Multimodal Multitask Emotion Recognition using Images, Texts and Tags
Bibliographic record
Abstract
Recently, multimodal emotion recognition received an increasing interest due to its potential to improve performance by leveraging complementary sources of information. In this work, we explore the use of images, texts and tags for emotion recognition. However, using several modalities can also come with an additional challenge that is often ignored, namely the problem of "missing modality". Social media users do not always publish content containing an image, text and tags, and consequently one or two modalities are often missing at test time. Similarly, the labeled training data that contain all modalities can be limited. Taking this in consideration, we propose a multimodal model that leverages a multitask framework to enable the use of training data composed of an arbitrary number of modality, while it can also perform predictions with missing modalities. We show that our approach is robust to one or two missing modalities at test time. Also, with this framework it becomes easy to fine-tune some parts of our model with unimodal and bimodal training data, which can further improve overall performance. Finally, our experiments support that this multitask learning also acts as a regularization mechanism that improves generalization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".