COCCI: Context-Driven Clothing Classification Network

Abstract

Clothing classification serves as a fundamental task for clothing retrieval, clothing recommendation, etc. In this task, there are two inherent challenges: suppressing complex backgrounds outside the clothing region and disentangling the feature entanglement of shape-similar clothing samples. These challenges arise from insufficient attention to key distinctions of clothing, which hinders the accuracy of clothing classification. Also, the high computational resource requirement of some complex and large-scale models also decreases the inference efficiency. To tackle these challenges, we propose a new COntext-driven Clothing ClassIfication network (COCCI), which improves inference accuracy while reducing model complexity. First, we design a self-adaptive attention fusion (SAAF) module to enhance category-exclusive clothing features and prevent misclassification by suppressing ineffective features with confused image contexts. Second, we propose a novel multi-scale feature aggregation (MSFA) module to establish spatial context correlations by using multi-scale clothing features. This helps disentangle feature entanglement among shape-similar clothing samples. Finally, we introduce knowledge distillation to extract reliable teacher knowledge from complex datasets, which helps student models learn clothing features with rich representation information, thereby improving generalization while reducing model complexity. In comparison to state-of-the-art networks trained with one single model, our method achieves SOTA performance on the widely-used clothing classification benchmark.