Towards Neuro-Symbolic Video Understanding

• URL: http://arxiv.org/abs/2403.11021v3
• Date: Tue, 03 Dec 2024 18:58:22 GMT
• Title: Towards Neuro-Symbolic Video Understanding
• Abstract summary: Long-term temporal reasoning is a key desideratum for frame retrieval systems.<n>A key reason for this failure is that they intertwine per-frame perception and temporal reasoning into a single deep network.<n>We propose a system that leverages vision-language models for semantic understanding of individual frames but effectively reasons about the long-term evolution of events.
• Score: 3.767558059128794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The unprecedented surge in video data production in recent years necessitates efficient tools to extract meaningful frames from videos for downstream tasks. Long-term temporal reasoning is a key desideratum for frame retrieval systems. While state-of-the-art foundation models, like VideoLLaMA and ViCLIP, are proficient in short-term semantic understanding, they surprisingly fail at long-term reasoning across frames. A key reason for this failure is that they intertwine per-frame perception and temporal reasoning into a single deep network. Hence, decoupling but co-designing semantic understanding and temporal reasoning is essential for efficient scene identification. We propose a system that leverages vision-language models for semantic understanding of individual frames but effectively reasons about the long-term evolution of events using state machines and temporal logic (TL) formulae that inherently capture memory. Our TL-based reasoning improves the F1 score of complex event identification by 9-15% compared to benchmarks that use GPT4 for reasoning on state-of-the-art self-driving datasets such as Waymo and NuScenes.

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