ENTER: Event Based Interpretable Reasoning for VideoQA
📰 ArXiv cs.AI
ENTER is an interpretable Video Question Answering system based on event graphs
Action Steps
- Convert videos into graphical representations using event graphs
- Generate code that parses event-graph for interpretable VideoQA
- Incorporate contextual visual information into the event graph
- Utilize event-event relationships (temporal/causal/hierarchical) to improve VideoQA accuracy
Who Needs to Know This
AI engineers and researchers on a team can benefit from ENTER as it provides an interpretable and structured approach to VideoQA, allowing for more transparent and explainable models
Key Insight
💡 ENTER provides an interpretable and structured approach to VideoQA using event graphs
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📹 ENTER: Event-based interpretable reasoning for VideoQA!
Key Takeaways
ENTER is an interpretable Video Question Answering system based on event graphs
Full Article
Title: ENTER: Event Based Interpretable Reasoning for VideoQA
Abstract:
arXiv:2501.14194v2 Announce Type: replace-cross Abstract: In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where video events form the nodes and event-event relationships (temporal/causal/hierarchical) form the edges. This structured representation offers many benefits: 1) Interpretable VideoQA via generated code that parses event-graph; 2) Incorporation of contextual vis
Abstract:
arXiv:2501.14194v2 Announce Type: replace-cross Abstract: In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where video events form the nodes and event-event relationships (temporal/causal/hierarchical) form the edges. This structured representation offers many benefits: 1) Interpretable VideoQA via generated code that parses event-graph; 2) Incorporation of contextual vis
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