Proceedings of the First International Workshop on Internet-Scale Multimedia Management
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2014 First ACM International Workshop on Internet-Scale Multimedia Management -- WISMM'14, co-located with the 2014 ACM International Conference on Multimedia. This workshop was inspired by the observation that every day people create and consume massive amounts of multimedia information and data by engaging with various mobile Internet services. With a wide variety of multimedia information and data around us being aggregated over time, the Internet is getting increasingly information centric. We are experiencing an age of increasing demands on how to host people's online engagements and how to augment people's lives in the physical world with more personalized smart services. This workshop is designed to bring researchers and practitioners from academia and industry together to discuss and share perspectives on a key characteristic of multimedia information and data which is that their scale is massive and requires a technological infrastructure that can naccommodate rapid processing, large-scale storage, and flexible analysis of multi-structured data. The mission of the workshop is to share interesting methods and approaches relating to various aspects in the collection, management and processing of large-scale structured and unstructured multimedia information and data. The call for papers attracted 18 full submissions and 6 short submissions from countries around the world including Italy, Singapore, Austria, Japan, China, Germany, France, Canada, the United States, and Mexico. From among all the received submissions, we selected 6 full papers which will be organized into two sessions, and 6 short papers which will be presented in a separate poster session. We also enthusiastically encourage the workshop participants to attend the two keynote talk presentations. These valuable and insightful talks will aid in our understanding and provide food for thought for future developments related to the workshop topics: Storytelling with Big Multimedia Data, Ramesh Jain (University of California, Irvine) Pushing Image Recognition in the Real World -- Towards Recognizing Millions of Entities, Xian-Sheng Hua (Microsoft Research, Redmond)
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 | 0.001 | 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".