Multi-hop Relational Contrastive Learning: Extending Spatial Contrastive Pre-training Beyond Pairwise Relations

2026-05-19 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

Multi-Hop Relational Contrastive Learning (MRCL) is a new framework that extends spatial contrastive pre-training to graph-structured scene representations, moving beyond traditional pairwise object relationships. It constructs scene graphs from detected objects and traces k-hop paths to capture implicit, compositional spatial dependencies. MRCL defines a multi-level contrastive objective across nodes, edges, and multi-hop paths, aiming for embeddings that are stable across object semantics yet responsive to spatial layout. Evaluated on a GQA subset, MRCL achieved a Normalized Discounted Cumulative Gain at 5 (NDCG@5) of 0.748 for content-based graph retrieval and consistently improved downstream tasks like spatial relationship recognition and graph-based question answering. This indicates that multi-hop relational supervision provides richer structural guidance than pairwise-only methods, leading to more robust, compositional, and geometry-aware visual representations.

Key takeaway

For research scientists developing computer vision models for complex scene understanding, you should consider integrating multi-hop relational contrastive learning. This approach, by modeling compositional spatial dependencies via k-hop paths in scene graphs, yields more robust and spatially-aware representations than pairwise methods. Implementing MRCL can significantly enhance performance in tasks requiring precise spatial reasoning, such as robotics, autonomous navigation, and graph-based question answering, by providing richer structural guidance.

Key insights

Multi-hop relational contrastive learning captures complex spatial dependencies beyond pairwise relations, improving scene understanding.

Principles

• Spatial reasoning requires compositional chains of relations.
• K-hop paths encode implicit compositional spatial relations.
• Graph-level contrastive learning improves compositional generalization.

Method

MRCL constructs scene graphs, extracts k-hop paths, and uses a graph neural network to encode these paths. A multi-hop contrastive objective aligns visual embeddings with graph embeddings, generalizing C-SIP's pairwise loss.

In practice

• Use k-hop paths for richer spatial context.
• Apply MRCL for improved graph retrieval.
• Integrate MRCL for better spatial relationship recognition.

Topics

• Multi-Hop Relational Contrastive Learning
• Scene Graphs
• Spatial Reasoning
• Contrastive Learning
• Graph Neural Networks

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.