Simple to Complex Cross-modal Learning to Rank

Abstract: The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model's robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the state-of-the-arts in this literature.

• The paper introduces a non-linear mapping approach combined with Self-Paced Learning with Diversity to effectively optimize cross-modal ranking.
• It converts image and text features into a shared embedding space using cosine similarity, capturing complex inter-modal relationships.
• The alternating optimization algorithm efficiently handles large-scale datasets, achieving superior mAP scores on benchmark tests.

Simple to Complex Cross-modal Learning to Rank

The paper "Simple to Complex Cross-modal Learning to Rank" (1702.01229) addresses the challenges inherent in cross-modal retrieval, specifically the heterogeneity-gap between different modalities. It proposes a novel approach to learning an optimal multi-modal embedding space using non-linear mapping functions, enhanced by Self-Paced Learning with Diversity (SPLD) to better handle outliers and improve generalization.

Framework Overview

Non-linear Mapping for Multi-modal Embedding

The paper introduces non-linear mapping functions that transform both image and text feature vectors into a shared embedding space. This approach leverages the cosine similarity between projected vectors to measure cross-modal similarity. The non-linear transformation ensures that more complex inter-modal relationships can be captured compared to traditional linear mapping strategies, thereby explicitly addressing the content alignment between different modalities.

Ranking Optimization

The paper frames cross-modal retrieval as a ranking optimization problem. It associates each image query with a set of ranked, alternative text descriptions and assigns importance weights to these rankings. The objective is to minimize a ranking-based loss that encourages aligned image-text pairs to achieve higher relevance scores.

Self-Paced Learning with Diversity

Self-Paced Learning (SPL) is incorporated to guide the learning process from simpler rankings to more challenging ones. SPLD further enhances this by ensuring that rank selection covers diverse query scenarios, thus preventing overfitting and promoting robust model behavior across varied datasets. The SPLD mechanism adapts the contribution of each ranking based on their complexity and diversity.

Algorithm Optimization

An alternating optimization algorithm is proposed to iteratively update embedding parameters and importance weights. By leveraging gradient descent methods, the paper ensures efficient computation of the embedding space, taking advantage of structured complexity reductions for scalability on large datasets.

Computational Complexity Analysis

The algorithm is designed to handle large-scale datasets efficiently. Computational overhead primarily involves obtaining similarity scores for selected tetrads, with complexity measures outlined for the optimization process. This provides the practical feasibility of implementing the method in real-world applications.

Experimental Evaluations

Performance on Benchmark Datasets

Experiments conducted on Pascal'07, NUS-WIDE, and Wiki datasets illustrate the superior performance of the proposed approach over existing methods such as CCA, C-CRF, PAMIR, and deep learning-based strategies. Notably, the proposed method achieved higher mean average precision (mAP) scores consistently across different retrieval directions.

Impact of Diversity Regularization

The inclusion of diversity regularization markedly improved convergence rates and final performance metrics. It was observed that the model achieved optimal solutions with fewer iterations, underscoring the efficacy of SPLD in avoiding local minima and maintaining high generalization capacity.

Conclusion

The paper successfully demonstrates that integrating SPLD with complex non-linear mappings significantly enhances cross-modal retrieval tasks. This approach effectively bridges the heterogeneity-gap between modalities by adapting the importance of rankings and promoting diverse query representation. Future work could explore potential extensions to weakly-supervised learning environments and expand this methodology to additional domains such as attribute detection and action recognition.