ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation

TL;DR

ReLIC-SGG is a novel framework for open-vocabulary scene graph generation that models relation incompleteness and semantic relations to improve predicate recognition and recover missing relations.

Contribution

It introduces a relation lattice and positive-unlabeled learning to handle incomplete annotations and semantic variability in open-vocabulary SGG.

Findings

Improves recognition of rare and unseen predicates.

Recovers missing relations more effectively.

Outperforms existing methods on multiple benchmarks.

Abstract

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.