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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 …
A survey on open-vocabulary detection and segmentation: Past, present, and future
As the most fundamental scene understanding tasks, object detection and segmentation
have made tremendous progress in deep learning era. Due to the expensive manual …
have made tremendous progress in deep learning era. Due to the expensive manual …
A survey of zero-shot learning: Settings, methods, and applications
Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …
been seen in training. In practice, many applications require classifying instances whose …
Word translation without parallel data
A survey of cross-lingual word embedding models
Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when develo** …
multilingual contexts and are a key facilitator of cross-lingual transfer when develo** …
[PDF][PDF] Word translation without parallel data
State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …
Z-score normalization, hubness, and few-shot learning
The goal of few-shot learning (FSL) is to recognize a set of novel classes with only few
labeled samples by exploiting a large set of abundant base class samples. Adopting a meta …
labeled samples by exploiting a large set of abundant base class samples. Adopting a meta …
Primitive generation and semantic-related alignment for universal zero-shot segmentation
We study universal zero-shot segmentation in this work to achieve panoptic, instance, and
semantic segmentation for novel categories without any training samples. Such zero-shot …
semantic segmentation for novel categories without any training samples. Such zero-shot …
Generalized zero-shot learning via synthesized examples
We present a generative framework for generalized zero-shot learning where the training
and test classes are not necessarily disjoint. Built upon a variational autoencoder based …
and test classes are not necessarily disjoint. Built upon a variational autoencoder based …