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Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning [article]

Hantao Yao, Shaobo Min, Yongdong Zhang, Changsheng Xu
2020 arXiv   pre-print
To solve the above problem, we propose a novel Attribute-Induced Bias Eliminating (AIBE) module for Transductive ZSL.  ...  Transductive Zero-shot learning (ZSL) targets to recognize the unseen categories by aligning the visual and semantic information in a joint embedding space.  ...  The substantial improvement can demonstrate the effectiveness of the proposed Attribute-Induced Bias Eliminating for zero-shot learning. Generalized Zero-shot learning.  ... 
arXiv:2006.00412v1 fatcat:vpz5hp3otfbdbeyt3aptuj5tla

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review [article]

Mahdi Rezaei, Mahsa Shahidi
2020 arXiv   pre-print
The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL).  ...  In this review paper, we present the definition of the problem, we review over fundamentals, and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions as well  ...  of Zero-Shot learning leverage manually annotated attributes in a two-stage learning schema.  ... 
arXiv:2004.14143v2 fatcat:erh6xyog7bb5vofcebkk2zxumm

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Mahdi Rezaei, Mahsa Shahidi
2020 Intelligence-Based Medicine  
The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL).  ...  We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their  ...  of Zero-Shot learning leverage manually annotated attributes in a two-stage learning schema.  ... 
doi:10.1016/j.ibmed.2020.100005 pmid:33043311 pmcid:PMC7531283 fatcat:qzyaf7gpufhermyg5gvank5cja

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Mahdi Rezaei, Mahsa Shahidi
2020 Social Science Research Network  
A B S T R A C T The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL).  ...  We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their  ...  of Zero-Shot learning leverage manually annotated attributes in a two-stage learning schema.  ... 
doi:10.2139/ssrn.3624379 fatcat:yifnxv46rjf6pgndowkxzmo5o4

Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective [article]

Kai Li and Martin Renqiang Min and Yun Fu
2019 arXiv   pre-print
Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the semantic descriptions of the classes.  ...  from the semantic attributes with an episode-based training scheme; For the generalized setting, we concatenate the learned highly discriminative classifiers for seen classes and the generated classifiers  ...  The best results are in bold. f-CLSWGAN, which generates additional data for training. Transductive zero-shot learning.  ... 
arXiv:1909.05995v2 fatcat:qsegfnko5vbjralyfrulkq4ory

Transductive Zero-Shot Action Recognition by Word-Vector Embedding [article]

Xun Xu, Timothy Hospedales, Shaogang Gong
2016 arXiv   pre-print
Instead of collecting ever more data and labelling them exhaustively for all categories, an attractive alternative approach is zero-shot learning" (ZSL).  ...  to learn for the purpose of generalising over any cross-category domain shift.  ...  zero-shot learning approaches with both wordvector and attribute embeddings.  ... 
arXiv:1511.04458v2 fatcat:yxfn52pdhjfatedmixz4evtiay

Transductive Zero-Shot Action Recognition by Word-Vector Embedding

Xun Xu, Timothy Hospedales, Shaogang Gong
2017 International Journal of Computer Vision  
The results demonstrate that our approach achieves the state-of-the-art zero-shot action recognition performance with a simple and efficient pipeline, and without supervised annotation of attributes.  ...  Finally, we present in-depth analysis into why and when zero-shot works, including demonstrating the ability to predict cross-category transferability in advance.  ...  |x). 3. for zero-shot learning.  ... 
doi:10.1007/s11263-016-0983-5 fatcat:c6rn4jpg3ff5pbks52ohlcafny

DASZL: Dynamic Action Signatures for Zero-shot Learning [article]

Tae Soo Kim, Jonathan D. Jones, Michael Peven, Zihao Xiao, Jin Bai, Yi Zhang, Weichao Qiu, Alan Yuille, Gregory D. Hager
2020 arXiv   pre-print
We also extend this method to form a unique framework for zero-shot joint segmentation and classification of activities in video and demonstrate the first results in zero-shot decoding of complex action  ...  by deep-learned components.  ...  We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Ti-tan V GPUs used for this research.  ... 
arXiv:1912.03613v3 fatcat:adjifazqhvc4lpyqgveu3imfkq

