Similarity Guided Multimodal Fusion Transformer for Semantic Location Prediction in Social Media

TL;DR

This paper introduces a novel multimodal fusion transformer that leverages similarity guidance to improve semantic location prediction from noisy social media posts containing text and images.

Contribution

It proposes a similarity-guided interaction module and a fusion mechanism that effectively reduce noise and modality heterogeneity in multimodal social media data.

Findings

Outperforms existing methods in semantic location prediction tasks.

Effectively reduces noise and heterogeneity in multimodal data.

Demonstrates superior accuracy through comprehensive experiments.

Abstract

Semantic location prediction aims to derive meaningful location insights from multimodal social media posts, offering a more contextual understanding of daily activities than using GPS coordinates. This task faces significant challenges due to the noise and modality heterogeneity in "text-image" posts. Existing methods are generally constrained by inadequate feature representations and modal interaction, struggling to effectively reduce noise and modality heterogeneity. To address these challenges, we propose a Similarity-Guided Multimodal Fusion Transformer (SG-MFT) for predicting the semantic locations of users from their multimodal posts. First, we incorporate high-quality text and image representations by utilizing a pre-trained large vision-language model. Then, we devise a Similarity-Guided Interaction Module (SIM) to alleviate modality heterogeneity and noise interference by incorporating both coarse-grained and fine-grained similarity guidance for improving modality interactions. Specifically, we propose a novel similarity-aware feature interpolation attention mechanism at the coarse-grained level, leveraging modality-wise similarity to mitigate heterogeneity and reduce noise within each modality. At the fine-grained level, we utilize a similarity-aware feed-forward block and element-wise similarity to further address the issue of modality heterogeneity. Finally, building upon pre-processed features with minimal noise and modal interference, we devise a Similarity-aware Fusion Module (SFM) to fuse two modalities with a cross-attention mechanism. Comprehensive experimental results clearly demonstrate the superior performance of our proposed method.