MCFNet: A Multimodal Collaborative Fusion Network for Fine-Grained Semantic Classification

TL;DR

This paper introduces MCFNet, a novel multimodal fusion network that enhances fine-grained image classification by effectively capturing subtle semantic interactions across modalities using a hybrid attention mechanism and collaborative decision strategies.

Contribution

The paper proposes a new fusion architecture with modality-specific regularization, hybrid attention, and a joint decision module for improved multimodal fine-grained classification.

Findings

Achieves consistent accuracy improvements on benchmark datasets.

Effectively models subtle cross-modal semantic interactions.

Demonstrates the importance of integrated fusion and decision strategies.

Abstract

Multimodal information processing has become increasingly important for enhancing image classification performance. However, the intricate and implicit dependencies across different modalities often hinder conventional methods from effectively capturing fine-grained semantic interactions, thereby limiting their applicability in high-precision classification tasks. To address this issue, we propose a novel Multimodal Collaborative Fusion Network (MCFNet) designed for fine-grained classification. The proposed MCFNet architecture incorporates a regularized integrated fusion module that improves intra-modal feature representation through modality-specific regularization strategies, while facilitating precise semantic alignment via a hybrid attention mechanism. Additionally, we introduce a multimodal decision classification module, which jointly exploits inter-modal correlations and unimodal discriminative features by integrating multiple loss functions within a weighted voting paradigm. Extensive experiments and ablation studies on benchmark datasets demonstrate that the proposed MCFNet framework achieves consistent improvements in classification accuracy, confirming its effectiveness in modeling subtle cross-modal semantics.