Search Results for author:
Found 4 papers, 1 papers with code
Learning Discriminative Spatio-temporal Representations for Semi-supervised Action Recognition
Semi-supervised action recognition aims to improve spatio-temporal reasoning ability with a few labeled data in conjunction with a large amount of unlabeled data.
Boosting Semi-Supervised Temporal Action Localization by Learning from Non-Target Classes
To this end, we first devise innovative strategies to adaptively select high-quality positive and negative classes from the label space, by modeling both the confidence and rank of a class in relation to those of the target class.
Learning from Noisy Pseudo Labels for Semi-Supervised Temporal Action Localization
To this end, we propose a unified framework, termed Noisy Pseudo-Label Learning, to handle both location biases and category errors.
Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization
The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e. g., context and background, in an untrimmed video.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