Dont Even Look Once: Synthesizing Features for Zero-Shot Detection [article]

Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
2020 arXiv   pre-print
Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated  ...  Our proposed scheme is evaluated on Pascal VOC and MSCOCO, and we demonstrate significant improvements in test accuracy over vanilla and other state-of-art zero-shot detectors  ...  GZSL, optimizing for TU can induce an unseen class bias resulting in poor performance on seen.  ... 
arXiv:1911.07933v3 fatcat:c4nesge7bzfknm55zkzs66ywp4

A Deep Dive into Adversarial Robustness in Zero-Shot Learning [article]

Mehmet Kerim Yucel, Ramazan Gokberk Cinbis, Pinar Duygulu
2020 arXiv   pre-print
In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes.  ...  In addition to creating possibly the first benchmark on adversarial robustness of ZSL models, we also present analyses on important points that require attention for better interpretation of ZSL robustness  ...  In Zero-shot learning (ZSL) and Generalized Zero-shot learning (GZSL) settings, however, the task differs from a generic supervised approach; the aim is to learn from a set of classes such that we can  ... 
arXiv:2008.07651v1 fatcat:76w46g67k5h4raa3ztjxnuwqca

Zero-Shot Action Recognition in Videos: A Survey [article]

Valter Estevam, Helio Pedrini, David Menotti
2020 arXiv   pre-print
specifically zero-shot action recognition in videos.  ...  Zero-Shot Action Recognition has attracted attention in the last years and many approaches have been proposed for recognition of objects, events and actions in images and videos.  ...  Another important issue is the Few-Shot Learning (FSL) or generalized zero-shot learning.  ... 
arXiv:1909.06423v2 fatcat:w5eh7wjdmnaktnbsqczsdmhane

Polarity Loss for Zero-shot Object Detection [article]

Shafin Rahman, Salman Khan, Nick Barnes
2020 arXiv   pre-print
In this paper, we propose a novel loss function called 'Polarity loss', that promotes correct visual-semantic alignment for an improved zero-shot object detection.  ...  To mimic similar behaviour, zero-shot object detection aims to recognize and localize 'unseen' object instances by using only their semantic information.  ...  RELATED WORK Zero-shot learning (ZSL): The earliest efforts were based on manually annotated attributes as a mid-level semantic representation [7] .  ... 
arXiv:1811.08982v3 fatcat:oihf7b2i3jaxxjx44qxq5eisk4

Small Sample Learning in Big Data Era [article]

Jun Shu, Zongben Xu, Deyu Meng
2018 arXiv   pre-print
The second category is called "experience learning", which usually co-exists with the large sample learning manner of conventional machine learning.  ...  This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures.  ...  They provided two models for zero-shot learning, i.e., direct attribute prediction (DAP) and indirect attribute prediction (IAP), with the idea that learned the probability of attributes for given visual  ... 
arXiv:1808.04572v3 fatcat:lqqzzrmgfnfb3izctvdzgopuny

Deep Reinforcement Learning [article]

Yuxi Li
2018 arXiv   pre-print
Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn.  ...  We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources.  ...  Lanctot et al. (2017) observe that independent RL, in which each agent learns by interacting with the environment, oblivious to other agents, can overfit the learned policies to other agents' policies  ... 
arXiv:1810.06339v1 fatcat:kp7atz5pdbeqta352e6b3nmuhy

Synaptic and nonsynaptic plasticity approximating probabilistic inference

Philip J. Tully, Matthias H. Hennig, Anders Lansner
2014 Frontiers in Synaptic Neuroscience  
Hence in analogy to neural transduction, a support value s j = β j + N i = 1 π x i w x i y j can be calculated by iterating over the set of possible conditioning attribute values N = Hn h for y j with  ...  The LTD area shrank for a constant STDP window width when τ p was increased because it induced a longer decay time for the P traces (Figure 5F ), emphasizing a slowness in learning.  ... 
doi:10.3389/fnsyn.2014.00008 pmid:24782758 pmcid:PMC3986567 fatcat:onoz2wjo5bhfbck25wnidrmph4
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