qhjqhj00 multimodal-emotion-recognition-eval
Quality-Controlled Multimodal Emotion Recognition in Conversations with Identity-Based Transfer Learning and MAMBA Fusion — Wang & Beigi (2025) (arXiv:2511.14969, 2025)
What this evaluates
Evaluates a model's ability to recognize emotions in conversational video clips using text, audio, and visual modalities. It probes how well identity-preserving representations and state-space fusion capture emotion-relevant acoustic and facial dynamics across different dataset configurations.
Datasets
- MELD — total ?; splits: test (-1)
- IEMOCAP — total ?; splits: test (-1)
Metrics
Accuracy(primary) — range: [0, 1]- Percentage of correctly predicted emotion labels out of the total number of instances.
W-F1— range: [0, 1]- Weighted F1-score across all emotion classes, where each class's F1 is weighted by its support (number of true instances).
Input / output format
Input: Multimodal conversational utterances comprising text transcripts, audio recordings, and video frames, optionally combined with speaker/face identity embeddings.
Output: Discrete emotion class label (7 classes for MELD: neutral, joy, sadness, anger, fear, disgust, surprise; 4 classes for IEMOCAP: neutral, sadness, anger, happy+excited).
Scoring recipe
def compute_metrics(preds, gold):
accuracy = sum(p == g for p, g in zip(preds, gold)) / len(gold)
classes = sorted(set(gold))
weighted_f1 = 0.0
total = len(gold)
for c in classes:
tp = sum(1 for p, g in zip(preds, gold) if p == c and g == c)
fp = sum(1 for p, g in zip(preds, gold) if p == c and g != c)
fn = sum(1 for p, g in zip(preds, gold) if p != c and g == c)
prec = tp / (tp + fp) if (tp + fp) > 0 else 0
rec = tp / (tp + fn) if (tp + fn) > 0 else 0
f1 = 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
weighted_f1 += f1 * sum(1 for g in gold if g == c)
weighted_f1 /= total
return accuracy, weighted_f1
Common pitfalls
- Temporal misalignment between emotional peaks in facial expressions and extracted video frames can degrade visual performance.
- Fear and disgust are frequently confused due to overlapping multimodal patterns in conversational contexts.
- Visual modality alone performs poorly compared to text, requiring careful fusion strategies to avoid performance drops.
Evidence (verbatim from paper)
On MELD, the trimodal system (T+V+A) achieves 64.8% accuracy and 64.3% weighted F1-score.
Citation
@misc{wang2025qualitycontrolled,
title={Quality-Controlled Multimodal Emotion Recognition in Conversations with Identity-Based Transfer Learning and MAMBA Fusion},
author={Wang & Beigi (2025)},
year={2025},
note={arXiv:2511.14969}
}
- arXiv: 2511.14969