Dynamic Cross-Modal Alignment for Robust Semantic Location Prediction
This work addresses semantic location prediction for applications in personalized services and human mobility analysis, representing an incremental advancement with specific performance gains.
The paper tackled the problem of semantic location prediction from multimodal social media posts by introducing CoVLA, a framework that addresses contextual ambiguity and modality discrepancy, resulting in improvements of 2.3% in accuracy and 2.5% in F1-score over state-of-the-art methods.
Semantic location prediction from multimodal social media posts is a critical task with applications in personalized services and human mobility analysis. This paper introduces \textit{Contextualized Vision-Language Alignment (CoVLA)}, a discriminative framework designed to address the challenges of contextual ambiguity and modality discrepancy inherent in this task. CoVLA leverages a Contextual Alignment Module (CAM) to enhance cross-modal feature alignment and a Cross-modal Fusion Module (CMF) to dynamically integrate textual and visual information. Extensive experiments on a benchmark dataset demonstrate that CoVLA significantly outperforms state-of-the-art methods, achieving improvements of 2.3\% in accuracy and 2.5\% in F1-score. Ablation studies validate the contributions of CAM and CMF, while human evaluations highlight the contextual relevance of the predictions. Additionally, robustness analysis shows that CoVLA maintains high performance under noisy conditions, making it a reliable solution for real-world applications. These results underscore the potential of CoVLA in advancing semantic location prediction research.