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Video Question Answering On Situated

Metrics

Average Accuracy

Results

Performance results of various models on this benchmark

Paper Title
VLAP (4 frames)67.1ViLA: Efficient Video-Language Alignment for Video Question Answering
LLaMA-VQA65.4Large Language Models are Temporal and Causal Reasoners for Video Question Answering
SeViLA64.9Self-Chained Image-Language Model for Video Localization and Question Answering
InternVideo58.7InternVideo: General Video Foundation Models via Generative and Discriminative Learning
GF(sup)53.94Glance and Focus: Memory Prompting for Multi-Event Video Question Answering
GF(uns)53.86Glance and Focus: Memory Prompting for Multi-Event Video Question Answering
MIST51.13MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering
Temp[ATP]48.37Revisiting the "Video" in Video-Language Understanding
AnyMAL-70B (0-shot)48.2AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
All-in-one47.5All in One: Exploring Unified Video-Language Pre-training
TraveLER (0-shot)44.9TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering
SeViLA (0-shot)44.6Self-Chained Image-Language Model for Video Localization and Question Answering
Flamingo-9B (4-shot)42.8Flamingo: a Visual Language Model for Few-Shot Learning
Flamingo-80B (4-shot)42.4Flamingo: a Visual Language Model for Few-Shot Learning
Flamingo-9B (0-shot)41.8Flamingo: a Visual Language Model for Few-Shot Learning
Flamingo-80B (0-shot)39.7Flamingo: a Visual Language Model for Few-Shot Learning
SHG-VQA (trained from scratch)39.47Learning Situation Hyper-Graphs for Video Question Answering
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Video Question Answering On Situated | SOTA | HyperAI