# Multimodal Fine-grained Reasoning for Post Quality Evaluation

arXiv: https://arxiv.org/abs/2507.17934 · HTML: https://mlsanity.com/paper/2507.17934/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 19 of 19)

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- [E2AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models](https://mlsanity.com/paper/2503.04833/) (2025)
- [Adversarial Backdoor Defense in CLIP](https://mlsanity.com/paper/2409.15968/) (2024)
- [Improving prediction of students' performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources](https://mlsanity.com/paper/2403.07194/) (2024)
- [Adaptive Marginalized Semantic Hashing for Unpaired Cross-Modal Retrieval](https://mlsanity.com/paper/2207.11880/) (2022)

## Abstract (arXiv)

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.