5 accepted papers
Source Free Unsupervised Domain Adaptation (SFUDA) is critical for deploying deep learning models across diverse clinical settings. However, existing methods are typically designed for low-gap, specific domain shifts and cannot generalize into a unified, multi-modalities, and multi-target framework,
Cross-Domain Few-Shot Learning has witnessed great stride with the development of meta-learning. However, most existing methods pay more attention to learning domain-adaptive inductive bias (meta-knowledge) through feature-wise manipulation or task diversity improvement while neglecting the phenomen…
In this paper, we study the property of frequency shift in two-dimensional Fractional Fourier Transform (2D-FRFT) domain. Based on the mathematical verification and computer simulations, it is demonstrated that the magnitude of reconstruction from phase information satisfies frequency shift-invarian…
We present an attention-weighted loss in a photometric stereo neural network to improve 3D surface recovery accuracy in complex-structured areas, such as edges and crinkles, where existing learning-based methods often failed. Instead of using a uniform penalty for all pixels, our method employs the…
As an information fusion tool, Kernel Entropy Component Analysis (KECA) is realized by using descriptor of information entropy and optimized by entropy estimation. However, as an unsuper-vised method, it merely puts the information or features from different channels together without considering the…