Learning Spatio-Temporal Features With Partial Expression Sequences for On-the-Fly Prediction
Bibliographic record
Abstract
Spatio-temporal feature encoding is essential for encoding facial expression dynamics in video sequences. At test time, most spatio-temporal encoding methods assume that a temporally segmented sequence is fed to a learned model, which could require the prediction to wait until the full sequence is available to an auxiliary task that performs the temporal segmentation. This causes a delay in predicting the expression. In an interactive setting, such as affective interactive agents, such delay in the prediction could not be tolerated. Therefore, training a model that can accurately predict the facial expression "on-the-fly" (as they are fed to the system) is essential. In this paper, we propose a new spatio-temporal feature learning method, which would allow prediction with partial sequences. As such, the prediction could be performed on-the-fly. The proposed method utilizes an estimated expression intensity to generate dense labels, which are used to regulate the prediction model training with a novel objective function. As results, the learned spatio-temporal features can robustly predict the expression with partial (incomplete) expression sequences, on-the-fly. Experimental results showed that the proposed method achieved higher recognition rates compared to the state-of-the-art methods on both datasets. More importantly, the results verified that the proposed method improved the prediction frames with partial expression sequence inputs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".