CristianoPatricio / ZSL_Thesis

A collection of papers, code and other things of my research in Zero-Shot Learning.

📁 Thesis Repository

Description:

• A collection of papers, code and other things of my research in Zero-Shot Learning.

Table of Contents

1. Papers
2. Datasets

📃 Papers

Paper	Author	Year	Folder
🔗 Describing objects by their attributes	A. Farhadi et al.	2009	Describing_Objects_Attributes/
🔗 Learning to detect unseen object classes by between-class attribute transfer	C. H. Lampert et al.	2009	DAP_IAP/
🔗 Zero-Shot Learning with Semantic Output Codes	M. Palatucci et al.	2009	SOC/
🔗 Zero-Shot Learning Through Cross-Modal Transfer	R. Socher et al.	2013	CMT/
🔗 Zero-Shot Learning by Convex Combination of Semantic Embeddings	M. Norouzi et al.	2013	ConSE/
🔗 DeViSE: A Deep Visual-Semantic Embedding Model	A. Frome et al.	2013	DeViSE/
🔗 Label-Embedding for Attribute-Based Classification	Z. Akata et al.	2013	ALE/
🔗 An embarrassingly simple approach to zero-shot learning	B. Romera-Paredes et al.	2015	ESZSL/
🔗 Synthesized Classifiers for Zero-Shot Learning	S. Changpinyo et al.	2016	SYNC/
🔗 Latent Embeddings for Zero-shot Classification	Y. Xian et al.	2016	LatEm/
🔗 Semantic Autoencoder for Zero-Shot Learning	E. Kodirov et al.	2017	SAE/
🔗 Learning a Deep Embedding Model for Zero-Shot Learning	L. Zhang et al.	2017	DEM/
🔗 A Simple Exponential Family Frameworkfor Zero-Shot Learning	V. Verma et al.	2018	-
🔗 Feature Generating Networks for Zero-Shot Learning	Y. Xian et al.	2018	-
🔗 Generalized Zero-Shot Learning with Deep Calibration Network	Y. Xian et al.	2018	-
🔗 A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders	A. Mishra et al.	2018	-
🔗 Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly	S. Liu et al.	2018	Survey ZSL/
🔗 f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning	Y. Xian et al.	2019	-
🔗 Semantic-Guided Multi-Attention Localization for Zero-Shot Learning	Y. Zhu et al.	2019	-
🔗 A Survey of Zero-Shot Learning: Settings, Methods, and Applications	W. Wang et al.	2019	Survey ZSL/
🔗 Region Graph Embedding Network for Zero-Shot Learning	G. Xie et al.	2020	-
🔗 Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention	D. Huynh et al.	2020	-
🔗 Generalized Zero-Shot Learning Via Over-Complete Distribution	R. Keshari et al.	2020	-

📚 Datasets

Dataset	No. classes	No. instances	No. attributes	Annotation level	Type
🔗 CUB-200-2011	200	11788	312	Per class	Fine-grained classification
🔗 Oxford 102 Flower	102	8189	None	None	Fine-grained classification
🔗 SUN Attributes	717	14340	102	Per image	Fine-grained classification
🔗 Stanford Dogs	120	20580	None	Class labels/Bounding boxes	Fine-grained classification
🔗 AwA2	50	37322	85	Per class	General classification
🔗 aPascal-aYahoo	32	15339	64	Per image	General classification
🔗 PubFig	200	58797	None	Per image	General classification
🔗 PubFig-sub	8	772	11	Per image pairs	General classification
🔗 OSR	8	2688	6	Per image pairs	General classification
🔗 ImageNet	22000	15 million	None	Per image	General classification
🔗 ImageNet 2012 1K	1000	1,2 million	None	Per image	General classification
🔗 CIFAR-10	10	60000	None	Per image	General classification
🔗 CIFAR-100	100	15339	None	Per image	General classification

License

MIT License: see the LICENSE file.