Multimodal Fine-grained Reasoning for Post Quality Evaluation

• URL: http://arxiv.org/abs/2507.17934v1
• Date: Mon, 21 Jul 2025 04:30:50 GMT
• Title: Multimodal Fine-grained Reasoning for Post Quality Evaluation
• Authors: Xiaoxu Guo, Siyan Liang, Yachao Cui, Juxiang Zhou, Lei Wang, Han Cao,
• Abstract summary: We propose the Multimodal Fine-grained Topic-post Reasoning (MFTRR) framework, which mimics human cognitive processes.<n>MFTRR reframes post-quality assessment as a ranking task and incorporates multimodal data to better capture quality variations.
• Score: 1.806315356676339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately assessing post quality requires complex relational reasoning to capture nuanced topic-post relationships. However, existing studies face three major limitations: (1) treating the task as unimodal categorization, which fails to leverage multimodal cues and fine-grained quality distinctions; (2) introducing noise during deep multimodal fusion, leading to misleading signals; and (3) lacking the ability to capture complex semantic relationships like relevance and comprehensiveness. To address these issues, we propose the Multimodal Fine-grained Topic-post Relational Reasoning (MFTRR) framework, which mimics human cognitive processes. MFTRR reframes post-quality assessment as a ranking task and incorporates multimodal data to better capture quality variations. It consists of two key modules: (1) the Local-Global Semantic Correlation Reasoning Module, which models fine-grained semantic interactions between posts and topics at both local and global levels, enhanced by a maximum information fusion mechanism to suppress noise; and (2) the Multi-Level Evidential Relational Reasoning Module, which explores macro- and micro-level relational cues to strengthen evidence-based reasoning. We evaluate MFTRR on three newly constructed multimodal topic-post datasets and the public Lazada-Home dataset. Experimental results demonstrate that MFTRR significantly outperforms state-of-the-art baselines, achieving up to 9.52% NDCG@3 improvement over the best unimodal method on the Art History dataset.

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