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Video Question Answering On How2Qa
Metrics
Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| Text + Text (no Multimodal Pretext Training) | 93.2 | Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval | |
| FrozenBiLM | 86.7 | Zero-Shot Video Question Answering via Frozen Bidirectional Language Models | |
| Just Ask | 84.4 | Just Ask: Learning to Answer Questions from Millions of Narrated Videos | |
| SeViLA | 83.7 | - | - |
| Hero w/ pre-training | 77.75 | HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training | |
| ATP | 65.1 | Revisiting the "Video" in Video-Language Understanding | |
| FrozenBiLM (0-shot) | 58.4 | Zero-Shot Video Question Answering via Frozen Bidirectional Language Models | |
| Just Ask (0-shot) | 51.1 | Just Ask: Learning to Answer Questions from Millions of Narrated Videos |
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