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Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
[article]
2018
arXiv
pre-print
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With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. ...
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. ...
Extending from multi-label classification, multi-label zero-shot learning (ML-ZSL) is a branch of zero-shot learning (ZSL), which require the prediction of unseen labels which are not defined during training ...
arXiv:1711.06526v2
fatcat:jssexvmbwzhctou7jy5n7jo24i
Multi-label Zero-Shot Learning with Structured Knowledge Graphs
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. ...
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. ...
Extending from multi-label classification, multi-label zero-shot learning (ML-ZSL) is a branch of zero-shot learning (ZSL), which require the prediction of unseen labels which are not defined during training ...
doi:10.1109/cvpr.2018.00170
dblp:conf/cvpr/LeeFYW18
fatcat:nspq2q7e7bhnnf3svks4yi4cpi
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
[article]
2020
arXiv
pre-print
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can benefit multi-label zero/few-shot document classification. ...
on few/zero-shot labels. ...
Experiments on three real-world datasets show that neural classifiers equipped with our multi-graph knowledge aggregation model can significantly improve the few/zero-shot classification performance. ...
arXiv:2010.07459v1
fatcat:dd5tkzft3rdgvkqzrgyjfgv5ye
Transductive Multi-class and Multi-label Zero-shot Learning
[article]
2015
arXiv
pre-print
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Recently, zero-shot learning (ZSL) has received increasing interest. ...
In this paper we discuss two related lines of work improving the conventional approach: exploiting transductive learning ZSL, and generalising ZSL to the multi-label case. ...
We propose a novel framework for multi-label zero-shot learning [9] . ...
arXiv:1503.07884v1
fatcat:or4zahtjj5atpoxks5n4dilega
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge
[article]
2020
arXiv
pre-print
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To address these difficult issues, this paper introduces a novel multi-label zero-shot classification framework by learning to transfer from external knowledge. ...
Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. It is more challenging than its single-label counterpart. ...
Some studies [7, 8, 14, 17, 18, 22, 35] try to solve the multi-label zero-shot learning problem by leveraging word embeddings [35] or constructing structured knowledge graphs [14] . ...
arXiv:2007.15610v2
fatcat:5dqtojkavjb25g4yrp42xb4tge
Zero-Shot Ingredient Recognition by Multi-Relational Graph Convolutional Network
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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More specifically, we introduce multi-relational GCN (graph convolutional network) that integrates ingredient hierarchy, attribute as well as co-occurrence for zero-shot ingredient recognition. ...
Therefore, in this paper, we target the problem of ingredient recognition with zero training samples. ...
Compared with single-label zero-shot learning, multi-label zero-shot learning receives less attention. ...
doi:10.1609/aaai.v34i07.6626
fatcat:47emjeg2sjb3bjtlx7iwwr3roa
Transfer Learning in a Transductive Setting
2013
Neural Information Processing Systems
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More specifically we adapt a graph-based learning algorithm -so far only used for semi-supervised learningto zero-shot and few-shot learning. ...
In this work, we extend transfer learning with semi-supervised learning to exploit unlabeled instances of (novel) categories with no or only a few labeled instances. ...
Most notably we achieve 83.7% AUC / 42.7% multi-class accuracy on the Animals with Attributes dataset for zero-shot recognition, scale to 200 unseen classes on ImageNet, and achieve up to 34.4% (+12.0% ...
dblp:conf/nips/RohrbachES13
fatcat:b3mufpaek5dqnfqogtaqbf3jzm
Transductive Multi-View Zero-Shot Learning
2015
IEEE Transactions on Pattern Analysis and Machine Intelligence
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To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. ...
Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation shared between an annotated auxiliary dataset and a target dataset with different ...
Thus the difference between zero-shot and N-shot learning lies only on the initial labelled instances: Zero-shot learning has the prototypes as labelled nodes; N-shot has instances as labelled nodes; and ...
doi:10.1109/tpami.2015.2408354
pmid:26440271
fatcat:eazqbmoc6vholji7ke6yyis5wq
Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation
[chapter]
2014
Lecture Notes in Computer Science
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Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation such as visual attributes or semantic word vectors. ...
In this paper we identify an inherent limitation with this approach. ...
classes with no training example, i.e. zero-shot learning. ...
doi:10.1007/978-3-319-10605-2_38
fatcat:clv6flbs4najfoxbhyfucgsxhy
Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective
[article]
2022
arXiv
pre-print
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Knowledge Extraction (KE) which aims to extract structural information from unstructured texts often suffers from data scarcity and emerging unseen types, i.e., low-resource scenarios. ...
Multi-task Learning (2) Knowledge Graph (KG) have leveraged FrameNet 2 to improve EE since frames defined in FrameNet share highly similar structures with events in ACE 3 EE program. ...
a zero-shot learning method. ...
arXiv:2202.08063v1
fatcat:2q64tx2mzne53gt24adi6ymj7a
Topological Transduction for Hybrid Few-shot Learning
2022
Proceedings of the ACM Web Conference 2022
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Few-shot learning (FSL) has attracted significant research attention for dealing with scarcely labeled concepts. ...
from both labeled and unlabeled data to enrich the knowledge about the task-specific data distribution and multi-space relationships. ...
In contrast, the graph structure of our task is not given, and moreover, we generalize the graph structure knowledge across unlimited graphs and adapt the graph learning procedure over different tasks. ...
doi:10.1145/3485447.3512033
fatcat:o4jes64ec5hhfhoffrxx57j5fa
MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2007.02379v1 fatcat:ieddgihgd5cvlo6ztgncwt6l7m
In this paper, we explore the concept hierarchy knowledge by leveraging concept graph, and take the concept graph as explicit meta-knowledge for the base learner, instead of learning implicit meta-knowledge ...
Specifically, we firstly propose a novel regularization with multi-level conceptual abstraction to constrain a meta-learner to learn to abstract concepts via the concept graph (i.e. identifying the concepts ...
semantics) which is useful for distinguishing categories with similiar graph structure; 2) the category graph is extracted from knowledge graph (e.g. ...
arXiv:2007.02379v2
fatcat:fzsqgk6btfeq7ayilukdbjop4u
A Survey on Visual Transfer Learning using Knowledge Graphs
[article]
2022
arXiv
pre-print
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KGs can represent auxiliary knowledge either in an underlying graph-structured schema or in a vector-based knowledge graph embedding. ...
, such as directed labeled graphs, hypergraphs, and hyper-relational graphs. ...
Zero-Shot Learning is a visual transfer learning task with labeled source domain data and unlabeled target domain data. ...
arXiv:2201.11794v1
fatcat:tapql5h4j5dvrnxjkaxek2cquu
Learning Graph Embeddings for Compositional Zero-shot Learning
2021
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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The key to our approach is exploiting the dependency between states, objects and their compositions within a graph structure to enforce the relevant knowledge transfer from seen to unseen compositions. ...
In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog) in the training set. ...
Conclusion We propose a novel graph formulation for Compositional Zero-shot learning in the challenging generalized zero-shot setting. ...
doi:10.1109/cvpr46437.2021.00101
fatcat:zzeohcixz5h7pirypc33m6dsqe
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection
[article]
2018
arXiv
pre-print
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, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings. ...
Much effort on zero-shot learning however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has received limited attention. ...
multi-label zero-shot learning. ...
arXiv:1808.02474v1
fatcat:dov2w7ofbvdg3kdfiprkb5sm3i
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