Multi-granularity Representation Learning For Sketch-based Dynamic Face Image Retrieval

Abstract

In specific scenarios, face sketch can be used to identify a person. However, drawing a face sketch often requires exceptional skill and is time-consuming, limiting its widespread applications in actual scenarios. The new framework of sketch less face image retrieval (SLFIR)[1] attempts to overcome the barriers by providing a means for humans and machines to interact during the drawing process. Considering SLFIR problem, there is a large gap between a partial sketch with few strokes and any whole face photo, resulting in poor performance at the early stages. In this study, we propose a multigranularity (MG) representation learning (MGRL) method to address the SLFIR problem, in which we learn the representation of different granularity regions for a partial sketch, and then, by combining all MG regions of the sketches and images, the final distance was determined. In the experiments, our method outperformed state-of-the-art baselines in terms of early retrieval on two accessible dataset

Stats

• citations1
• S2 citations—
• github stars22
• HF likes0
• heat score4.98
• arxiv keywang2023multi

Related papers

• Sketch Less Face Image Retrieval: A New Challenge (2023)6.34
• Active Learning For Fine-grained Sketch-based Image Retrieval (2023)0.00
• Sketch Less For More: On-the-fly Fine-grained Sketch Based Image Retrieval (2020)15.28
• Deep Reinforced Attention Regression For Partial Sketch Based Image Retrieval (2021)5.24
• Adaptive Fine-grained Sketch-based Image Retrieval (2022)9.76
• More Photos Are All You Need: Semi-supervised Learning For Fine-grained Sketch Based Image Retrieval (2021)13.23
• Cross-modal Hierarchical Modelling For Fine-grained Sketch Based Image Retrieval (2020)6.77
• Data-free Sketch-based Image Retrieval (2023)13.17