Dynamic Cross-Modal Alignment for Robust Semantic Location Prediction

TL;DR

The paper tackles semantic location prediction from multimodal social media posts, addressing contextual ambiguity and modality discrepancy. It introduces CoVLA, which combines a Contextual Alignment Module (CAM) for cross-modal alignment guided by context graphs and a Cross-modal Fusion Module (CMF) for dynamic, context-aware fusion, trained with a hybrid loss that includes knowledge distillation from pretrained LVLMs: $\\mathcal{L} = \\\mathcal{L}_{CE} + \\\lambda \\\mathcal{L}_{KD}$ where $\\mathcal{L}_{KD} = \\\|h_F - h_{pretrained}\\|^2$. Empirical results on a 10,000-post benchmark show CoVLA achieving accuracy and F1-score improvements of 2.3 and 2.5 percentage points over strong baselines, with ablations confirming CAM and CMF contributions and human evaluations confirming contextual relevance. The work demonstrates robust, task-tailored adaptation of LVLMs for multimodal semantic location prediction, enabling more reliable location-aware services in real-world, noisy data scenarios.

Abstract

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.