Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Bibliographic record
Abstract
It is our great pleasure to invite you all to the Seventh Indian Conference on Vision, Graphics and Image Processing to be held in Chennai, India. This conference is a continuation of the bi-annual series of ICVGIPs that originated in New Delhi in 1998. Subsequent conferences have been held in many well-known cities in India. This conference has emerged as one of the premier conferences in India. By adopting the review process and structure (double-blind, use of area chairs, single tracks, etc) that is used in other top tier vision conferences, ICVGIP is also acquiring a stature as an international conference of solid reputation. Right from the beginning, many outstanding researchers in India in the fields covered by ICVGIP have put their hearts and minds into having a high-quality conference and the attendees of this conference will not be disappointed. We are happy to note that the Seventh ICVGIP will host attendees from India, USA, UK, Germany, France, Denmark, Finland, China, and South Africa. The conference program has 24 oral presentations, 46 poster presentations, five plenary talks, and a tutorial. The plenary talks on a diverse set of topics will be given by well-known researchers from USA, France, Canada, Switzerland, and India. The tutorial on Markov random fields for computer vision will focus on a field that is reemerging in computer vision and graphics. Thus the attendees of this conference will be exposed to many cutting-edge research topics in the fields covered by the conference.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.081 | 0.048 |
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".