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ProAlignNet: Unsupervised Learning for Progressively Aligning Noisy Contours
Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned.
ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours
Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned.
Wavelets to the Rescue: Improving Sample Quality of Latent Variable Deep Generative Models
Variational Autoencoders (VAE) are probabilistic deep generative models underpinned by elegant theory, stable training processes, and meaningful manifold representations.
High-Frequency guided Curriculum Learning for Class-specific Object Boundary Detection
In this work, we devise a curriculum-learning-based training process for object boundary detection.
