Bibliographic record
Abstract
Welcome to Montreal, Quebec, and the Twentieth Conference on Robots and Vision (CRV 2023)!This conference series provides a high-quality forum for the international and Canadian computer and robot vision communities to share their work.After three years of virtual and hybrid conferences, the easing of the covid-19 situation has enabled an in-person format to socialize and attend the sessions together at McGill University.Our conference is sponsored by the Canadian Image Processing and Pattern Recognition Society / Association Canadienne de Traitement d'Images et de Reconnaissance des Formes (CIPPRS/ACTIRF).CIPPRS/ACTIRF is a special interest group of the Canadian Information Processing Society (CIPS) and is the official Canadian member of the governing board of the International Association for Pattern Recognition (IAPR).The goal of CIPPRS/ACTIRF is to promote research and development activities in Computer Vision, Robot Vision, Image Processing, Medical Imaging and Pattern Recognition.The papers here have each been peer-reviewed by a Program Committee comprised of 46 internationally recognized computer and robot vision researchers.We wish to thank the Program Committee for the careful and professional reviews they provided, despite a short reviewing period.This year we received a total of 61 submissions, from which 40 papers were accepted.Of these 40, 17 were selected for oral presentation and 23 for poster presentations.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.528 | 0.340 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".