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Author (up) Tao Wu; Kai Wang; Chuanming Tang; Jianlin Zhang edit  url
openurl 
  Title Diffusion-based network for unsupervised landmark detection Type Journal Article
  Year 2024 Publication Knowledge-Based Systems Abbreviated Journal  
  Volume 292 Issue Pages 111627  
  Keywords  
  Abstract Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience a notable decline in accuracy. To address these drawbacks, we propose a novel diffusion-based network (DBN) for unsupervised landmark detection, which leverages the generation ability of the diffusion models to detect the landmark locations. In particular, we introduce a dual-branch encoder (DualE) for extracting visual features and predicting landmarks. Additionally, we lighten the decoder structure for faster inference, referred to as LightD. By this means, we avoid relying on extensive data comparison and the necessity of designing complex architectures as in previous methods. Experiments on CelebA, AFLW, 300W and Deepfashion benchmarks have shown that DBN performs state-of-the-art compared to the existing methods. Furthermore, DBN shows robustness even when faced with limited data cases.  
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  Notes LAMP Approved no  
  Call Number Admin @ si @ WWT2024 Serial 4024  
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