A Hybrid Local and Server-Based Large Scale Image Retrieval System and its Applications
Bibliographic record
Abstract
著者らは実空間とWeb空間との連動を目的とし，TV CMや屋外広告等をスマートフォンで画像認識するアプリを開発した．本技術により，スマートフォンをかざす対象，場所，連動ゲームのスコアに応じて異なる関連情報を提示することでユーザに新たな体験を提供するとともに，放送と通信とにおけるマルチスクリーンのシームレスな連携を実現した．事前学習した特徴量を1/10以下に圧縮し，圧縮状態のまま認識することでスマートフォンでもリアルタイム動作を実現し，認識後に起動するゲームではTV CMと同じくスマートフォンに閉じ込められている男女を再会させる演出で，認識対象等に応じたマルチストーリー分岐を用意した．本稿では，全国放映されたTV CVでも多くのユーザが同時利用できる大規模画像検索技術と，高臨場感を醸し出す背景領域抽出技術，および当該アプリを一般公開した効果や反響について報告する．
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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 | 0.001 | 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 | 0.002 |
| 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".