Semantic Modeling of Textual Relationships in Cross-Modal Retrieval (1810.13151v3)

Abstract: Feature modeling of different modalities is a basic problem in current research of cross-modal information retrieval. Existing models typically project texts and images into one embedding space, in which semantically similar information will have a shorter distance. Semantic modeling of textural relationships is notoriously difficult. In this paper, we propose an approach to model texts using a featured graph by integrating multi-view textual relationships including semantic relations, statistical co-occurrence, and prior relations in the knowledge base. A dual-path neural network is adopted to learn multi-modal representations of information and cross-modal similarity measure jointly. We use a Graph Convolutional Network (GCN) for generating relation-aware text representations, and use a Convolutional Neural Network (CNN) with non-linearities for image representations. The cross-modal similarity measure is learned by distance metric learning. Experimental results show that, by leveraging the rich relational semantics in texts, our model can outperform the state-of-the-art models by 3.4% and 6.3% on accuracy on two benchmark datasets.

• The paper introduces a novel graph-based framework to model semantic, co-occurrence, and knowledge relationships in text.
• It employs a combined GCN-CNN architecture to jointly learn rich textual and visual representations for improved retrieval.
• The approach outperforms state-of-the-art methods with up to 6.3% gain in MAP scores across benchmark datasets.

Semantic Modeling of Textual Relationships in Cross-Modal Retrieval

The paper "Semantic Modeling of Textual Relationships in Cross-Modal Retrieval" aims to enhance cross-modal information retrieval (CMIR) by introducing a framework that integrates multiple textual relationships into a unified graph representation. The focus is on improving the semantic modeling of texts and bridging the gap between textual and visual data through a novel graph-based approach.

Introduction

Cross-modal information retrieval facilitates the retrieval of information in one modality using queries from another. The paper contends that the key challenge in CMIR is representing features from different modalities within a single, coherent semantic space, which traditionally involves using flat feature representations for text and visual data. The authors argue that these existing models fail to capture the nuanced relational semantics inherent in textual data, motivating the need for a more sophisticated modeling strategy.

Figure 1: (a) The original text and three kinds of textual relationships: (b) distributed semantic relationship in the embedding space, (c) word co-occurrence relationship and (d) general knowledge relationship defined by a knowledge graph.

Methodology

The proposed method employs a GCN-CNN architecture, which integrates Graph Convolutional Networks (GCN) for textual feature extraction and Convolutional Neural Networks (CNN) for visual feature modeling. A multimodal semantic space is learned and utilized for similarity matching:

• Textual Relationship Construction: The paper constructs a graph model incorporating three types of relationships—semantic, co-occurrence, and knowledge-based—to capture diverse textual relations. This graph is constructed offline and shared by all text samples.
• Graph Convolutional Network: A GCN is employed to enhance text representations by leveraging the constructed graph. This is aimed at learning relationship-aware features that better represent semantic content in the text.
• Framework Overview: The dual-path network consists of text modeling via GCN, image modeling using a CNN backbone, and distance metric learning, which computes the similarity between textual and visual features for retrieval purposes.

  Figure 2: The schematic illustration of our proposed framework for cross-modal retrieval.

Results

The authors evaluated their approach on two benchmark datasets, CMPlaces and Eng-Wiki, demonstrating superior performance compared to several state-of-the-art methods. Their approach significantly boosted average Mean Average Precision (MAP) scores, particularly for text queries:

• Performance Metrics: The proposed model, SCKR, achieved a 3.4% and 6.3% improvement in retrieval accuracy on Eng-Wiki and CMPlaces datasets respectively. This notable gain underscores the efficacy of incorporating diverse textual relationships in text modeling for CMIR tasks.
• Ablation Studies and Qualitative Analysis: Ablation studies illustrate the significance of integrating multiple relationship types. The paper shows that individual models focusing on specific relationships (semantic, co-occurrence, or knowledge) are outperformed by their integrated approach.

  Figure 3: Some samples of text query results using four of our models on the CMPlaces dataset. The corresponding relation graphs are shown in the second column. The retrieval results are given in the third column.

Conclusion

The research offers significant insights into CMIR by introducing a comprehensive relationship-aware text modeling framework utilizing GCN and CNN. Its contribution lies in demonstrating the utility of harnessing multiple relational views to improve text-image cross-modal retrieval performance. The integration of semantic, co-occurrence, and knowledge relationships collectively enhances the semantic representation of texts, leading to better generalization and retrieval accuracy. Future work could extend this relational modeling approach to other multimodal applications, including image and video captioning.