TouchTone: Interactive Local Image Adjustment Using Point‐and‐Swipe
Bibliographic record
Abstract
Abstract Recent proliferation of camera phones, photo sharing and social network services has significantly changed how we process our photos. Instead of going through the traditional download‐edit‐share cycle using desktop editors, an increasing number of photos are taken with camera phones and published through cellular networks. The immediacy of the sharing process means that on‐device image editing, if needed, should be quick and intuitive. However, due to the limited computational resources and vastly different user interaction model on small screens, most traditional local selection methods can not be directly adapted to mobile devices. To address this issue, we present TouchTone, a new method for edge‐aware image adjustment using simple finger gestures. Our method enables users to select regions within the image and adjust their corresponding photographic attributes simultaneously through a simple point‐and‐swipe interaction. To enable fast interaction, we develop a memory‐ and computation‐efficient algorithm which samples a collection of 1D paths from the image, computes the adjustment solution along these paths, and interpolates the solutions to entire image through bilateral filtering. Our system is intuitive to use, and can support several local editing tasks, such as brightness, contrast, and color balance adjustments, within a minute on a mobile device.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".