Vriend, Sita |
Deposit Date
| 2023-03-13 |
Data Type
| User performance data; Stimuli |
Software
| Rstudio, Version: 2022.12.0+353
Gazealytics, Version: V1.0 |
Related Material
| Drew A. Hudson and Christopher D. Manning. 2019. GQA: A new dataset for real-world visual reasoning and compositional question answering. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 6693–6702. https://doi.org/10.1109/CVPR.2019.00686 |
Data Source
| The images and questions used in the user study is a subset of the GQA dataset (Hudson and Manning, 2019, CC-BY 4.0), downloaded at https://cs.stanford.edu/people/dorarad/gqa/download.html. For more information see: https://cs.stanford.edu/people/dorarad/gqa/index.html and
Drew A. Hudson and Christopher D. Manning. 2019. GQA: A new dataset for real-world visual reasoning and compositional question answering. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 6693-6702. doi: 10.1109/CVPR.2019.00686 |