FLIRL-Net: Fuzzy Logic-Driven Important Relationship Learning for Scene Graph Generation

Jin Wang, Zilong Yang, Jialing Xu, Sixu Wu
2025 · doi:10.21203/rs.3.rs-7500066/v1
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FLIRL-Net employs fuzzy logic to learn crucial relationships for improved scene graph generation accuracy.

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This preprint studied scene graph generation, focusing on predicting which visual relationships in an image are “important” rather than enumerating all possible relationships. The authors proposed FLIRL-Net, which uses fuzzy logic to compute per-relationship importance scores, applies these scores to weight relationship label losses via an RLIW module, and introduces an Importance-Based Entity Pair Feature PoolFormer module to improve recognition of important entity pairs; they evaluated performance on the VG150 dataset using Important Relationship Recall (IR@K) and Precision (IP@K). Reported experimental results indicate the model better identifies important relationships and reduces unimportant relationship outputs, with the paper caveating that it is a preprint not yet peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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FLIRL-Net: Fuzzy Logic-Driven Important Relationship Learning for Scene Graph Generation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article FLIRL-Net: Fuzzy Logic-Driven Important Relationship Learning for Scene Graph Generation Jin Wang, Zilong Yang, Jialing Xu, Sixu Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7500066/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2026 Read the published version in Multimedia Systems → Version 1 posted 10 You are reading this latest preprint version Abstract Scene Graph Generation (SGG) is a fundamental task in computer vision. It entails classifying entities in images and predicting visual relationships among them. Existing scene graph generation research targets the identification of all possible relationships within an image. However, this approach may suffer from inefficiencies and generate redundant information, hindering the further processing of relationship data in downstream tasks. In contrast, identifying important relationships is more aligned with the practical requiements of downstream tasks. To address this issue, we introduce the Fuzzy Logic-Driven Important Relationship Learning Network (FLIRL-Net). Initially, we employ fuzzy logic to compute an importance score for each relationship. This computation accounts for factors affecting relationship significance and user requirements. Subsequently, we design the Relationship Label Loss Importance Weighting (RLIW) module. This module utilizes these importance scores as weights to adjust relationship sample losses. Such adjustments direct the model's focus towards important relationships. Thirdly, we develop the Importance-Based Entity Pair Feature PoolFormer (IEPFP) module to enhance the model's recognition of important entity pairs. Additionally, We propose metrics Important Relationship Recall (IR@K) and Important Relationship Precision (IP@K). These metrics assess the model's effectiveness in identifying important relationships. Experimental results demonstrate that our model excels at identifying important relationships in the VG150 dataset and effectively minimizes unimportant relationship outputs. Scene Graph Generation Important Relationships Fuzzy Logic Importance Weighting Entity Pair Feature PoolFormer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2026 Read the published version in Multimedia Systems → Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviews received at journal 31 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 17 Oct, 2025 Submission checks completed at journal 10 Sep, 2025 First submitted to journal 31 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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