Variational Learning of Beta-Liouville Hidden Markov Models for Infrared Action Recognition
Bibliographic record
Abstract
Infrared (IR) images are characterized by a lower sensitivity to lighting conditions than the visible spectrum. This opens the door to relatively untapped research potential of automatic recognition systems that are robust to shadows and variability in illumination levels or appearance. IR action recognition (AR) is one such application. It remains a fairly unexplored domain in IR. As such, in this paper, we propose the use of hidden Markov models (HMM) for IR AR. We also derive the mathematical model for the variational learning of Beta-Liouville (BL) HMMs. Next, we present the results of the proposed model on the Infrared Action Recognition (InfAR) dataset. To the best of our knowledge, this is the first application of HMMs to AR in the IR domain, and the first application of the BL HMMs to AR. Experimental results demonstrate promising results using different features extracted from the InfAR dataset.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".