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Found 5 papers, 2 papers with code
Adaptive Margin Diversity Regularizer for handling Data Imbalance in Zero-Shot SBIR
Extensive experiments and analysis justifies the effectiveness of the proposed AMDReg for mitigating the effect of data imbalance for generalization to unseen classes in ZS-SBIR.
Test-time Training for Data-efficient UCDR
Image retrieval under generalized test scenarios has gained significant momentum in literature, and the recently proposed protocol of Universal Cross-domain Retrieval is a pioneer in this direction.
Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains
Towards that goal, we propose SnMpNet (Semantic Neighbourhood and Mixture Prediction Network), which incorporates two novel losses to account for the unseen classes and domains encountered during testing.
SML: Semantic Meta-learning for Few-shot Semantic Segmentation
In addition, we propose to use the well established technique, ridge regression, to not only bring in the class-level semantic information, but also to effectively utilise the information available from multiple images present in the training data for prototype computation.
Unified Generator-Classifier for Efficient Zero-Shot Learning
Generative models have achieved state-of-the-art performance for the zero-shot learning problem, but they require re-training the classifier every time a new object category is encountered.
