CLAMP: Contrastive Learning with Adaptive Multi-loss and Progressive Fusion for Multimodal Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2507.16854v1
• Date: Mon, 21 Jul 2025 11:49:57 GMT
• Title: CLAMP: Contrastive Learning with Adaptive Multi-loss and Progressive Fusion for Multimodal Aspect-Based Sentiment Analysis
• Authors: Xiaoqiang He,
• Abstract summary: This paper introduces an end to end Contrastive Learning framework with Adaptive Multi-loss and Progressive Attention Fusion.<n>The framework is composed of three novel modules: Progressive Attention Fusion network, Multi-task Contrastive Learning, and Adaptive Multi-loss Aggregation.<n> evaluation on standard public benchmarks demonstrates that CLAMP consistently outperforms the vast majority of existing state of the art methods.
• Score: 0.6961946145048322
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal aspect-based sentiment analysis(MABSA) seeks to identify aspect terms within paired image-text data and determine their fine grained sentiment polarities, representing a fundamental task for improving the effectiveness of applications such as product review systems and public opinion monitoring. Existing methods face challenges such as cross modal alignment noise and insufficient consistency in fine-grained representations. While global modality alignment methods often overlook the connection between aspect terms and their corresponding local visual regions, bridging the representation gap between text and images remains a challenge. To address these limitations, this paper introduces an end to end Contrastive Learning framework with Adaptive Multi-loss and Progressive Attention Fusion(CLAMP). The framework is composed of three novel modules: Progressive Attention Fusion network, Multi-task Contrastive Learning, and Adaptive Multi-loss Aggregation. The Progressive Attention Fusion network enhances fine-grained alignment between textual features and image regions via hierarchical, multi-stage cross modal interactions, effectively suppressing irrelevant visual noise. Secondly, multi-task contrastive learning combines global modal contrast and local granularity alignment to enhance cross modal representation consistency. Adaptive Multi-loss Aggregation employs a dynamic uncertainty based weighting mechanism to calibrate loss contributions according to each task's uncertainty, thereby mitigating gradient interference. Evaluation on standard public benchmarks demonstrates that CLAMP consistently outperforms the vast majority of existing state of the art methods.

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