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Found 14 papers, 2 papers with code
Machine Learning to Estimate Gross Loss of Jewelry for Wax Patterns
In mass manufacturing of jewellery, the gross loss is estimated before manufacturing to calculate the wax weight of the pattern that would be investment casted to make multiple identical pieces of jewellery.
Motion-Augmented Self-Training for Video Recognition at Smaller Scale
With the motion model we generate pseudo-labels for a large unlabeled video collection, which enables us to transfer knowledge by learning to predict these pseudo-labels with an appearance model.
Efficient Action Recognition via Dynamic Knowledge Propagation
Efficient action recognition has become crucial to extend the success of action recognition to many real-world applications.
Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization
Temporally localizing actions in videos is one of the key components for video understanding.
ActionBytes: Learning From Trimmed Videos to Localize Actions
Different from existing works, which all use annotated untrimmed videos during training, we learn only from short trimmed videos.
TimeGate: Conditional Gating of Segments in Long-range Activities
When recognizing a long-range activity, exploring the entire video is exhaustive and computationally expensive, as it can span up to a few minutes.
Multi-Fidelity Recursive Behavior Prediction
Predicting the behavior of surrounding vehicles is a critical problem in automated driving.
Guess Where? Actor-Supervision for Spatiotemporal Action Localization
Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable.
Tubelets: Unsupervised action proposals from spatiotemporal super-voxels
First, inspired by selective search for object proposals, we introduce an approach to generate action proposals from spatiotemporal super-voxels in an unsupervised manner, we call them Tubelets.
VideoLSTM Convolves, Attends and Flows for Action Recognition
We present a new architecture for end-to-end sequence learning of actions in video, we call VideoLSTM.
Objects2action: Classifying and localizing actions without any video example
Our key contribution is objects2action, a semantic word embedding that is spanned by a skip-gram model of thousands of object categories.
Ranked #17 on
Zero-Shot Action Recognition
on UCF101
What do 15,000 Object Categories Tell Us About Classifying and Localizing Actions?
This paper contributes to automatic classification and localization of human actions in video.
Action Localization with Tubelets from Motion
Our approach significantly outperforms the state-of-the-art on both datasets, while restricting the search of actions to a fraction of possible bounding box sequences.
Better Exploiting Motion for Better Action Recognition
Several recent works on action recognition have attested the importance of explicitly integrating motion characteristics in the video description.
