Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks (1812.04794v1)

Abstract: The task in referring expression comprehension is to localise the object instance in an image described by a referring expression phrased in natural language. As a language-to-vision matching task, the key to this problem is to learn a discriminative object feature that can adapt to the expression used. To avoid ambiguity, the expression normally tends to describe not only the properties of the referent itself, but also its relationships to its neighbourhood. To capture and exploit this important information we propose a graph-based, language-guided attention mechanism. Being composed of node attention component and edge attention component, the proposed graph attention mechanism explicitly represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches. Furthermore, the proposed graph attention mechanism enables the comprehension decision to be visualisable and explainable. Experiments on three referring expression comprehension datasets show the advantage of the proposed approach.

• The paper introduces LGRAN, a novel framework that uses language-guided graph attention to dynamically integrate object features for accurate referring expression comprehension.
• It employs dual attention mechanisms—node and edge attentions—to finely capture both object cues and inter-object relationships in images.
• Empirical results on RefCOCO, RefCOCO+, and RefCOCOg demonstrate that LGRAN outperforms state-of-the-art methods in precision and model explainability.

Language-Guided Graph Attention Networks for Referring Expression Comprehension

The paper "Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks" introduces a novel approach to the task of referring expression comprehension using a graph-based, language-guided attention mechanism. The challenge in this task is to localize an object in an image based on a natural language description, requiring a fine-grained understanding of both the linguistic expression and the image's visual elements.

Graph-Based Attention Mechanism

The authors propose a Language-guided Graph Attention Network (LGRAN) that leverages graphs to represent and infer relationships between objects in an image. The graph consists of nodes corresponding to objects and edges representing inter-object relationships. This approach contrasts with conventional methods that often consider objects independently. By using a language-guided attention mechanism, LGRAN dynamically adapts the representation of each object based on the referring expression, thus tailoring the object features specifically for the task.

Key Components

LGRAN is built upon two pivotal attention mechanisms: node attention and edge attention. The node attention focuses on highlighting the potential objects described by the expression, effectively narrowing the search space for the correct referent. Meanwhile, the edge attention is tasked with identifying and emphasizing the relationships pertinent to the referring expression. This dual approach allows LGRAN to produce more discriminative object representations by taking into account the syntactic and semantic structure of the language.

The edge attention is further divided into intra-class and inter-class categories, aiming to distinguish relationships between objects of the same type and those among different types. This division facilitates more nuanced attention, as these relationships typically differ both visually and semantically.

Empirical Results

The paper validates the proposed approach across three well-established datasets: RefCOCO, RefCOCO+, and RefCOCOg. LGRAN demonstrates superior performance over existing state-of-the-art methods across various splits, indicating its effectiveness in dealing with the complexities of referring expressions. By surpassing previous methods, LGRAN presents a robust solution to the problems posed in referring expression comprehension, thus shedding light on effectively integrating visual and linguistic modalities.

Implications and Future Directions

This work opens new avenues in the development of intelligent systems capable of understanding complex linguistic cues in visual contexts, thereby enriching human-computer interaction capabilities. By rendering the comprehension decision both visualisable and explainable, LGRAN contributes to the growing discourse on transparency and interpretability in AI models.

The potential applications are vast, including improved systems for human-robot interaction, autonomous driving, and enhanced visual search engines. Future research could explore the integration of LGRAN with other multimodal learning paradigms, as well as its adaptability to more extensive vocabularies and varied linguistic constructs within real-world scenarios.

This paper offers a meticulous exploration of language-guided graph attention mechanisms, extending the scope of linguistic-visual integration and promising substantial advancements in the precision and explainability of referring expression comprehension systems.