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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 …
Decision-making driven by driver intelligence and environment reasoning for high-level autonomous vehicles: a survey
Autonomous vehicle (AV) is expected to reshape the future transportation system, and its
decision-making is one of the most critical modules. Many current decision-making modules …
decision-making is one of the most critical modules. Many current decision-making modules …
Linguistic binding in diffusion models: Enhancing attribute correspondence through attention map alignment
Text-conditioned image generation models often generate incorrect associations between
entities and their visual attributes. This reflects an impaired map** between linguistic …
entities and their visual attributes. This reflects an impaired map** between linguistic …
Causality-inspired single-source domain generalization for medical image segmentation
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 …
one source domain do not generalize well to other unseen domains. In this work, we …
Counterfactual zero-shot and open-set visual recognition
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 …
Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by …
Learning conditional attributes for compositional zero-shot learning
Abstract Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel
compositional concepts based on learned concepts such as attribute-object combinations …
compositional concepts based on learned concepts such as attribute-object combinations …
Transzero: Attribute-guided transformer for zero-shot learning
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 …
knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute …
Siamese contrastive embedding network for compositional zero-shot learning
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 …
formed from seen state and object during training. Since the same state may be various in …
Learning attention as disentangler for compositional zero-shot learning
Compositional zero-shot learning (CZSL) aims at learning visual concepts (ie, attributes and
objects) from seen compositions and combining concept knowledge into unseen …
objects) from seen compositions and combining concept knowledge into unseen …
Disentangling visual embeddings for attributes and objects
We study the problem of compositional zero-shot learning for object-attribute recognition.
Prior works use visual features extracted with a backbone network, pre-trained for object …
Prior works use visual features extracted with a backbone network, pre-trained for object …