Multi-Scale Attention Constraint Network for Fine-Grained Visual Classification

Abstract

Capturing subtle yet discriminative features constitutes a great challenge in fine-grained visual classification due to the large intra-class and small inter-class variances. Main-stream works for this problem localize at attention mechanism and feature relationship learning. However, existing methods treat the features in isolation while neglecting the effect of attention-enhanced features on relationships between different network layers. In this paper, we propose a novel attention-based method by Multi-Scale Attention Constraint network composed of two important components: (1) a feature extractor with lightweight group-wise enhanced attention blocks that guides the generation of high representation features; and (2) a multi-scale regularizer that explores the relationships between different features. Extensive experiments show that our approach achieves state-of-the-art performance on standard benchmark datasets. Moreover, we introduce a new dataset, consisting of comprehensive surgical instrument categories based on three common surgeries, to support the classification and inventory work of surgical instruments.