REACT: Real-time Efficiency and Accuracy Compromise for Tradeoffs in Scene Graph Generation


Maëlic Neau (Umeå University), Paulo Eduardo Santos (PrioriAnalytica), Anne-Gwenn Bosser (Bretagne INP), Akihiro Sugimoto (National Institute of Informatics), Cedric Buche (CNRS IRL 2010 CROSSING, IMT Atlantique)
The 35th British Machine Vision Conference
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Abstract

Scene Graph Generation (SGG) is a task that encodes visual relationships between objects in images as graph structures. SGG shows significant promise as a foundational component for downstream tasks, such as reasoning for embodied agents. To enable real-time applications, SGG must address the trade-off between performance and inference speed. However, current methods tend to focus on one of the following: (1) improving relation prediction accuracy, (2) enhancing object detection accuracy, or (3) reducing latency, without aiming to balance all three objectives simultaneously. To address this limitation, we propose the Real-time Efficiency and Accuracy Compromise for Tradeoffs in Scene Graph Generation (REACT) architecture, which achieves the highest inference speed among existing SGG models, improving object detection accuracy without sacrificing relation prediction performance. Compared to state-of-the-art approaches, REACT is 2.7 times faster and improves object detection accuracy by 58\%. Furthermore, our proposal significantly reduces model size, with an average of 5.5x fewer parameters. The code is available at https://github.com/Maelic/SGG-Benchmark.

Citation

@inproceedings{Neau_2025_BMVC,
author    = {Maëlic Neau and Paulo Eduardo Santos and Anne-Gwenn Bosser and Akihiro Sugimoto and Cedric Buche},
title     = {REACT: Real-time Efficiency and Accuracy Compromise for Tradeoffs in Scene Graph Generation},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_239/paper.pdf}
}


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