Studying vision-based multiple-user interaction with in-home large displays
Bibliographic record
Abstract
Large displays at home such as TVs are becoming larger in size and more interactive in functionality. When multiple co-located users share the screen space of a large display, when, where and how to display their media contents becomes an issue. This paper compares the use of automatic versus manual methods for managing personal screen real-estate on large in-home displays. We assume horizontally laid out "personal interaction spaces" as the user interface for multiple users to manage their screen real-estate. In this case, users need to sign in and out as well as have their personal spaces placed on the display. We constructed a computer-vision based system that tracks the identities and positions of multiple people in front of the display to support the user studies that compare the use of tracker-based mechanisms versus manual ones for managing the display. Our results suggest that the tracking system shows promise for a) simplifying the user registration process in conjunction with a manual sign-in/out process and b) effective tracker-based user-centric placement of people's interaction space. Proper integration of manual methods could improve the sense of control and ownership for users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".