A review of generalized zero-shot learning methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …
under the condition that some output classes are unknown during supervised learning. To …
A closer look at self-training for zero-label semantic segmentation
Being able to segment unseen classes not observed during training is an important
technical challenge in deep learning, because of its potential to reduce the expensive …
technical challenge in deep learning, because of its potential to reduce the expensive …
Zero-shot learning on 3d point cloud objects and beyond
Zero-shot learning, the task of learning to recognize new classes not seen during training,
has received considerable attention in the case of 2D image classification. However, despite …
has received considerable attention in the case of 2D image classification. However, despite …
Discriminative and robust attribute alignment for zero-shot learning
Zero-shot learning (ZSL) aims to learn models that can recognize images of semantically
related unseen categories, through transferring attribute-based knowledge learned from …
related unseen categories, through transferring attribute-based knowledge learned from …
Generalized zero-shot learning with multiple graph adaptive generative networks
Generative adversarial networks (GANs) for (generalized) zero-shot learning (ZSL) aim to
generate unseen image features when conditioned on unseen class embeddings, each of …
generate unseen image features when conditioned on unseen class embeddings, each of …