# Causal Debiasing for Visual Commonsense Reasoning

arXiv: https://arxiv.org/abs/2510.20281 · HTML: https://mlsanity.com/paper/2510.20281/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 15 of 15)

- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [Unifying Vision-and-Language Tasks via Text Generation](https://mlsanity.com/paper/2102.02779/) (2021)
- [ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts](https://mlsanity.com/paper/2312.00784/) (2023)
- [Two Causal Principles for Improving Visual Dialog](https://mlsanity.com/paper/1911.10496/) (2019)
- [Backdoor Defense via Deconfounded Representation Learning](https://mlsanity.com/paper/2303.06818/) (2023)
- [VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering](https://mlsanity.com/paper/2205.11501/) (2022)
- [Multimodal Integration of Human-Like Attention in Visual Question Answering](https://mlsanity.com/paper/2109.13139/) (2021)
- [GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement](https://mlsanity.com/paper/2208.08965/) (2022)
- [SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning](https://mlsanity.com/paper/2112.08587/) (2021)
- [RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training](https://mlsanity.com/paper/2202.08595/) (2022)
- [Heterogeneous Graph Learning for Visual Commonsense Reasoning](https://mlsanity.com/paper/1910.11475/) (2019)
- [Joint Answering and Explanation for Visual Commonsense Reasoning](https://mlsanity.com/paper/2202.12626/) (2022)
- [Generating Natural Questions from Images for Multimodal Assistants](https://mlsanity.com/paper/2012.03678/) (2020)
- [Learning to Agree on Vision Attention for Visual Commonsense Reasoning](https://mlsanity.com/paper/2302.02117/) (2023)
- [EventLens: Leveraging Event-Aware Pretraining and Cross-modal Linking Enhances Visual Commonsense Reasoning](https://mlsanity.com/paper/2404.13847/) (2024)

## Abstract (arXiv)

Visual Commonsense Reasoning (VCR) refers to answering questions and providing explanations based on images. While existing methods achieve high prediction accuracy, they often overlook bias in datasets and lack debiasing strategies. In this paper, our analysis reveals co-occurrence and statistical biases in both textual and visual data. We introduce the VCR-OOD datasets, comprising VCR-OOD-QA and VCR-OOD-VA subsets, which are designed to evaluate the generalization capabilities of models across two modalities. Furthermore, we analyze the causal graphs and prediction shortcuts in VCR and adopt a backdoor adjustment method to remove bias. Specifically, we create a dictionary based on the set of correct answers to eliminate prediction shortcuts. Experiments demonstrate the effectiveness of our debiasing method across different datasets.