Nemesis: Neural Mean Teacher Learning-Based Emotion-Centric Speaker
Bibliographic record
Abstract
Image captioning is the multi-modal task of automatically describing a digital image based on its contents and their semantic relationship. This research area has gained increasing popularity over the past few years; however, most of the previous studies have been focused on purely objective content-based descriptions of the image scenes. In this study, efforts have been made to generate more engaging captions by leveraging human-like emotional responses. To achieve this task, a mean teacher learning-based method has been applied to the recently introduced ArtEmis dataset. ArtEmis is the first large-scale dataset for emotion-centric image captioning, containing 455K emotional descriptions of 80K artworks from WikiArt. This method includes a self-distillation relationship between memory-augmented language models with meshed connectivity. These language models are trained in a cross-entropy phase and then fine-tuned in a self-critical sequence training phase. According to various popular natural language processing metrics, such as BLEU, METEOR, ROUGE-L, and CIDEr, our proposed model has obtained a new state of the art on ArtEmis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".