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Found 10 papers, 5 papers with code
Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input
We observe the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue.
Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography
Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations.
Towards a Comprehensive Solution for a Vision-based Digitized Neurological Examination
In this paper, we propose an accessible vision-based exam and documentation solution called Digitized Neurological Examination (DNE) to expand exam biomarker recording options and clinical applications using a smartphone/tablet.
An Adversarial Learning Based Approach for Unknown View Tomographic Reconstruction
The goal of 2D tomographic reconstruction is to recover an image given its projections from various views.
Joint Learning of Portrait Intrinsic Decomposition and Relighting
To solve this ill-posed problem from single image, state-of-the-art methods in shape from shading mostly resort to supervised training on all the components on either synthetic or real datasets.
DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization
The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content.
MSR-GAN: Multi-Segment Reconstruction via Adversarial Learning
We formulate MSR as a distribution matching problem where the goal is to recover the signal and the probability distribution of the segments such that the distribution of the generated measurements following a known forward model is close to the real observations.
UVTomo-GAN: An adversarial learning based approach for unknown view X-ray tomographic reconstruction
To accommodate this, we use Gumbel-softmax approximation of samples from categorical distribution to approximate the generator's loss as a function of the unknown image and the projection distribution.
Geometric Invariants for Sparse Unknown View Tomography
For a point source model, we show that these features reveal geometric information about the model such as the radial and pairwise distances.
Multi-Segment Reconstruction Using Invariant Features
Instead of trying to locate the segment within the sequence through pair-wise matching, we propose a new approach that uses shift-invariant features to estimate both the underlying signal and the distribution of the positions of the segments.
Signal Processing
