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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".