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Found 5 papers, 3 papers with code
Adversarial Augmentation Training Makes Action Recognition Models More Robust to Realistic Video Distribution Shifts
More precisely, we created dataset splits of HMDB-51 or UCF-101 for training, and Kinetics-400 for testing, using the subset of the classes that are overlapping in both train and test datasets.
An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition
In practice, a given video can contain multiple valid positive annotations for the same action.
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition
We address the problem of capturing temporal information for video classification in 2D networks, without increasing their computational cost.
A New Split for Evaluating True Zero-Shot Action Recognition
We benchmark several recent approaches on the proposed True Zero-Shot(TruZe) Split for UCF101 and HMDB51, with zero-shot and generalized zero-shot evaluation.
Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning
This paper presents an approach for recognizing human activities from extreme low resolution (e. g., 16x12) videos.
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