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Found 24 papers, 2 papers with code
Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers
Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences.
On the Hardness of Learning to Stabilize Linear Systems
Inspired by the work of Tsiamis et al. \cite{tsiamis2022learning}, in this paper we study the statistical hardness of learning to stabilize linear time-invariant systems.
Inferring Relational Potentials in Interacting Systems
In this work, we propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions that enables greater flexibility in trajectory modeling: it discovers a set of relational potentials, represented as energy functions, which when minimized reconstruct the original trajectory.
Cross-view Action Recognition via Contrastive View-invariant Representation
Cross view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint.
SAIF: Sparse Adversarial and Imperceptible Attack Framework
Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal.
Self-Supervised Decomposition, Disentanglement and Prediction of Video Sequences while Interpreting Dynamics: A Koopman Perspective
Human interpretation of the world encompasses the use of symbols to categorize sensory inputs and compose them in a hierarchical manner.
Peak Estimation and Recovery with Occupation Measures
Peak Estimation aims to find the maximum value of a state function achieved by a dynamical system.
Systems and Control Systems and Control Algebraic Geometry Dynamical Systems 37M99
Key Frame Proposal Network for Efficient Pose Estimation in Videos
Human pose estimation in video relies on local information by either estimating each frame independently or tracking poses across frames.
Solving Interpretable Kernel Dimensionality Reduction
While KDR methods can be easily solved by keeping the most dominant eigenvectors of the kernel matrix, its features are no longer easy to interpret.
Iterative Spectral Method for Alternative Clustering
Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition.
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion
The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence measure that has applications in various aspects of machine learning.
Solving Interpretable Kernel Dimension Reduction
While KDR methods can be easily solved by keeping the most dominant eigenvectors of the kernel matrix, its features are no longer easy to interpret.
SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms
This paper addresses the problem of subspace clustering in the presence of outliers.
DYAN: A Dynamical Atoms-Based Network for Video Prediction
The ability to anticipate the future is essential when making real time critical decisions, provides valuable information to understand dynamic natural scenes, and can help unsupervised video representation learning.
MoNet: Moments Embedding Network
In addition, we propose a novel sub-matrix square-root layer, which can be used to normalize the output of the convolution layer directly and mitigate the dimensionality problem with off-the-shelf compact pooling methods.
Dynamics Enhanced Multi-Camera Motion Segmentation From Unsynchronized Videos
This paper considers the multi-camera motion segmentation problem using unsynchronized videos.
Multi-camera Multi-Object Tracking
For multi-camera system tracking problem, efficient data association across cameras, and at the same time, across frames becomes more important than single-camera system tracking.
Solving Temporal Puzzles
Many physical phenomena, within short time windows, can be explained by low order differential relations.
Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold
In this paper we propose a new framework to compare and classify temporal sequences.
Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming
This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers.
Person Re-identification in Appearance Impaired Scenarios
Our experiments on the original and the appearance impaired datasets demonstrate the benefits of incorporating dynamics-based information with appearance-based information to re-identification algorithms.
A Convex Optimization Approach to Robust Fundamental Matrix Estimation
This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers.
Self Scaled Regularized Robust Regression
Linear Robust Regression (LRR) seeks to find the parameters of a linear mapping from noisy data corrupted from outliers, such that the number of inliers (i. e. pairs of points where the fitting error of the model is less than a given bound) is maximized.
Robust Anomaly Detection Using Semidefinite Programming
This paper presents a new approach, based on polynomial optimization and the method of moments, to the problem of anomaly detection.
