One-Shot Texture Retrieval Using Global Grouping Metric

Abstract

Texture retrieval is widely used in the fields of fashion and e-commerce. This paper presents the problem of one-shot texture retrieval: given an example of a new reference texture, we aim to detect and segment all pixels of the same texture category within an arbitrary image. To address this problem, an OS-TR network is proposed to encode both reference and query images into a texture representation space, and a better comparison is made based on the global grouping information. Because the learned texture representation should be invariant to the spatial layout while preserving the rough semantic concepts, we introduce an adaptive directionality-aware module to finely discriminate the orderless texture details. To make full use of the global context information given only a few examples, we incorporate a grouping-attention mechanism into the relation network, resulting in the per-channel modulation of the local relation features. Extensive experiments on two benchmark datasets (i.e., the DTD and ADE20K dataset) and real scenarios demonstrate that our proposed method can achieve above-par segmentation performance and robust generalization across domains.