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

TL;DR

This paper introduces CCL, a hierarchical network that enhances cross-modal retrieval by modeling multi-grained, intra- and inter-modality correlations with a multi-stage, multi-task learning framework, outperforming existing methods.

Contribution

The paper proposes a novel hierarchical network with multi-grained fusion and multi-level association, addressing limitations of existing cross-modal retrieval methods.

Findings

Achieves the best performance on 6 datasets compared to 13 state-of-the-art methods.

Effectively models intra- and inter-modality correlations with multi-grained fusion.

Utilizes multi-task learning to balance semantic and similarity constraints.

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.