A deformable CNN-based triplet model for fine-grained sketch-based image retrieval
作者：
Highlights：
• We propose a new way to generate pseudo sketches, which can be used alone as an artistic creation tool, at the preprocessing step. Experiments consistently illustrate the superior performance of the proposed method.
• We propose a novel multi-task FG-SBIR structure which takes advantage of deformable convolutional neural networks while, at the same time, taking into consideration of freehand sketch attributes to reduce the semantic gap.
• We build a new clothing fine grained sketch dataset, which has 2000 sketch-image pairs and rich semantic attribute annotations for FG-SBIR, for the first time.
摘要
•We propose a new way to generate pseudo sketches, which can be used alone as an artistic creation tool, at the preprocessing step. Experiments consistently illustrate the superior performance of the proposed method.•We propose a novel multi-task FG-SBIR structure which takes advantage of deformable convolutional neural networks while, at the same time, taking into consideration of freehand sketch attributes to reduce the semantic gap.•We build a new clothing fine grained sketch dataset, which has 2000 sketch-image pairs and rich semantic attribute annotations for FG-SBIR, for the first time.
论文关键词：Freehand sketches,FG-SBIR,Semantic attributes,Deformable CNNs,Preprocessing
论文评审过程：Received 24 November 2020, Revised 5 December 2021, Accepted 22 December 2021, Available online 26 December 2021, Version of Record 8 January 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108508
