ReLIC-SGG: 开放词汇场景图生成的关系格补全
ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation
AI总结：
针对开放词汇场景图生成中标注不完整导致大量有效关系被误判为负例的问题，提出ReLIC-SGG框架，通过构建语义关系格建模谓词间的相似、蕴含和矛盾关系，将未标注关系视为潜在变量而非确定负例，结合视觉-语言兼容性、图上下文和语义一致性推断缺失正关系，并采用正-无标记图学习减少假负例监督，格引导解码生成紧凑且语义一致的场景图。
摘要：
开放词汇场景图生成（SGG）旨在用超越固定谓词集的灵活关系短语描述视觉场景。现有方法通常将标注的三元组视为正例，所有未标注的对象-对关系视为负例。然而，场景图标注本质上是不完整的：许多有效关系缺失，且同一交互可以以不同粒度描述，例如 extit{on}、 extit{standing on}、 extit{resting on} 和 extit{supported by}。由于开放词汇SGG的关系空间更大，这一问题变得更加严重。我们提出 extbf{ReLIC-SGG}，一种关系不完整性感知框架，将未标注关系视为潜在变量而非确定负例。ReLIC-SGG构建语义关系格来建模开放词汇谓词间的相似性、蕴含和矛盾关系，并利用它从视觉-语言兼容性、图上下文和语义一致性中推断缺失的正关系。正-无标记图学习目标进一步减少假负例监督，而格引导解码生成紧凑且语义一致的场景图。在常规、开放词汇和全景SGG基准上的实验表明，ReLIC-SGG改进了稀有和未见谓词的识别，并更好地恢复了缺失关系。
英文摘要：
Open-vocabulary scene graph generation (SGG) aims to describe visual scenes with flexible relation phrases beyond a fixed predicate set. Existing methods usually treat annotated triplets as positives and all unannotated object-pair relations as negatives. However, scene graph annotations are inherently incomplete: many valid relations are missing, and the same interaction can be described at different granularities, e.g., \textit{on}, \textit{standing on}, \textit{resting on}, and \textit{supported by}. This issue becomes more severe in open-vocabulary SGG due to the much larger relation space. We propose \textbf{ReLIC-SGG}, a relation-incompleteness-aware framework that treats unannotated relations as latent variables rather than definite negatives. ReLIC-SGG builds a semantic relation lattice to model similarity, entailment, and contradiction among open-vocabulary predicates, and uses it to infer missing positive relations from visual-language compatibility, graph context, and semantic consistency. A positive-unlabeled graph learning objective further reduces false-negative supervision, while lattice-guided decoding produces compact and semantically consistent scene graphs. Experiments on conventional, open-vocabulary, and panoptic SGG benchmarks show that ReLIC-SGG improves rare and unseen predicate recognition and better recovers missing relations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
