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 …

Text-conditioned image generation models often generate incorrect associations between
entities and their visual attributes. This reflects an impaired map** between linguistic …

We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-
Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by …

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic
knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute …

Deep learning models usually suffer from the domain shift issue, where models trained on
one source domain do not generalize well to other unseen domains. In this work, we …

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general
understanding of the world. Such an agent would require the ability to continually …

Despite the success of deep neural networks, there are still many challenges in deep
representation learning due to the data scarcity issues such as data imbalance, unseen …

In compositional zero-shot learning, the goal is to recognize unseen compositions (eg old
dog) of observed visual primitives states (eg old, cute) and objects (eg car, dog) in the …

Abstract Three-dimensional (3D) cross-modality cardiac image segmentation is critical for
cardiac disease diagnosis and treatment. However, it confronts the challenge of modality …

Abstract Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions
formed from seen state and object during training. Since the same state may be various in …