Frame2KG: A Benchmark and Evaluation Toolkit for Interpretable Frame-to-Graph Generation

Abstract

Interpretable frame-to-knowledge-graph (Frame2KG) generation enables structured visual scene representation while supporting on-device inference to enhance privacy, improve interpretability, and minimise compute. We introduce Frame2KG-YC2 , a synthetic, reproducible dataset derived from YouCook2 that pairs keyframes with schema-valid JSON knowledge graphs containing typed, spatially grounded entities and semantic predicates, alongside faithful textual paraphrases. Using this corpus, we fine-tune Qwen2.5-VL models (3B and 7B) with parameter-efficient LoRA adapters on attention layers (QKVO), with and without GateProj/Up/Down MLP projections. For evaluation and benchmarking, we propose a deterministic toolkit featuring two-stage node matching, an IoU gate followed by Hungarian assignment on blended spatial-semantic similarity, and comprehensive metrics spanning node/edge precision-recall-F1, matched-pair IoU, and structural validity. On a held-out test set, our models achieve Node F1 µ up to 0.621 and Edge F1 µ up to 0.208, with mean matched IoU of ≈ 0 . 61 and > 98% schema conformity. We show that MLP gating consistently improves predicate accuracy and spatial grounding, while post-training quantisation maintains accuracy and improves deployability on edge hardware. We release the dataset, code, adapters, and evaluation toolkit to establish an open, interpretable baseline for future temporal and multi-view extensions.