Bibliographic record
Abstract
The sixth International Conference on Machine Vision and Information Technology (CMVIT 2022), was held on Feb. 25, 2022. Organized by Asia Pacific Institute of Science and Engineering and media supported by MDPI-Information, CMVIT 2022 strives to offer networking opportunities to meet and interact with the world-leading scientists, engineers and researchers as well as industrial partners in machine vision and information technologies. CMVIT has been held for five years since 2017 after four successful onsite conferences in Singapore, Hong Kong, Guangzhou, and Sanya till 2020 and one virtual conference in 2021. As the sixth event, CMVIT 2022 continues to provide a unique and excellent forum to exchange research methodologies, explore practical applications, and foster innovative ideas, in machine vision and information technology. The conference was originally scheduled in Haikou, China from in February 2022. Due to the COVID-19 situation in China, most authors cannot attend offline. It had to be turned into a fully virtual conference via Tencent meeting. Moreover, as authors wished to attend the conference as scheduled, for the sake of academic exchange and publication request, the conference was held on Feb. 25, 2022 as virtual conference, instead of postponement. The proceedings of this year’s edition consist of 3 main categories: Information Technology, Machine Learning, and Computer Vision. The research tracks attracted 75 submissions, among which 41 were accepted, including countries like Canada, Malaysia, UK, Portugal, Saudi Arabia, China, etc. All the submissions were rigorously reviewed by the Program Committee (PC). In the full-day conference, 16 oral presentations were made in two sessions and 25 posters were demonstrated in the single poster session. For each presentation, warm discussion and Q&A were followed. We have also selected 1 best paper award, 1 best poster award, and 2 best oral presentation awards. One plenary speaker Prof. Yingxu Wang from Canada, and 2 keynote speakers Prof. Dongbing Zhao and Prof. Zenglin Xu from China were invited to share their latest and insightful research ideas in the conference. We sincerely thank all the authors for submitting their papers to our conference. We also would like to show our appreciation to the invited speakers, PC members, supporting staff, as well as all the Organizing Committee members for their great efforts. Without their contribution, CMVIT 2022 would not be possible. List of Committees are available in the pdf.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".