Abstract
In this paper, we tackle one-shot texture retrieval:
given an example of a new reference texture, detect and segment all the pixels of the same texture
category within an arbitrary image. To address this
problem, we present an OS-TR network to encode
both reference and query image, leading to achieve
texture segmentation towards the reference category. Unlike the existing texture encoding methods
that integrate CNN with orderless pooling, we propose a directionality-aware module to capture the
texture variations at each direction, resulting in spatially invariant representation. To segment new categories given only few examples, we incorporate a
self-gating mechanism into relation network to exploit global context information for adjusting perchannel modulation weights of local relation features. Extensive experiments on benchmark texture
datasets and real scenarios demonstrate the abovepar segmentation performance and robust generalization across domains of our proposed method