Bibliographic record
Abstract
Advances in deep learning have enabled brand new video analytics systems and applications. Existing systems research on real-time video event detection does not consider matching based on natural language; rather, it focuses on using Domain Specific Languages that define spatio-temporal operators on video streams for efficient matching. Alternatively, research in the multimodal AI community on joint understanding of video and language focuses on applications such as language-based video retrieval, where videos may have been processed offline. In this work, we propose AlertMe, a multimodal-based live video trigger system that matches incoming video streams to a set of user-defined natural language triggers. We dynamically select the optimal sliding window size to extract feature vectors from different modalities in near real time. We also describe our approach to achieve on-device deployment by introducing a profiler to select runtime-efficient feature extractors. Lastly, we show that limiting the number of trigger candidates can significantly increase event detection performance in applications such as task following in AR glasses.
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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 | 0.000 | 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) | 0.001 | 0.001 |
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".