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Text-Guided Nonverbal Enhancement Based on Modality-Invariant and -Specific Representations for Video Speaking Style Recognition
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Proposes TNvE, a text-guided method that selects critical nonverbal shots via cross-modal attention on ImageBind modality-invariant embeddings and decouples modality-invariant from modality-specific representations for video speaking style recognition.
Problem
Video speaking style recognition struggles because conversation videos look visually similar, and existing multimodal methods treat all modalities equally even though text is more informative for conversation understanding.
Approach
Use textual modality-invariant representations as a guide to select the top nonverbal shots via cross-modal attention, then decouple modality-invariant and modality-specific representations with a soft orthogonality loss and a reconstruction loss, fusing them with a gate.
Key results
- On LVU-VSSR, TNvE reaches 56.7% accuracy and 51.7 macro F1, outperforming the best prior method MMSF (50.2% acc).
- On LVU-VSRR, TNvE reaches 71.4% accuracy and 61.5 macro F1, versus 71.2% for Movie2Scenes and 69.4% for LMP in accuracy.
- Ablation shows gated fusion of invariant and specific representations (56.7 acc) beats addition, concatenation, and no decoupling variants.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply TNvE instead of the strongest reported baseline | VSSR · LVU-VSSR | Acc: 71.2 -> 71.4 | Helped |
| apply TNvE instead of the strongest reported baseline | VSSR · LVU-VSSR | Acc: 50.8 -> 56.7 | Helped |
| add text-guided nonverbal selection (ablation) | unimodal VSSR · LVU-VSSR, visual only | Acc: 36.3 -> 41.8 | Helped |
| add text-guided nonverbal selection (ablation) | unimodal VSSR · LVU-VSSR, audio only | Acc: 43.3 -> 45.3 | Helped |
| add text-guided nonverbal selection (ablation) | VSSR using visual and audio · LVU-VSSR | Acc: 41.3 -> 37.8 | Hurt |
| add text-guided nonverbal selection (ablation) | VSSR using visual and text · LVU-VSSR | Acc: 48.3 -> 51.2 | Helped |
| add text-guided nonverbal selection (ablation) | VSSR using audio and text · LVU-VSSR | Acc: 50.3 -> 50.8 | Helped |
| add text-guided nonverbal selection (ablation) | VSSR using all three modalities · LVU-VSSR | Acc: 47.3 -> 54.2 | Helped |
| combine visual, audio, and text instead of using one modality (ablation) | VSSR · LVU-VSSR, with TNvRS | Acc: 50.3 -> 54.2 | Helped |
| add modality-specific representations to modality-invariant representations (ablation) | TNvE · LVU-VSSR | Acc: 54.2 -> 50.3 | Hurt |
| add reconstruction loss to decoupling loss (ablation) | TNvE · LVU-VSSR | Acc: 54.2 -> 55.7 | Helped |
| replace addition with gated fusion of invariant and specific representations (ablation) | TNvE · LVU-VSSR | Acc: 55.7 -> 56.7 | Helped |
Surprising findings
- Adding text-guided selection to visual-plus-acoustic fusion lowered performance, which the authors attribute to pulling nonverbal representations toward textual content.
- Using only modality-specific representations for text-guided selection performed worse than using modality-invariant ones.
Limitations
- Relies on a small dataset (about 1K videos), which limits real-world relevance, as the authors note.
- Two-stage training with hand-tuned hyperparameters (margins, loss weights, selection numbers per dataset).
- Fusion of visual and acoustic only degrades when text-guided selection is applied.
Experimental setup
Datasets: LVU-VSSR LVU-VSRR
Benchmarks: LVU-VSSR LVU-VSRR
Metrics: Accuracy Macro F1 Macro Precision Macro Recall Weighted F1 Weighted Precision EMD PAD
Baselines: ObjTrans ViS4mer S5 TFN MulT Bert-MAG LF-VILA DMD Movie2Scenes LMP MMSF MA-LLM
Builds on: ImageBind VideoMAE V2 XLS-R BERT TransNet V2
Closest papers by meaning
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- Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition (AAAI 2024 · similarity 0.83)
- ES3: Evolving Self-Supervised Learning of Robust Audio-Visual Speech Representations (CVPR 2024 · similarity 0.83)
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