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Paper details
Number 3 - September 2024
Volume 34 - 2024
DCF-VQA: Counterfactual structure based on multi-feature enhancement
Guan Yang, Cheng Ji, Xiaoming Liu, Ziming Zhang, Chen Wang
Abstract
Visual question answering (VQA) is a pivotal topic at the intersection of computer vision and natural language processing. This paper addresses the challenges of linguistic bias and bias fusion within invalid regions encountered in existing VQA models due to insufficient representation of multi-modal features. To overcome those issues, we propose a multi-feature enhancement scheme. This scheme involves the fusion of one or more features with the original ones, incorporating discrete cosine transform (DCT) features into the counterfactual reasoning framework. This approach harnesses finegrained information and spatial relationships within images and questions, enabling a more refined understanding of the indirect relationship between images and questions. Consequently, it effectively mitigates linguistic bias and bias fusion within invalid regions in the model. Extensive experiments are conducted on multiple datasets, including VQA2 and VQACP2, employing various baseline models and fusion techniques, resulting in promising and robust performance.
Keywords
visual question answering, multi-feature enhancement, counterfactual, discrete cosine transform