Results 231 to 240 of about 16,806 (262)
Some of the next articles are maybe not open access.
IEEE Transactions on Circuits and Systems for Video Technology, 2022
Zero-shot Learning (ZSL) is a highly non-trivial task to generalize from seen to unseen classes. In this paper, we propose spherical zero-shot learning (SZSL) to address the major challenges in ZSL. By decoupling the similarity metric in the spherical embedding space into radius and angle, our SZSL can map classes to hyperspherical surfaces of ...
Jiayi Shen +3 more
openaire +2 more sources
Zero-shot Learning (ZSL) is a highly non-trivial task to generalize from seen to unseen classes. In this paper, we propose spherical zero-shot learning (SZSL) to address the major challenges in ZSL. By decoupling the similarity metric in the spherical embedding space into radius and angle, our SZSL can map classes to hyperspherical surfaces of ...
Jiayi Shen +3 more
openaire +2 more sources
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019
In this work, we tackle the zero-shot metric learning problem and propose a novel method abbreviated as ZSML, with the purpose to learn a distance metric that measures the similarity of unseen categories (even unseen datasets). ZSML achieves strong transferability by capturing multi-nonlinear yet continuous relation among data.
Xinyi Xu +4 more
openaire +2 more sources
In this work, we tackle the zero-shot metric learning problem and propose a novel method abbreviated as ZSML, with the purpose to learn a distance metric that measures the similarity of unseen categories (even unseen datasets). ZSML achieves strong transferability by capturing multi-nonlinear yet continuous relation among data.
Xinyi Xu +4 more
openaire +2 more sources
Towards Open Zero-Shot Learning
2022In Generalized Zero-Shot Learning (GZSL), unseen categories (for which no visual data are available at training time) can be predicted by leveraging their class embeddings (e.g., a list of attributes describing them) together with a complementary pool of seen classes (paired with both visual data and class embeddings).
Federico Marmoreo +3 more
openaire +2 more sources
Zero-Shot Learning with Superclasses
2018Zero-shot learning (ZSL) can be regarded as transfer learning from seen classes to unseen ones so that the later can be recognized without any training samples. Its main difficulty lies in that there often exists a large domain gap between the seen and unseen class domains.
Yuqi Huo +5 more
openaire +2 more sources
A review on multimodal zero‐shot learning
WIREs Data Mining and Knowledge Discovery, 2023AbstractMultimodal learning provides a path to fully utilize all types of information related to the modeling target to provide the model with a global vision. Zero‐shot learning (ZSL) is a general solution for incorporating prior knowledge into data‐driven models and achieving accurate class identification.
Weipeng Cao +6 more
openaire +2 more sources
Zero-Shot Learning With Transferred Samples
IEEE Transactions on Image Processing, 2017By transferring knowledge from the abundant labeled samples of known source classes, zero-shot learning (ZSL) makes it possible to train recognition models for novel target classes that have no labeled samples. Conventional ZSL approaches usually adopt a two-step recognition strategy, in which the test sample is projected into an intermediary space in ...
Yuchen Guo +3 more
openaire +3 more sources
Zero-Shot Learning with Fuzzy Attribute
2017 3rd IEEE International Conference on Cybernetics (CYBCONF), 2017As the zero-shot problem was proposed in machine learning field, attributes became the key point to solve zero-shot problems. The wildly used binary attribute in zero-shot learning has many limitations, and many researches had made an improvement on it. In this paper, we propose fuzzy attributes, which can describe objects better than binary attributes.
Chong Wen Liu +2 more
openaire +1 more source
2018
Zero-shot learning targets at precisely recognizing unseen categories through a shared visual-semantic function, which is built on the seen categories and expected to well adapt to unseen categories. However, the semantic gap across visual features and their underlying semantics is still the most challenging obstacle.
Zhengming Ding, Handong Zhao, Yun Fu
openaire +1 more source
Zero-shot learning targets at precisely recognizing unseen categories through a shared visual-semantic function, which is built on the seen categories and expected to well adapt to unseen categories. However, the semantic gap across visual features and their underlying semantics is still the most challenging obstacle.
Zhengming Ding, Handong Zhao, Yun Fu
openaire +1 more source
Zero-Shot Learning With Attribute Selection
Proceedings of the AAAI Conference on Artificial Intelligence, 2018Zero-shot learning (ZSL) is regarded as an effective way to construct classification models for target classes which have no labeled samples available. The basic framework is to transfer knowledge from (different) auxiliary source classes having sufficient labeled samples with some attributes shared by target and source classes as ...
Yuchen Guo +3 more
openaire +2 more sources
Zero-Shot Learning for Gesture Recognition
Proceedings of the 2020 International Conference on Multimodal Interaction, 2020Zero-Shot Learning (ZSL) is a new paradigm in machine learning that aims to recognize the classes that are not present in the training data. Hence, this paradigm is capable of comprehending the categories that were never seen before. While deep learning has pushed the limits of unseen object recognition, ZSL for temporal problems such as unfamiliar ...
openaire +2 more sources