Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval

Fine-grained fashion retrieval searches for items that share a similar attribute with the query image.Most existing methods use a pre-trained feature extractor (e.g., ResNet 50) to capture image representations.However, a pre-trained feature backbone is typically trained for image classification and object detection, which are fundamentally different tasks from fine-grained fashion retrieval.Therefore, existing methods suffer from a feature gap problem when directly using the pre-trained backbone for fine-tuning.To solve this problem, we introduce an attribute-guided multi-level attention network (AG-MAN).Specifically, we first enhance the pre-trained feature extractor to capture multi-level image embedding, thereby enriching the low-level features within these representations.Then, we propose a classification scheme where images with the same attribute, albeit with different values, are categorized into the same class.This can further alleviate the feature gap problem by perturbing objectcentric feature learning.Moreover, we propose an improved attribute-guided attention module for extracting more accurate attribute-specific representations.Our model consistently outperforms existing attention based methods when assessed on the FashionAI (62.8788% in MAP), DeepFashion (8.9804% in MAP), and Zappos50k datasets (93.32% in Prediction accuracy).Especially, ours improves the most typical ASENet V2 model by 2.12%, 0.31%, and 0.78% points in FashionAI, DeepFashion, and Zappos50k datasets, respectively.