Bibliographic record
Abstract
Message from the ChairsWelcome to the 15th IEEE International Conference on Computational Photography (ICCP 2023), taking place at Monona Terrace in Madison, WI!This year's three-day conference features 24 accepted papers, 3 keynote talks, 8 invited talks, and 66 posters and/or demos.Paper Submission and Reviewing.A total of 52 complete paper submissions were received through Microsoft's Conference Management Toolkit.To ensure a rigorous and comprehensive selection process, we invited 75 experts to join the program committee.Our aim was to create a diverse committee comprising both highly-experienced senior researchers and highly-accomplished junior researchers.Their expertise spans across various domains, including computational photography, computational imaging, computer vision, computer graphics, signal processing, and optics.Conflicts were managed through automated tools like the Toronto Paper Matching System and the DBLP database, along with manual oversight by the program chairs themselves.The program chairs abstained from submitting papers to ensure impartiality.In cases where a program chair had a conflict of interest with a specific paper, they were completely removed from all aspects of the reviewing process concerning that particular paper.Following the submission deadline, the program committee members were given the opportunity to express their interest in reviewing specific papers, excluding those where there was a conflict of interest.The program chairs then carefully assigned papers to reviewers while ensuring each paper had a minimum of 3 reviewers.For each paper, the program chairs designated one of the assigned reviewers as the meta-reviewer.Almost all program committee members had no more than one meta-reviewer assignment, with only two exceptions.The reviewers were allotted a period of 4 weeks to complete their evaluations.Following the review phase, authors were given 1 week to provide rebuttals to the reviews.A week-long discussion period followed, during which the meta-reviewers thoroughly analyzed the assigned papers and formulated their recommendations.The recommendations consisted of two crucial components.Firstly, the paper outcome was indicated as either rejected, conditionally accepted to the ICCP 2023 proceedings, or conditionally accepted to the Transactions on Pattern Analysis and Machine Intelligence special issue on computational photography (TPAMI special issue).Secondly, for conditionally-accepted papers, specific revisions and required changes were outlined that the authors needed to implement before the paper could be officially accepted.This process ensured a rigorous and fair evaluation of all submissions.Upon reviewing the recommendations, the program chairs made the decision to conditionally accept 24 papers, resulting in a 46% acceptance rate.Out of these, 6 papers were conditionally accepted for inclusion in the TPAMI special issue, accounting for 12% of the accepted papers.The remaining conditionally accepted papers were approved for inclusion in the ICCP 2023 proceedings.During the final phase of the paper reviewing process, authors were granted a one-month window to revise their papers based on the feedback received during the initial review.At the end of this period, authors submitted their revised manuscripts, along with cover letters explaining the changes made, and annotated manuscripts highlighting the implemented revisions.All the submitted revised manuscripts underwent a second round of reviewing, conducted by both the program chairs and meta-reviewers.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.264 | 0.189 |
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".