Valence classification on GAViD 1.0 (test)
0.6621AccuracyCAGNet
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| CAGNetModality=V + A + C, Training Split=Train+Val2026.04 | 0.6621 | 0.647 | |
| CAGNetModality=V + A + C, Training Split=Train2026.04 | 0.632 | 0.614 | |
| Baseline 1Modality=V + A + C, Training Split=Train+Val2026.04 | 0.6155 | 0.58 | |
| CAGNetModality=V + A, Training Split=Train+Val2026.04 | 0.6089 | 0.597 | |
| Baseline 2Modality=V + A + C, Training Split=Train+Val2026.04 | 0.6012 | 0.588 | |
| CAGNetModality=V + C, Training Split=Train+Val2026.04 | 0.6012 | 0.606 | |
| CAGNetModality=V + A, Training Split=Train2026.04 | 0.5953 | 0.565 | |
| CAGNetModality=V + C, Training Split=Train2026.04 | 0.5913 | 0.577 | |
| CAGNetModality=A + C, Training Split=Train+Val2026.04 | 0.5911 | 0.591 | |
| Baseline 1Modality=V + A + C, Training Split=Train2026.04 | 0.5856 | 0.554 | |
| CAGNetModality=A + C, Training Split=Train2026.04 | 0.5829 | 0.558 | |
| Baseline 2Modality=V + A + C, Training Split=Train2026.04 | 0.5747 | 0.559 | |
| LLaVA-NeXTModality=V+A, Training Split=Train2026.04 | 0.5482 | 0.388 | |
| Video-GPTModality=V+A, Training Split=Train2026.04 | 0.5192 | 0.525 |