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Found 10 papers, 3 papers with code
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning
Specifically, we outperform the state of the art by 7% on UCF and 4% on HMDB for video retrieval, and 5% on UCF and 6% on HMDB for video classification
Transformers in Action: Weakly Supervised Action Segmentation
The video action segmentation task is regularly explored under weaker forms of supervision, such as transcript supervision, where a list of actions is easier to obtain than dense frame-wise labels.
Learning to Align Sequential Actions in the Wild
To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions.
Learning by Aligning Videos in Time
To overcome this problem, we propose a temporal regularization term (i. e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space.
Direct and indirect transactions and requirements
The indirect transactions between sectors of an economic system has been a long-standing open problem.
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
We present a sampling-free approach for computing the epistemic uncertainty of a neural network.
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition
The lack of large-scale real datasets with annotations makes transfer learning a necessity for video activity understanding.
Human Motion Analysis with Deep Metric Learning
Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks.
Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization.
Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization.
