CCL: Cross-modal Correlation Learning with Multi-grained Fusion by Hierarchical Network (1704.02116v4)

Abstract: Cross-modal retrieval has become a highlighted research topic for retrieval across multimedia data such as image and text. A two-stage learning framework is widely adopted by most existing methods based on Deep Neural Network (DNN): The first learning stage is to generate separate representation for each modality, and the second learning stage is to get the cross-modal common representation. However, the existing methods have three limitations: (1) In the first learning stage, they only model intra-modality correlation, but ignore inter-modality correlation with rich complementary context. (2) In the second learning stage, they only adopt shallow networks with single-loss regularization, but ignore the intrinsic relevance of intra-modality and inter-modality correlation. (3) Only original instances are considered while the complementary fine-grained clues provided by their patches are ignored. For addressing the above problems, this paper proposes a cross-modal correlation learning (CCL) approach with multi-grained fusion by hierarchical network, and the contributions are as follows: (1) In the first learning stage, CCL exploits multi-level association with joint optimization to preserve the complementary context from intra-modality and inter-modality correlation simultaneously. (2) In the second learning stage, a multi-task learning strategy is designed to adaptively balance the intra-modality semantic category constraints and inter-modality pairwise similarity constraints. (3) CCL adopts multi-grained modeling, which fuses the coarse-grained instances and fine-grained patches to make cross-modal correlation more precise. Comparing with 13 state-of-the-art methods on 6 widely-used cross-modal datasets, the experimental results show our CCL approach achieves the best performance.

Analysis of "CCL: Cross-modal Correlation Learning with Multi-grained Fusion by Hierarchical Network"

The paper "CCL: Cross-modal Correlation Learning with Multi-grained Fusion by Hierarchical Network" addresses significant limitations in the domain of cross-modal retrieval, specifically the gap in effective representation between multimedia data such as images and text. The authors propose a novel approach, Cross-modal Correlation Learning (CCL), which employs a hierarchical network architecture to improve retrieval accuracy by exploiting both intra-modality and inter-modality correlations with multi-grained fusion strategies.

Key Contributions

1. Joint Optimization in Separate Representation Learning: The paper highlights a two-stage learning process widely used in cross-modal retrieval systems. Commonly, separate representations for each modality are modeled using intra-modality correlations, neglecting potential inter-modality insights. This research introduces joint optimization techniques at the first stage that simultaneously leverage both correlations, thus preserving essential complementary contexts.
2. Multi-task Learning Strategy for Common Representation: Current approaches typically utilize shallow network architectures with single-loss regularization, missing out on the interplay between intra-modality and inter-modality correlations. This research proposes a multi-task strategy that adaptively balances semantic category constraints within a modality and pairwise similarity constraints across modalities. As these tasks are intrinsically relevant, their co-learning boosts generalization performance.
3. Multi-grained Modeling through Hierarchical Network: The authors address the oversight in leveraging fine-grained clues provided by patches of original instances. Their system adopts a multi-pathway network integrating both coarse-grained instances and fine-grained patches, capturing more precise cross-modal correlations. This approach effectively overcomes typical reliance on only original instances.

Strong Numerical Results

The CCL method was rigorously compared to thirteen state-of-the-art techniques across six widely-used cross-modal datasets. It consistently achieved superior Mean Average Precision (MAP) scores in both bi-modal and all-modal retrieval tasks. For instance, on the Wikipedia dataset using CNN features, CCL reached an average MAP of 0.481 for bi-modal retrieval. This substantive improvement is attributed to both the richer modeling of correlations and the novel integration of multi-grained data representations.

Implications

Practical Implications: The proposed model holds significant improvements for real-world applications such as multimedia search engines and data categorization systems. By bridging the heterogeneity gap between different modalities more effectively, users can expect an enhanced retrieval experience with more accurate and relevant results across disparate media.

Theoretical Implications: The paper’s introduction of hierarchical network models to simultaneous intra- and inter-modality learning tasks offers a pivotal advance in representation learning. This framework underscores the importance of task relationships in multi-task learning, setting a foundation for further exploration of multi-modal data representation strategies.

Future Prospects in AI

The research opens avenues for more sophisticated cross-domain AI applications. Future developments could explore extending the approach to other modality combinations or examine how the multi-task learning framework could generalize to emerging AI fields. Enhanced segmentation strategies for fine-grained information extraction and semi-supervised regularization might pave the way for further advancements.

This paper presents a comprehensive approach to improving cross-modal retrieval performance by addressing inherent modeling deficiencies in traditional systems and proposing robust solutions through hierarchical network design and innovative learning strategies.