Learning Structured Visual Compositional Representations for Weakly Supervised Referring Expression Comprehension

2026-07-06 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, medium

Summary

A new Structured Visual Compositional Representation (SVCR) learning framework is proposed to advance Weakly Supervised Referring Expression Comprehension (WREC). WREC aims to localize objects in images using natural language descriptions with limited supervision. Existing WREC methods primarily use anchor-level visual representations that implicitly encode relational interactions, resulting in flat, unary-only visual representations which limit alignment with the structured nature of language. The SVCR framework explicitly models both unary object embeddings and pairwise relational embeddings, creating a structured visual representation space. It also introduces a compositional alignment mechanism to unify the matching of unary and pairwise visual representations with their corresponding textual embeddings under weak supervision. Extensive experiments on RefCOCO, RefCOCO+, and RefCOCOg datasets demonstrate that SVCR achieves state-of-the-art performance, validating the effectiveness of explicit structured visual representations and visual-textual alignment for WREC.

Key takeaway

For Machine Learning Engineers developing weakly supervised referring expression comprehension systems, you should consider adopting the Structured Visual Compositional Representation (SVCR) framework. This approach explicitly models both unary object and pairwise relational embeddings. It significantly improves performance on datasets like RefCOCO. Implementing SVCR overcomes limitations of flat visual representations. This enables more accurate object localization by better aligning visual data with structured natural language descriptions.

Key insights

Explicitly modeling structured visual representations, including unary and pairwise embeddings, significantly enhances weakly supervised referring expression comprehension.

Principles

• Explicitly model visual relations.
• Structured visuals align with language.
• Unify visual-textual compositional alignment.

Method

The SVCR framework explicitly models unary object and pairwise relational embeddings to form a structured visual representation space. It then uses a compositional alignment mechanism for unified visual-textual matching under weak supervision.

In practice

• Apply SVCR for WREC tasks.
• Model explicit visual relations.
• Use compositional visual-textual alignment.

Topics

• Referring Expression Comprehension
• Weakly Supervised Learning
• Visual Representation Learning
• Compositional Models
• Visual-Textual Alignment
• Object Localization
• RefCOCO Datasets

Code references

• Yangyi-Chen/vi-struct

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.