# Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention Source: [Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:41:00.437Z Authors: Ying Liu, Ge Bai, Chenji Lu, Shilong Li, Zhang Zhang, Ruifang Liu, Wenbin Guo Published: 2024-10-14T06:09:16.000Z ## Abstract Despite the remarkable advancements in Visual Question Answering (VQA), the challenge of mitigating the language bias introduced by textual information remains unresolved. Previous approaches capture language bias from a coarse-grained perspective. However, the finer-grained information within a sentence, such as context and keywords, can result in different biases. Due to the ignorance of fine-grained information, most existing methods fail to sufficiently capture language bias. In this paper, we propose a novel causal intervention training scheme named CIBi to eliminate language bias from a finer-grained perspective. Specifically, we divide the language bias into context bias and keyword bias. We employ causal intervention and contrastive learning to eliminate context bias and improve the multi-modal representation. Additionally, we design a new question-only branch based on counterfactual generation to distill and eliminate keyword bias. Experimental results illustrate that CIBi is applicable to various VQA models, yielding competitive performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()