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Found 6 papers, 6 papers with code
Object-Centric Video Prediction via Decoupling of Object Dynamics and Interactions
We propose a novel framework for the task of object-centric video prediction, i. e., extracting the compositional structure of a video sequence, as well as modeling objects dynamics and interactions from visual observations in order to predict the future object states, from which we can then generate subsequent video frames.
MSPred: Video Prediction at Multiple Spatio-Temporal Scales with Hierarchical Recurrent Networks
In our experiments, we demonstrate that MSPred accurately predicts future video frames as well as high-level representations (e. g. keypoints or semantics) on bin-picking and action recognition datasets, while consistently outperforming popular approaches for future frame prediction.
Ranked #1 on
Video Prediction
on KTH
(LPIPS metric)
Unsupervised Image Decomposition with Phase-Correlation Networks
The ability to decompose scenes into their object components is a desired property for autonomous agents, allowing them to reason and act in their surroundings.
Deep learning architectural designs for super-resolution of noisy images
Recent advances in deep learning have led to significant improvements in single image super-resolution (SR) research.
Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning
(2) To improve the already strong results further, we created a small dataset (ClassArch) consisting of ancient Greek vase paintings from the 6-5th century BCE with person and pose annotations.
Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement
This method is inspired by the observation that, in the scattering transform domain, the subspaces formed by the eigenvectors corresponding to the few largest eigenvalues of the data matrices of individual classes are nearly shared among different classes.
Ranked #4 on
Image Clustering
on MNIST-test
