Decoupled global-local collaborative network for visual question answering

Abstract

Visual Question Answering (VQA) aims to achieve cross-modal semantic understanding through joint modeling of visual content and natural language. Although existing attention-based approaches effectively align features, they struggle to simultaneously accommodate global semantic modeling and local fine-grained perception within single-path architectures, leading to semantic drift and underutilization of information when addressing multi-perspective reasoning tasks. To mitigate this limitation, we propose a Decoupled Global-Local Collaborative Network (DGL-Net) that structurally partitions the cross-modal reasoning process into two complementary branches: global semantic understanding and local fine-grained modeling. Specifically, the global branch employs hierarchical attention fusion to model long-range dependencies and holistic scene semantics, providing contextual constraints for reasoning. The local branch introduces semantic relation enhancement and relevant region screening to explicitly model intra-modal structural relationships, strengthening fine-grained region-word alignment by injecting structural information. During training and inference, a branch-decoupling and collaborative fusion strategy enables independent yet complementary feature learning. Extensive experimental results demonstrate accuracies of 72.15% and 57.87% on VQA-v2 and GQA benchmarks, respectively. Notably, the proposed method exhibits enhanced robustness and generalization in complex semantic and compositional reasoning tasks, validating the efficacy of the decoupled collaborative strategy.