GMDA: a grouped multi-scale difference perception attention mechanism for identifying highly similar interferences

Accurate image classification remains challenging when targets share strong visual similarity with surrounding interference, as fine discriminative cues are easily obscured by coupled background and texture noise. Most mainstream attention methods rely on feature reweighting without explicitly modeling feature gaps between targets and interference, leading to unstable discrimination under coupled local-global interference and forming a core bottleneck for classification robustness. To address this limitation, we propose the Grouped Multi-Scale Difference Perception Attention mechanism (GMDA), which shifts the design paradigm from passive weight allocation to active difference amplification. GMDA consists of three synergistic core components. Group-Wise Differential Feature Extraction decouples entangled features via grouped multi-grained extraction to reduce cross-channel interference redundancy. Dual Feature Differential Perception explicitly amplifies subtle discriminative feature differences through dual-path differential operations. Multi-Scale Context Fusion integrates multi-scale contextual information to balance fine-grained local detail perception and global interference suppression. Together they form a progressive processing pipeline that suppresses redundant interference while preserving critical target characteristics, generating precise attention maps and enabling reliable distinction between targets and highly similar backgrounds. We validate GMDA on five representative datasets covering skin lesion classification, fine-grained plant disease recognition, camouflaged object categorization, industrial defect detection and natural scene classification. Extensive experiments are conducted on ResNet-50, MobileNet-V3 and Vision Transformer backbones. We compare GMDA with classic attention methods such as Coordinate Attention and Convolutional Block Attention Module (CBAM), as well as state-of-the-art variants including Coordinate Mixed Pooling Mechanism (CMM) and Adaptive Coordinate Attention (ACA), and supplement the evaluation with systematic component ablation and robustness analyses. Results show that across all test datasets, GMDA delivers an average improvement of over 3% in classification accuracy, macro-F1 and other key metrics relative to mainstream and cutting-edge attention methods, with only marginal increases in parameter count and computational overhead. This work provides an effective solution for image recognition under highly similar interference conditions, and shows promising application potential in agricultural disease detection, medical image analysis, industrial defect inspection and related fields.