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    <abstract>Scene Graph Generation (SGG) structures visual scenes as graphs of objects and their relations. While Multimodal Large Language Models (MLLMs) have advanced end-to-end SGG, current methods are hindered by both a lack of task-specific structured reasoning and the challenges of sparse, long-tailed relation distributions, resulting in incomplete scene graphs characterized by low recall and biased predictions. To address these issues, we introduce SGG-R^ 3, a structured reasoning framework that integrates task-specific Chain-of-Thought (CoT)-guided supervised fine-tuning (SFT) and reinforcement learning (RL) with group sequence policy optimization (GSPO), designed to engage in three sequential stages to achieve end-to-end unbiased scene graph generation. During the SFT phase, we propose a relation augmentation strategy by leveraging an MLLM and refined via embedding similarity filtering to alleviate relation sparsity. Subsequently, a stage-aligned reward scheme optimizes the procedural reasoning during RL. Specifically, we propose a novel dual-granularity reward which integrates fine-grained and coarse-grained relation rewards, simultaneously mitigating the long-tail issue via frequency-based adaptive weighting of predicates and improving relation coverage through semantic clustering. Experiments on two benchmarks show that SGG-R^ 3 achieves superior performance compared to existing methods, demonstrating the effectiveness and generalization of the framework.</abstract>
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%0 Conference Proceedings
%T SGG-R^ 3: From Next-Token Prediction to End-to-End Unbiased Scene Graph Generation
%A Feng, Jiaye
%A Yin, Qixiang
%A Liu, Yuankun
%A Mo, Tong
%A Li, Weiping
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F feng-etal-2026-sgg
%X Scene Graph Generation (SGG) structures visual scenes as graphs of objects and their relations. While Multimodal Large Language Models (MLLMs) have advanced end-to-end SGG, current methods are hindered by both a lack of task-specific structured reasoning and the challenges of sparse, long-tailed relation distributions, resulting in incomplete scene graphs characterized by low recall and biased predictions. To address these issues, we introduce SGG-R^ 3, a structured reasoning framework that integrates task-specific Chain-of-Thought (CoT)-guided supervised fine-tuning (SFT) and reinforcement learning (RL) with group sequence policy optimization (GSPO), designed to engage in three sequential stages to achieve end-to-end unbiased scene graph generation. During the SFT phase, we propose a relation augmentation strategy by leveraging an MLLM and refined via embedding similarity filtering to alleviate relation sparsity. Subsequently, a stage-aligned reward scheme optimizes the procedural reasoning during RL. Specifically, we propose a novel dual-granularity reward which integrates fine-grained and coarse-grained relation rewards, simultaneously mitigating the long-tail issue via frequency-based adaptive weighting of predicates and improving relation coverage through semantic clustering. Experiments on two benchmarks show that SGG-R^ 3 achieves superior performance compared to existing methods, demonstrating the effectiveness and generalization of the framework.
%R 10.18653/v1/2026.findings-acl.992
%U https://aclanthology.org/2026.findings-acl.992/
%U https://doi.org/10.18653/v1/2026.findings-acl.992
%P 19811-19830

Markdown (Informal)

[SGG-R 3: From Next-Token Prediction to End-to-End Unbiased Scene Graph Generation](https://aclanthology.org/2026.findings-acl.992/) (Feng et al., Findings 2026)

• SGG-R 3: From Next-Token Prediction to End-to-End Unbiased Scene Graph Generation (Feng et al., Findings 2026)

ACL

• Jiaye Feng, Qixiang Yin, Yuankun Liu, Tong Mo, and Weiping Li. 2026. SGG-R 3: From Next-Token Prediction to End-to-End Unbiased Scene Graph Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19811–19830, San Diego, California, United States. Association for Computational Linguistics.