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Found 3 papers, 1 papers with code
Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance
We compared the model performances among our approach, baseline approach, and 3 alternative approaches to leverage semantic features.
Visual Representation Learning with Self-Supervised Attention for Low-Label High-data Regime
For few-shot image classification we train SSL-ViTs without any supervision, on external data, and use this trained embedder to adapt quickly to novel classes with limited number of labels.
A weakly supervised adaptive triplet loss for deep metric learning
We address the problem of distance metric learning in visual similarity search, defined as learning an image embedding model which projects images into Euclidean space where semantically and visually similar images are closer and dissimilar images are further from one another.
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