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Found 9 papers, 4 papers with code
MetaVD: A Meta Video Dataset for enhancing human action recognition datasets
To realize this solution, we constructed a meta video dataset from the existing datasets for human action recognition, referred to as MetaVD.
Learning Decorrelated Representations Efficiently Using Fast Fourier Transform
Barlow Twins and VICReg are self-supervised representation learning models that use regularizers to decorrelate features.
A Fast and Easy Regression Technique for k-NN Classification Without Using Negative Pairs
This paper proposes an inexpensive way to learn an effective dissimilarity function to be used for $k$-nearest neighbor ($k$-NN) classification.
STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset
In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention.
Ridge Regression, Hubness, and Zero-Shot Learning
This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space.
Video Caption Dataset for Describing Human Actions in Japanese
Each caption in our dataset describes a video in the form of "who does what and where."
Action Class Relation Detection and Classification Across Multiple Video Datasets
The Meta Video Dataset (MetaVD) provides annotated relations between action classes in major datasets for human action recognition in videos.
Rethinking Loss Functions for Fact Verification
We explore loss functions for fact verification in the FEVER shared task.
