Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment
Peking University（北京大学）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；School of Computer Science, Peking University（北京大学计算机学院）
Ming Zhang, Ke Chang, Yunfang Wu
备注：
10 pages, 4 figures, accepted by LREC-COLING 2024(main conference, long paper)
Multi-modal semantic understanding requires integrating information from different modalities to extract users' real intention behind words. Most previous work applies a dual-encoder structure to separately encode image and text, but fails to learn cross-modal feature alignment, making it hard to achieve cross-modal deep information interaction. This paper proposes a novel CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment, which projects the features derived from different modalities into a unified deep space. On multi-modal sarcasm detection (MMSD) and multi-modal sentiment analysis (MMSA) tasks, the experimental results show that our proposed model significantly outperforms several baselines, and our feature alignment strategy brings obvious performance gain over models with different aggregating methods and models even enriched with knowledge. More importantly, our model is simple to implement without using task-specific external knowledge, and thus can easily migrate to other multi-modal tasks. Our source codes are available at https://github.com/ChangKe123/CLFA.
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising
Sun Yat-sen University（中山大学）；The Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education（机器智能与先进计算教育部重点实验室（中山大学））；JD AI Research（京东人工智能研究院）
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition
School of computer and Information Engineering, Central South University of Forestry and Technology（中南林业科技大学计算机与信息工程学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；State University of New York（纽约州立大学）
Beyond Topical Similarity: Contrastive Evidence Retrieval with Interpretable Attention Alignment in RAG
University of Chile（智利大学）；São Paulo State University（圣保罗州立大学）；Saarland University（萨尔兰州立大学）；University of Munich（慕尼黑大学）；Portland State University（波特兰州立大学）；Idiap Research Institute（Idiap研究机构）
Interactive Contrastive Learning for Self-supervised Entity Alignment
Tsinghua University（清华大学）；Beijing University of Chemical Technology（北京化工大学）；Singapore Management University（新加坡管理大学）；Information Research Center of Military Science（军事科学院信息研究中心）；China Mobile Research Institute（中国移动研究院）
