Video Question Answering On Tvqa
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| VindLU | 79.0 | VindLU: A Recipe for Effective Video-and-Language Pretraining | - |
| FrozenBiLM | 82 | Zero-Shot Video Question Answering via Frozen Bidirectional Language Models | - |
| Hero w/ pre-training | 74.24 | HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training | - |
| iPerceive (Chadha et al., 2020) | 76.96 | iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | - |
| LLaMA-VQA | 82.2 | Large Language Models are Temporal and Causal Reasoners for Video Question Answering | - |
| STAGE (Lei et al., 2019) | 70.50 | TVQA+: Spatio-Temporal Grounding for Video Question Answering | - |
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