Fine-Grained Video Captioning through Scene Graph Consolidation

• URL: http://arxiv.org/abs/2502.16427v1
• Date: Sun, 23 Feb 2025 03:59:05 GMT
• Title: Fine-Grained Video Captioning through Scene Graph Consolidation
• Authors: Sanghyeok Chu, Seonguk Seo, Bohyung Han,
• Abstract summary: We propose a novel zero-shot video captioning approach that combines frame-level scene graphs from a video to obtain intermediate representations for caption generation.<n>Our method first generates frame-level captions using an image VLM, converts them into scene graphs, and consolidates these graphs to produce comprehensive video-level descriptions.
• Score: 44.30028794237688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in visual language models (VLMs) have significantly improved image captioning, but extending these gains to video understanding remains challenging due to the scarcity of fine-grained video captioning datasets. To bridge this gap, we propose a novel zero-shot video captioning approach that combines frame-level scene graphs from a video to obtain intermediate representations for caption generation. Our method first generates frame-level captions using an image VLM, converts them into scene graphs, and consolidates these graphs to produce comprehensive video-level descriptions. To achieve this, we leverage a lightweight graph-to-text model trained solely on text corpora, eliminating the need for video captioning annotations. Experiments on the MSR-VTT and ActivityNet Captions datasets show that our approach outperforms zero-shot video captioning baselines, demonstrating that aggregating frame-level scene graphs yields rich video understanding without requiring large-scale paired data or high inference cost.

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