CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning (1710.05106v2)

Abstract: It is known that the inconsistent distribution and representation of different modalities, such as image and text, cause the heterogeneity gap that makes it challenging to correlate such heterogeneous data. Generative adversarial networks (GANs) have shown its strong ability of modeling data distribution and learning discriminative representation, existing GANs-based works mainly focus on generative problem to generate new data. We have different goal, aim to correlate heterogeneous data, by utilizing the power of GANs to model cross-modal joint distribution. Thus, we propose Cross-modal GANs to learn discriminative common representation for bridging heterogeneity gap. The main contributions are: (1) Cross-modal GANs architecture is proposed to model joint distribution over data of different modalities. The inter-modality and intra-modality correlation can be explored simultaneously in generative and discriminative models. Both of them beat each other to promote cross-modal correlation learning. (2) Cross-modal convolutional autoencoders with weight-sharing constraint are proposed to form generative model. They can not only exploit cross-modal correlation for learning common representation, but also preserve reconstruction information for capturing semantic consistency within each modality. (3) Cross-modal adversarial mechanism is proposed, which utilizes two kinds of discriminative models to simultaneously conduct intra-modality and inter-modality discrimination. They can mutually boost to make common representation more discriminative by adversarial training process. To the best of our knowledge, our proposed CM-GANs approach is the first to utilize GANs to perform cross-modal common representation learning. Experiments are conducted to verify the performance of our proposed approach on cross-modal retrieval paradigm, compared with 10 methods on 3 cross-modal datasets.

Overview of CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning

This paper introduces Cross-modal Generative Adversarial Networks (CM-GANs) developed to address the heterogeneity gap present between multimodal datasets, such as images and text. The heterogeneity gap, resulting from inconsistencies in data distribution and representation across modalities, presents a significant challenge in the correlation of heterogeneous data. CM-GANs leverage the power of Generative Adversarial Networks (GANs) to learn common representations that bridge this gap. Unlike traditional GAN implementations focused on data generation, CM-GANs emphasize learning from existing data to enhance cross-modal correlations.

Core Contributions

The paper outlines the CM-GANs architecture with three primary contributions:

1. Cross-modal GANs for Joint Distribution Modeling: The architecture models inter-modality and intra-modality correlations through generative and discriminative models, improving cross-modal correlation learning.
2. Cross-modal Convolutional Autoencoders: These autoencoders utilize weight-sharing constraints to capture common representations, preserving semantic consistency across modalities through reconstruction information.
3. Cross-modal Adversarial Mechanism: This mechanism employs two types of discriminative models for intra-modality and inter-modality discrimination, iteratively enhancing the generative models to produce more discriminative common representations.

Experimental Evaluation

The paper provides extensive experimental validation using three datasets: the newly constructed large-scale XMediaNet, the Wikipedia dataset, and the Pascal Sentence dataset. These experiments primarily focus on cross-modal retrieval tasks—both bi-modal and all-modal retrievals—to assess the performance of the learned common representations. The results demonstrate superior performance over ten state-of-the-art methods. Notably, CM-GANs showed considerable improvements in Mean Average Precision (MAP) scores, establishing the effectiveness of the approach in correlating multimodal data.

Implications and Future Prospects

The theoretical implications of this research highlight the potential of using adversarial training for cross-modal representation learning, offering a robust framework for overcoming modality discrepancies. Practically, CM-GANs could be crucial in applications where diverse data types must be synergistically integrated, such as multimedia retrieval systems and advanced AI-driven data analytics.

Looking forward, expanding the scope of CM-GANs to include a broader range of modalities like video and audio could enhance its applicability. Moreover, exploring unsupervised variants of this approach could be essential in handling increasingly large volumes of unlabelled multimodal data, potentially setting the path toward more generalized and autonomous cross-modal learning systems.

In summary, this paper presents a well-structured approach to addressing the heterogeneity gap in multimodal data using advanced GAN architectures, establishing a foundation for further innovations in cross-modal machine learning.