Semantic Event Graphs for Long-Form Video Question Answering

TL;DR

This paper introduces Semantic Event Graphs, a symbolic approach that enhances long-form video question answering by efficiently capturing temporal interactions, significantly reducing token usage while maintaining high accuracy.

Contribution

The authors propose Semantic Event Graphs as a novel, lightweight symbolic interface that improves long-form video reasoning efficiency for vision-language models.

Findings

Achieves 65.0% accuracy on long videos with only 3.47k tokens per query.

Reduces token usage by 91.4% compared to full-log baselines.

Outperforms short-context baselines by maintaining long-range reasoning.

Abstract

Long-form video question answering remains challenging for modern vision-language models, which struggle to reason over hour-scale footage without exceeding practical token and compute budgets. Existing systems typically downsample frames or feed dense visual embeddings to large-context language models, trading off temporal coverage against cost. We propose Semantic Event Graphs (SEG), a lightweight symbolic interface between video and language that replaces raw frames with compact temporal interaction logs. Our pipeline detects and tracks objects with YOLOv11, converts proximity patterns into START/END human-object events, and organizes them into a Temporal Scene Graph (TSG). At inference time, a query-aware pruning module identifies anchor entities and lexically relevant events, returning only a small subgraph which is verbalized and passed to Gemini 2.5 Flash for answer generation. On five YouTube videos (300-500 interactions each) and 120 automatically generated long-horizon questions, SEG achieves 65.0% accuracy using only 3.47k tokens per query, closely matching a full-log baseline (62.5% at 40.39k tokens) while reducing token usage by 91.4%. A short-context baseline restricted to the last 30 seconds collapses to 2.5% accuracy, underscoring the need for explicit temporal memory. These results show that symbolic temporal graphs can serve as an effective, plug-and-play memory layer for off-the-shelf vision-language models, preserving long-range reasoning ability while making long-form video question answering substantially more token- and cost-efficient. Code, logs, and event-extraction tools will be released for reproducibility.