Open AccessGuidance Cleaning Network for Sketch-Based 3D Shape Retrieval
Author(s) -
Qi Liu,
Shengjie Zhao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1961/1/012072
Subject(s) - sketch , computer science , weighting , ranking (information retrieval) , flexibility (engineering) , focus (optics) , regularization (linguistics) , artificial intelligence , noise (video) , information retrieval , data mining , image (mathematics) , algorithm , mathematics , medicine , statistics , physics , optics , radiology
The sketch-based query for 3D shapes has drawn growing attention from the academic community due to its flexibility and accessibility. However, most recent works focus on reducing the cross-modality discrepancy between 2D sketches and 3D shapes, which neglect the influence caused by noise information in some low-quality free-hand sketches. To address this issue, we proposed a novel network to decrease the impact in two ways: 1) an attention weighting module to detect the noisy samples by a self-attention mechanism; 2) a data cleaning module to clear up low-quality sketches according to a ranking regularization. Experiments on two widely-used datasets demonstrate the effectiveness of our method.