EmoNets: Multimodal deep learning approaches for emotion recognition in\n video
Bibliographic record
Abstract
The task of the emotion recognition in the wild (EmotiW) Challenge is to\nassign one of seven emotions to short video clips extracted from Hollywood\nstyle movies. The videos depict acted-out emotions under realistic conditions\nwith a large degree of variation in attributes such as pose and illumination,\nmaking it worthwhile to explore approaches which consider combinations of\nfeatures from multiple modalities for label assignment. In this paper we\npresent our approach to learning several specialist models using deep learning\ntechniques, each focusing on one modality. Among these are a convolutional\nneural network, focusing on capturing visual information in detected faces, a\ndeep belief net focusing on the representation of the audio stream, a K-Means\nbased "bag-of-mouths" model, which extracts visual features around the mouth\nregion and a relational autoencoder, which addresses spatio-temporal aspects of\nvideos. We explore multiple methods for the combination of cues from these\nmodalities into one common classifier. This achieves a considerably greater\naccuracy than predictions from our strongest single-modality classifier. Our\nmethod was the winning submission in the 2013 EmotiW challenge and achieved a\ntest set accuracy of 47.67% on the 2014 dataset.\n
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".