Zero-Shot Learning posed as a Missing Data Problem
TL;DR: Zhang et al. as mentioned in this paper pose zero-shot learning as the missing data problem, rather than the missing label problem, and propose a transductive framework to estimate data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space.
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Abstract: This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. Specifically, most existing ZSL methods focus on learning mapping functions from the image feature space to the label embedding space. Whereas, the proposed method explores a simple yet effective transductive framework in the reverse way \--- our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. In experiments, our method outperforms the state-of-the-art on two popular datasets.
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Citations
A Survey of Zero-Shot Learning: Settings, Methods, and Applications
TL;DR: This paper categorizes existing zero-shot learning methods and introduces representative methods under each category, and highlights promising future research directions of zero- shot learning.
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AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
Jiahong Wu,He Zheng,Bo Zhao,Yixin Li,Baoming Yan,Rui Liang,Wenjia Wang,Shipei Zhou,Guosen Lin,Yanwei Fu,Yizhou Wang,Yonggang Wang +11 more
TL;DR: In this dataset, rich annotations bridge the semantic gap between low-level images and high-level concepts, and is an effective benchmark to evaluate and improve different computational methods.
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Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning
Yunlong Yu,Zhong Ji,Yanwei Fu,Jichang Guo,Yanwei Pang,Zhongfei Zhang +5 more
- 01 Jan 2018
TL;DR: A novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions.
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Small Sample Learning in Big Data Era.
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TL;DR: A survey to comprehensively introduce the current techniques proposed on Small Sample Learning and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process.
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Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review.
Mahdi Rezaei,Mahsa Shahidi +1 more
- 02 Oct 2020
TL;DR: A novel and broaden solution called Few / one-shot learning is introduced, and the definition of the ZSL problem as an extreme case of the few- shot learning is presented, to convey a useful intuition towards the goal of handling complex learning tasks more similar to the way humans learn.
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