An Attention-Seq2Seq Model Based on CRNN Encoding for Automatic Labanotation Generation from Motion Capture Data
Bibliographic record
Abstract
Labanotation is an important notation system widely used for recording dances. Numerous methods have been proposed for automatic Labanotation generation from motion capture data. Recently, the sequence-to-sequence (seq2seq) model is proposed. However, the encoder of the model only encodes the temporal information of motion data, lacking the encoding for spatial information. And it is challenging for the decoder to align input and output sequences due to the imbalance of the sequence lengths. In this paper, we propose an attention-seq2seq model based on Convolutional Recurrent Neural Network (CRNN). The proposed model employs an encoder based on CRNN to learn the spatial-temporal information of motion data and applies an attention mechanism to align each target Laban symbol with relevant parts of the input motion data in decoding. Experiments show that the proposed method performs favorably against state-of-the-art algorithms in the automatic Labanotation generation task.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".