Challenge: Modern review helpfulness prediction systems focus on polishing cross-modal representations and suffer from inferior optimization.
Approach: They propose a method to polish cross-modal relation representations by learning mutual information through contrastive learning.
Outcome: The proposed framework outperforms baselines and achieves state-of-the-art results on two publicly available datasets.

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SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning (2022.coling-1)

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Challenge: e-commerce has become a research hotspot for review helpfulness prediction . a new approach to help predict helpfulness of multimodal product reviews is proposed .
Approach: They propose a machine learning task to identify helpfulness of multimodal product reviews . they use a probe-based strategy to enforce high attention weights on regions of greater significance .
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Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews (2021.acl-long)

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Challenge: Existing review helpfulness prediction tasks rely on text and image modalities to analyze review helpfuliness.
Approach: They propose a task to analyze review helpfulness from text and visual modalities and propose 'multi-perspective coherent reasoning' method to combine coherence between product and review is proposed.
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Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis (2022.findings-emnlp)

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Challenge: Recent work on multimodal representation learning has focused on uni-modality pre-training or cross-modalities integration.
Approach: They propose a framework for multimodal representation learning that uses uni-modal contrastive coding and an efficient unimodal feature augmentation strategy to capture intermodal dynamics.
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Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment (2024.lrec-main)

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Challenge: Current work on multi-modal semantic understanding primarily exploits a dual-encoder structure to separate image and text, but fails to learn cross-modal feature alignment.
Approach: They propose a CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment by projecting features from different modalities into a unified deep space.
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Improving Multimodal Sentiment Analysis: Supervised Angular margin-based Contrastive Learning for Enhanced Fusion Representation (2023.findings-emnlp)

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Challenge: Existing methods for multimodal sentiment analysis focus on forming positive and negative pairs, neglecting the variation in sentiment scores within the same class.
Approach: They propose a framework to enhance discrimination and generalizability of the multimodal representation and overcome biases in the fusion vector’s modality.
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Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction (2023.findings-acl)

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Challenge: Existing studies have shown that FCNNs perform inefficient splitting for review features, making it difficult to clearly differentiate helpful from unhelpful reviews.
Approach: They propose a listwise attention network that captures the MRHP ranking context and a pairwise optimization objective that enhances model generalization.
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UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning (2023.findings-acl)

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Challenge: Existing multimodal fusion methods ignore inter-modality relationship, treat each modality equally, suffer sensor noise, and thus reduce multimodal learning performance.
Approach: They propose a multimodal contrastive method to explore more reliable multimodal representations under the weak supervision of unimodal predicting.
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CLGSI: A Multimodal Sentiment Analysis Framework based on Contrastive Learning Guided by Sentiment Intensity (2024.findings-naacl)

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Challenge: Recent studies have focused on contrastive learning, but lack detailed learning of the distribution of sample pairs with different sentiment intensity differences in the contrastive training representation space.
Approach: They propose a framework for multimodal sentiment analysis based on contrastive learning guided by sentiment intensity (CLGSI) it selects positive and negative sample pairs based upon sentiment intensity differences and assigns corresponding weights accordingly.
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Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)

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Challenge: Multimodal Sentiment Analysis (MSA) is effective when using rich information from multiple sources, but the potential sentiment-irrelevant information across modalities may hinder the performance from being further improved.
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Learning Multimodal Contrast with Cross-modal Memory and Reinforced Contrast Recognition (2024.findings-acl)

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Challenge: Using a memory module, we learn multimodal contrast using encoding-decoding paradigm . multimodal information are used in many applications, including news feeding, social media, etc.
Approach: They propose an LLM-based approach for learning multimodal contrast following the encoding-decoding paradigm . they use a memory module with reinforced contrast recognition to enhance learning .
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