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729,407
Semantic Graph for Zero-Shot Learning
"... Zero-shot learning aims to classify visual objects without any training data via knowledge transfer between seen and unseen classes. This is typically achieved by exploring a semantic embedding space where the seen and unseen classes can be related. Previous works differ in what embedding space is u ..."
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Zero-shot learning aims to classify visual objects without any training data via knowledge transfer between seen and unseen classes. This is typically achieved by exploring a semantic embedding space where the seen and unseen classes can be related. Previous works differ in what embedding space
Making Large-Scale Support Vector Machine Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 620 (1 self)
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algorithmic and computational results developed for SVM light V2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains.
Zero-Shot Recognition with Unreliable Attributes
"... In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category’s attributes. For example, with classifiers for generic attributes like striped and four-legged, one can construct a classifier for the zebra category by enumerating which properties it ..."
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Cited by 2 (0 self)
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In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category’s attributes. For example, with classifiers for generic attributes like striped and four-legged, one can construct a classifier for the zebra category by enumerating which properties
D.: Zero-Shot Learning via Visual Abstraction
- In: ECCV
"... Abstract. One of the main challenges in learning fine-grained visual categories is gathering training images. Recent work in Zero-Shot Learn-ing (ZSL) circumvents this challenge by describing categories via at-tributes or text. However, not all visual concepts, e.g., two people danc-ing, are easily ..."
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Cited by 3 (3 self)
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Abstract. One of the main challenges in learning fine-grained visual categories is gathering training images. Recent work in Zero-Shot Learn-ing (ZSL) circumvents this challenge by describing categories via at-tributes or text. However, not all visual concepts, e.g., two people danc-ing, are easily
Zero-shot learning via semantic similarity embedding
- In ICCV, 2015. 8
"... In this paper we consider a version of the zero-shot learning problem where seen class source and target do-main data are provided. The goal during test-time is to ac-curately predict the class label of an unseen target domain instance based on revealed source domain side information (e.g. attribute ..."
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Cited by 1 (1 self)
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In this paper we consider a version of the zero-shot learning problem where seen class source and target do-main data are provided. The goal during test-time is to ac-curately predict the class label of an unseen target domain instance based on revealed source domain side information (e
PLSA-BASED ZERO-SHOT LEARNING
"... Current zero-shot learning methods relied on attributes to de-scribe the unseen class characteristics, using the learned seen class model. However, these approaches required extensive attribute labels on each object class, and a well-defined, at-tributes relationship between the seen and unseen clas ..."
Abstract
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Cited by 3 (3 self)
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Current zero-shot learning methods relied on attributes to de-scribe the unseen class characteristics, using the learned seen class model. However, these approaches required extensive attribute labels on each object class, and a well-defined, at-tributes relationship between the seen and unseen
CYC: A Large-Scale Investment in Knowledge Infrastructure
- Communications of the ACM
, 1995
"... This article examines the fundamental ..."
Zero-Shot Learning with Structured Embeddings
"... Despite significant recent advances in image classification, fine-grained classifi-cation remains a challenge. In the present paper, we address the zero-shot and few-shot learning scenarios as obtaining labeled data is especially difficult for fine-grained classification tasks. First, we embed state ..."
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Despite significant recent advances in image classification, fine-grained classifi-cation remains a challenge. In the present paper, we address the zero-shot and few-shot learning scenarios as obtaining labeled data is especially difficult for fine-grained classification tasks. First, we embed
FU ET AL: TRANSDUCTIVE MULTI-LABEL ZERO-SHOT LEARNING 1 Transductive Multi-label Zero-shot Learning
"... Zero-shot learning has received increasing interest as a means to alleviate the of-ten prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic represen-tations in the form of attributes and mor ..."
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Zero-shot learning has received increasing interest as a means to alleviate the of-ten prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic represen-tations in the form of attributes
COSTA: Co-Occurrence Statistics for Zero-Shot Classification
"... In this paper we aim for zero-shot classification, that is visual recognition of an unseen class by using knowl-edge transfer from known classes. Our main contribution is COSTA, which exploits co-occurrences of visual concepts in images for knowledge transfer. These inter-dependencies arise naturall ..."
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Cited by 6 (0 self)
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regression model for learning a weight for each related class. We also show that our zero-shot classifiers can serve as priors for few-shot learning. Exper-iments on three multi-labeled datasets reveal that our pro-posed zero-shot methods, are approaching and occasionally outperforming fully supervised SVMs
Results 1 - 10
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729,407
