# Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering

arXiv: https://arxiv.org/abs/2006.09073 · HTML: https://mlsanity.com/paper/2006.09073/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 30 of 51)

- [An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA](https://mlsanity.com/paper/2109.05014/) (2021)
- [PromptCap: Prompt-Guided Task-Aware Image Captioning](https://mlsanity.com/paper/2211.09699/) (2022)
- [Retrieval Augmented Visual Question Answering with Outside Knowledge](https://mlsanity.com/paper/2210.03809/) (2022)
- [Multi-Modal Answer Validation for Knowledge-Based VQA](https://mlsanity.com/paper/2103.12248/) (2021)
- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [EchoSight: Advancing Visual-Language Models with Wiki Knowledge](https://mlsanity.com/paper/2407.12735/) (2024)
- [REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering](https://mlsanity.com/paper/2206.01201/) (2022)
- [See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning](https://mlsanity.com/paper/2301.05226/) (2023)
- [Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering](https://mlsanity.com/paper/2109.04014/) (2021)
- [Zero-shot Visual Question Answering using Knowledge Graph](https://mlsanity.com/paper/2107.05348/) (2021)
- [MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2203.09138/) (2022)
- [Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering](https://mlsanity.com/paper/2109.08029/) (2021)
- [Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2303.01903/) (2023)
- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem](https://mlsanity.com/paper/2207.11850/) (2022)
- [LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection](https://mlsanity.com/paper/2207.12888/) (2022)
- [SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning](https://mlsanity.com/paper/2112.08587/) (2021)
- [Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2204.10448/) (2022)
- [VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges](https://mlsanity.com/paper/2212.13296/) (2022)
- [DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation](https://mlsanity.com/paper/2205.12617/) (2022)
- [A survey on knowledge-enhanced multimodal learning](https://mlsanity.com/paper/2211.12328/) (2022)
- [X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering](https://mlsanity.com/paper/2107.11576/) (2021)
- [Graph Neural Networks in Vision-Language Image Understanding: A Survey](https://mlsanity.com/paper/2303.03761/) (2023)
- [Combo of Thinking and Observing for Outside-Knowledge VQA](https://mlsanity.com/paper/2305.06407/) (2023)
- [Rationale-guided Prompting for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2412.16936/) (2024)
- [Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2203.02985/) (2022)
- [Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering](https://mlsanity.com/paper/2210.10176/) (2022)
- [Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA](https://mlsanity.com/paper/2310.09147/) (2023)
- [Multimodal Reranking for Knowledge-Intensive Visual Question Answering](https://mlsanity.com/paper/2407.12277/) (2024)
- [Semantic-aware Modular Capsule Routing for Visual Question Answering](https://mlsanity.com/paper/2207.10404/) (2022)

## Abstract (arXiv)

Fact-based Visual Question Answering (FVQA) requires external knowledge beyond visible content to answer questions about an image, which is challenging but indispensable to achieve general VQA. One limitation of existing FVQA solutions is that they jointly embed all kinds of information without fine-grained selection, which introduces unexpected noises for reasoning the final answer. How to capture the question-oriented and information-complementary evidence remains a key challenge to solve the problem. In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features. On top of the multi-layer graph representations, we propose a modality-aware heterogeneous graph convolutional network to capture evidence from different layers that is most relevant to the given question. Specifically, the intra-modal graph convolution selects evidence from each modality and cross-modal graph convolution aggregates relevant information across different modalities. By stacking this process multiple times, our model performs iterative reasoning and predicts the optimal answer by analyzing all question-oriented evidence. We achieve a new state-of-the-art performance on the FVQA task and demonstrate the effectiveness and interpretability of our model with extensive experiments.