2 citations · 3 across the 6 of their papers we have counts for
8 papers
GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts
Zoltán Á. Milacski, Koichiro Niinuma, Ryosuke Kawamura +2
The connection between our 3D surroundings and the descriptive language that characterizes them would be well-suited for localizing and generating human motion in context but for o…
CoGS: Controllable Gaussian Splatting
Heng Yu, Joel Julin, Zoltán Á. Milacski +2
Capturing and re-animating the 3D structure of articulated objects present significant barriers. On one hand, methods requiring extensively calibrated multi-view setups are prohibi…
Unsupervised Style-based Explicit 3D Face Reconstruction from Single Image
Heng Yu, Zoltan A. Milacski, Laszlo A. Jeni
Inferring 3D object structures from a single image is an ill-posed task due to depth ambiguity and occlusion. Typical resolutions in the literature include leveraging 2D or 3D grou…
DyLiN: Making Light Field Networks Dynamic
Heng Yu, Joel Julin, Zoltan A. Milacski +2
Light Field Networks, the re-formulations of radiance fields to oriented rays, are magnitudes faster than their coordinate network counterparts, and provide higher fidelity with re…
Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness
Dávid Szeghy, Mahmoud Aslan, Áron Fóthi +3
While deep neural networks are sensitive to adversarial noise, sparse coding using the Basis Pursuit (BP) method is robust against such attacks, including its multi-layer extension…
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction
Changhee Han, Leonardo Rundo, Kohei Murao +7
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstru…
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis
Changhee Han, Leonardo Rundo, Kohei Murao +5
Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a…
Cognitive Deep Machine Can Train Itself
András Lőrincz, Máté Csákvári, Áron Fóthi +3
Machine learning is making substantial progress in diverse applications. The success is mostly due to advances in deep learning. However, deep learning can make mistakes and its ge…