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

• URL: http://arxiv.org/abs/2604.22546v3
• Date: Tue, 28 Apr 2026 02:01:57 GMT
• Title: ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation
• Abstract summary: We propose textbfReLIC-SGG, a relation-incompleteness-aware framework for open-vocabulary scene graph generation.<n>ReLIC-SGG builds a semantic relation lattice to model similarity, entailment, and contradiction among open-vocabulary predicates.<n> Experiments show that ReLIC-SGG improves rare and unseen predicate recognition and better recovers missing relations.
• Score: 5.184088650563149
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses [1.3468350096927395]
  We propose a framework for open-vocabulary 3D scene graph generation from posed RGB-D sequences under incomplete relation supervision.<n>The key concept is a relation witness: a concrete visual-geometric cue that makes a relation observable in the captured scene.<n> RelWitness constructs relation witness records from RGB views, depth maps, reconstructed 3D geometry, role-sensitive text, object-prior null views, and multi-view consistency.
  arXiv  Detail & Related papers  (2026-05-20T07:18:56Z)
• CAGE-SGG: Counterfactual Active Graph Evidence for Open-Vocabulary Scene Graph Generation [16.971992237358638]
  Open-vocabulary scene graph generation (SGG) aims to describe visual scenes with flexible and fine-grained relation phrases.<n>We propose an evidence-rounded open-vocabulary SGG framework based on counterfactual relation verification.
  arXiv  Detail & Related papers  (2026-04-24T06:34:45Z)
• Leveraging Predicate and Triplet Learning for Scene Graph Generation [31.09787444957997]
  Scene Graph Generation (SGG) aims to identify entities and predict the relationship triplets. We propose a Dual-granularity Relation Modeling (DRM) network to leverage fine-grained triplet cues besides the coarse-grained predicate ones. Our method establishes new state-of-the-art performance on Visual Genome, Open Image, and GQA datasets.
  arXiv  Detail & Related papers  (2024-06-04T07:23:41Z)
• Relation Rectification in Diffusion Model [64.84686527988809]
  We introduce a novel task termed Relation Rectification, aiming to refine the model to accurately represent a given relationship it initially fails to generate. We propose an innovative solution utilizing a Heterogeneous Graph Convolutional Network (HGCN) The lightweight HGCN adjusts the text embeddings generated by the text encoder, ensuring the accurate reflection of the textual relation in the embedding space.
  arXiv  Detail & Related papers  (2024-03-29T15:54:36Z)
• Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and Retention [69.36723767339001]
  Scene Graph Generation (SGG) offers a structured representation critical in many computer vision applications. We propose a unified framework named OvSGTR towards fully open vocabulary SGG from a holistic view. For the more challenging settings of relation-involved open vocabulary SGG, the proposed approach integrates relation-aware pretraining.
  arXiv  Detail & Related papers  (2023-11-18T06:49:17Z)
• FloCoDe: Unbiased Dynamic Scene Graph Generation with Temporal Consistency and Correlation Debiasing [14.50214193838818]
  FloCoDe: Flow-aware Temporal and Correlation Debiasing with uncertainty attenuation for unbiased dynamic scene graphs. We propose correlation debiasing and a correlation-based loss to learn unbiased relation representations for long-tailed classes.
  arXiv  Detail & Related papers  (2023-10-24T14:59:51Z)
• Entailment Graph Learning with Textual Entailment and Soft Transitivity [69.91691115264132]
  We propose a two-stage method, Entailment Graph with Textual Entailment and Transitivity (EGT2) EGT2 learns local entailment relations by recognizing possible textual entailment between template sentences formed by CCG-parsed predicates. Based on the generated local graph, EGT2 then uses three novel soft transitivity constraints to consider the logical transitivity in entailment structures.
  arXiv  Detail & Related papers  (2022-04-07T08:33:06Z)
• Hyper-relationship Learning Network for Scene Graph Generation [95.6796681398668]
  We propose a hyper-relationship learning network, termed HLN, for scene graph generation. We evaluate HLN on the most popular SGG dataset, i.e., the Visual Genome dataset. For example, the proposed HLN improves the recall per relationship from 11.3% to 13.1%, and maintains the recall per image from 19.8% to 34.9%.
  arXiv  Detail & Related papers  (2022-02-15T09:26:16Z)
• Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation [48.21846438269506]
  Scene graph generation (SGG) aims to capture a wide variety of interactions between pairs of objects. Existing SGG methods fail to acquire complex reasoning about visual and textual correlations due to various biases in training data. We propose a novel framework for SGG training that exploits relation labels based on their informativeness.
  arXiv  Detail & Related papers  (2021-11-26T14:34:12Z)
• Dual ResGCN for Balanced Scene GraphGeneration [106.7828712878278]
  We propose a novel model, dubbed textitdual ResGCN, which consists of an object residual graph convolutional network and a relation residual graph convolutional network. The two networks are complementary to each other. The former captures object-level context information, textiti.e., the connections among objects. The latter is carefully designed to explicitly capture relation-level context information textiti.e., the connections among relations.
  arXiv  Detail & Related papers  (2020-11-09T07:44:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.