Joint Dictionaries for Zero-Shot Learning
A classic approach toward zero-shot learning (ZSL) is to map the input domain to a set of semantically meaningful attributes that could be used later on to classify unseen classes of data (e.g. visual data). In this paper, we propose to learn a visual feature dictionary that has semantically meaningful atoms. Such dictionary is learned via joint dictionary learning for the visual domain and the attribute domain, while enforcing the same sparse coding for both dictionaries. Our novel attribute aware formulation provides an algorithmic solution to the domain shift/hubness problem in ZSL. Upon learning the joint dictionaries, images from unseen classes can be mapped into the attribute space by finding the attribute aware joint sparse representation using solely the visual data. We demonstrate that our approach provides superior or comparable performance to that of the state of the art on benchmark datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeDictionary LearningZero-Shot LearningSimilar Papers 제목 키워드 기반
Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning
Zero-shot learning for visual recognition has received much interest in the most recent years. However, the semantic gap across visual features and their underlying semantics is still the biggest obstacle in zero-shot le…
Dictionary LearningZero-Shot LearningTextual Entailment with Dynamic Contrastive Learning for Zero-shot NER
In this paper, we study the problem of zero-shot NER, which aims at building a Named Entity Recognition (NER) system from scratch. It needs to identify the entities in the given sentences when we have zero token-level an…
Contrastive Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3Zero-Shot Image Classification Using Coupled Dictionary Embedding
Zero-shot learning (ZSL) is a framework to classify images belonging to unseen classes based on solely semantic information about these unseen classes. In this paper, we propose a new ZSL algorithm using coupled dictiona…
AttributeClassificationDictionary LearningGeneral Classification+4Semantically Informed Slang Interpretation
Slang is a predominant form of informal language making flexible and extended use of words that is notoriously hard for natural language processing systems to interpret. Existing approaches to slang interpretation tend t…
Machine TranslationTranslationSemantically Informed Slang Interpretation
Slang is a predominant form of informal language making flexible and extended use of words that is notoriously hard for natural language processing systems to interpret. Existing approaches to slang interpretation tend t…
Machine TranslationTranslation