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Found 7 papers, 3 papers with code
GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control
We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories.
EA-RAS: Towards Efficient and Accurate End-to-End Reconstruction of Anatomical Skeleton
In this work, we also develop a progressive training strategy and integrated it with an enhanced optimization process, enabling the network to obtain initial weights using only a small skin dataset and achieve self-supervision in skeleton reconstruction.
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices.
Unsupervised Lesion Detection via Image Restoration with a Normative Prior
In this work, we approach unsupervised lesion detection as an image restoration problem and propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation.
Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach
In this work, we take the advantage of additional geometric information from synthetic data, a powerful yet largely neglected cue, to bridge the domain gap.
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
In this paper, we explore the feasibility of using state-of-the-art auto-encoder-based deep generative models, such as variational and adversarial auto-encoders, for one such task: abnormality detection in medical imaging.
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task.
