Semantic Granularity Metric Learning for Visual Search

TL;DR

This paper introduces a semantic granularity metric learning approach that leverages attribute semantic space and a novel loss function to improve visual search by capturing multiple levels of similarity.

Contribution

It proposes a new deep metric learning framework that models multiple granularities of visual similarity using attribute semantics and a specialized loss function.

Findings

Outperforms recent state-of-the-art methods by 1-4.5% in Recall@1 on DeepFashion In-Shop dataset.

Effectively captures multiple levels of visual similarity.

Demonstrates improved retrieval performance on benchmark datasets.

Abstract

Deep metric learning applied to various applications has shown promising results in identification, retrieval and recognition. Existing methods often do not consider different granularity in visual similarity. However, in many domain applications, images exhibit similarity at multiple granularities with visual semantic concepts, e.g. fashion demonstrates similarity ranging from clothing of the exact same instance to similar looks/design or a common category. Therefore, training image triplets/pairs used for metric learning inherently possess different degree of information. However, the existing methods often treats them with equal importance during training. This hinders capturing the underlying granularities in feature similarity required for effective visual search. In view of this, we propose a new deep semantic granularity metric learning (SGML) that develops a novel idea of leveraging attribute semantic space to capture different granularity of similarity, and then integrate this information into deep metric learning. The proposed method simultaneously learns image attributes and embeddings using multitask CNNs. The two tasks are not only jointly optimized but are further linked by the semantic granularity similarity mappings to leverage the correlations between the tasks. To this end, we propose a new soft-binomial deviance loss that effectively integrates the degree of information in training samples, which helps to capture visual similarity at multiple granularities. Compared to recent ensemble-based methods, our framework is conceptually elegant, computationally simple and provides better performance. We perform extensive experiments on benchmark metric learning datasets and demonstrate that our method outperforms recent state-of-the-art methods, e.g., 1-4.5\% improvement in Recall@1 over the previous state-of-the-arts [1],[2] on DeepFashion In-Shop dataset.