CLAMP：面向多模态方面级情感分析的自适应多损失与渐进融合对比学习
CLAMP: Contrastive Learning with Adaptive Multi-loss and Progressive Fusion for Multimodal Aspect-Based Sentiment Analysis
AI总结：
针对MABSA中的跨模态对齐噪声与细粒度一致性不足问题，提出CLAMP框架，通过渐进注意力融合、多任务对比学习和不确定性动态多损失聚合提升性能。
摘要：
多模态方面级情感分析（MABSA）旨在识别成对图文数据中的方面词，并确定其细粒度情感极性，是提升产品评论系统、舆情监测等应用效果的一项基础任务。现有方法面临跨模态对齐噪声以及细粒度表示一致性不足等挑战。尽管全局模态对齐方法往往忽视方面词与其对应局部视觉区域之间的联系，但弥合文本与图像之间的表示差距仍然是一项难题。为克服这些局限，本文提出一种端到端的自适应多损失与渐进注意力融合对比学习框架（CLAMP）。该框架由三个新颖模块组成：渐进注意力融合网络、多任务对比学习和自适应多损失聚合。渐进注意力融合网络通过分层、多阶段的跨模态交互增强文本特征与图像区域之间的细粒度对齐，有效抑制无关视觉噪声。其次，多任务对比学习结合全局模态对比与局部粒度对齐，以增强跨模态表示一致性。自适应多损失聚合采用基于不确定性的动态加权机制，根据每项任务的不确定性校准损失贡献，从而缓解梯度干扰。在标准公开基准上的评估表明，CLAMP始终优于绝大多数现有最先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
