ONSUM: A system for generating online navigation summaries
Bibliographic record
Abstract
We propose an algorithm for generating navigation summaries. Navigation summaries are a specialization of video summaries, where the focus is on video collected by a mobile robot, on a specified trajectory. We are interested in finding a few images that epitomize the visual experience of a robot as it traverses a terrain. This paper presents a novel approach to generating summaries in form of a set of images, where the decision to include the image in the summary set is made online. Our focus is on the case where the number of observations is infinite or unknown, but the size of the desired summary is known. Our strategy is to consider the images in the summary set as the prior hypothesis of the appearance of the world, and then use Set Theoretic Surprise to compute the novelty of an observed image. If the novelty is above a threshold, then we accept the image. We discuss different criterion for setting this threshold. Online nature of our approach allows for several interesting applications such as coral reef inspection, surveying, and surveillance.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".