Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.017 |
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 it