Language-Conditioned Graph Networks for Relational Reasoning

TL;DR

This paper introduces Language-Conditioned Graph Networks (LCGN), a framework that creates context-aware object representations for relational reasoning in grounded language tasks, improving accuracy across multiple datasets.

Contribution

The paper presents a novel LCGN framework that integrates language conditioning into graph-based object representations for enhanced relational reasoning.

Findings

LCGN effectively supports relational reasoning tasks.

Improves performance across multiple datasets.

Provides a flexible, context-aware object representation method.

Abstract

Solving grounded language tasks often requires reasoning about relationships between objects in the context of a given task. For example, to answer the question "What color is the mug on the plate?" we must check the color of the specific mug that satisfies the "on" relationship with respect to the plate. Recent work has proposed various methods capable of complex relational reasoning. However, most of their power is in the inference structure, while the scene is represented with simple local appearance features. In this paper, we take an alternate approach and build contextualized representations for objects in a visual scene to support relational reasoning. We propose a general framework of Language-Conditioned Graph Networks (LCGN), where each node represents an object, and is described by a context-aware representation from related objects through iterative message passing conditioned on the textual input. E.g., conditioning on the "on" relationship to the plate, the object "mug" gathers messages from the object "plate" to update its representation to "mug on the plate", which can be easily consumed by a simple classifier for answer prediction. We experimentally show that our LCGN approach effectively supports relational reasoning and improves performance across several tasks and datasets. Our code is available at http://ronghanghu.com/lcgn.