In Proceedings of AAAI-02 Workshop on Intelligent Situation-Aware Media and Presentation, Edmonton, Alberta, Canada, July 28, 2002
Bibliographic record
Abstract
Our personal conversation memory agent is a wearable `experience collection' system, which unobtrusively records the wearer's conversation, recognizes the face of the dialog partner and remembers his/her voice. When the system sees the same person's face or hears the same voice it uses a summary of the last conversation with this person to remind the wearer. To correctly identify a person and help remember the earlier conversation, the system must be aware of the current situation, as analyzed from audio and video streams, and classify the situation by combining these modalities. Multimodal classifiers, however, are relatively unstable in the uncontrolled real word environments, and a simple linear interpolation of multiple classification judgments cannot effectively combine multimodal classifiers. We propose a meta-classification strategy using a Support Vector Machine as a new combination strategy. Experimental results show that combining face recognition and speaker identification by meta-classification is dramatically more effective than a linear combination. This meta-classification approach is general enough to be applied to any situation-aware application that needs to combine multiple classifiers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.083 | 0.041 |
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".