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Found 13 papers, 3 papers with code
Online Lifelong Generalized Zero-Shot Learning
Methods proposed in the literature for zero-shot learning (ZSL) are typically suitable for offline learning and cannot continually learn from sequential streaming data.
Generative Replay-based Continual Zero-Shot Learning
Zero-shot learning is a new paradigm to classify objects from classes that are not available at training time.
Generalized Continual Zero-Shot Learning
Further, to enhance the reliability, we develop CZSL for a single head continual learning setting where task identity is revealed during the training process but not during the testing.
Towards Zero-Shot Learning with Fewer Seen Class Examples
We present a meta-learning based generative model for zero-shot learning (ZSL) towards a challenging setting when the number of training examples from each \emph{seen} class is very few.
Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks On Deep COVID-19 Models
The experiments and results show the proposed model prevents the deep model against the six adversarial attacks and maintains high accuracy to classify the COVID-19 cases from the Chest X-Ray image and CT image Datasets.
Stacked Adversarial Network for Zero-Shot Sketch based Image Retrieval
Conventional approaches to Sketch-Based Image Retrieval (SBIR) assume that the data of all the classes are available during training.
Generative Model for Zero-Shot Sketch-Based Image Retrieval
We present a probabilistic model for Sketch-Based Image Retrieval (SBIR) where, at retrieval time, we are given sketches from novel classes, that were not present at training time.
A Zero-Shot Framework for Sketch based Image Retrieval
In this paper, we propose a new bench mark for zero-shot SBIR where the model is evaluated on novel classes that are not seen during training.
A Zero-Shot Framework for Sketch-based Image Retrieval
In this paper, we propose a new benchmark for zero-shot SBIR where the model is evaluated in novel classes that are not seen during training.
A Generative Approach to Zero-Shot and Few-Shot Action Recognition
In particular, we assume that the distribution parameters for any action class in the visual space can be expressed as a linear combination of a set of basis vectors where the combination weights are given by the attributes of the action class.
Generalized Zero-Shot Learning via Synthesized Examples
Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero-shot learning settings.
A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders
By extensive testing on four benchmark datasets, we show that our model outperforms the state of the art, particularly in the more realistic generalized setting, where the training classes can also appear at the test time along with the novel classes.
Bi-modal First Impressions Recognition using Temporally Ordered Deep Audio and Stochastic Visual Features
We propose a novel approach for First Impressions Recognition in terms of the Big Five personality-traits from short videos.
