Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning

Yuval Atzmon, Gal Chechik
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:381-391, 2018.

Abstract

In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples. Instead, it is given seman- tic information about the class, like a textual description or a set of attributes. Learning from attributes could benefit from explicitly modeling structure of the attribute space. Un- fortunately, learning of general structure from empirical samples is hard with typical dataset sizes. Here we describe LAGO1, a probabilistic model designed to capture natural soft and- or relations across groups of attributes. We show how this model can be learned end-to- end with a deep attribute-detection model. The soft group structure can be learned from data jointly as part of the model, and can also read- ily incorporate prior knowledge about groups if available. The soft and-or structure suc- ceeds to capture meaningful and predictive structures, improving the accuracy of zero-shot learning on two of three benchmarks. Finally, LAGO reveals a unified formulation over two ZSL approaches: DAP (Lampert et al., 2009) and ESZSL (Romera-Paredes & Torr, 2015). Interestingly, taking only one sin- gleton group for each attribute, introduces a new soft-relaxation of DAP, that outperforms DAP by $\tilde$40%.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-atzmon18a, title = {Probabilistic {AND}-{OR} Attribute Grouping for Zero-Shot Learning}, author = {Atzmon, Yuval and Chechik, Gal}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {381--391}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/atzmon18a/atzmon18a.pdf}, url = {https://proceedings.mlr.press/r16/atzmon18a.html}, abstract = {In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples. Instead, it is given seman- tic information about the class, like a textual description or a set of attributes. Learning from attributes could benefit from explicitly modeling structure of the attribute space. Un- fortunately, learning of general structure from empirical samples is hard with typical dataset sizes. Here we describe LAGO1, a probabilistic model designed to capture natural soft and- or relations across groups of attributes. We show how this model can be learned end-to- end with a deep attribute-detection model. The soft group structure can be learned from data jointly as part of the model, and can also read- ily incorporate prior knowledge about groups if available. The soft and-or structure suc- ceeds to capture meaningful and predictive structures, improving the accuracy of zero-shot learning on two of three benchmarks. Finally, LAGO reveals a unified formulation over two ZSL approaches: DAP (Lampert et al., 2009) and ESZSL (Romera-Paredes & Torr, 2015). Interestingly, taking only one sin- gleton group for each attribute, introduces a new soft-relaxation of DAP, that outperforms DAP by $\tilde$40%.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning %A Yuval Atzmon %A Gal Chechik %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-atzmon18a %I PMLR %P 381--391 %U https://proceedings.mlr.press/r16/atzmon18a.html %V R16 %X In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples. Instead, it is given seman- tic information about the class, like a textual description or a set of attributes. Learning from attributes could benefit from explicitly modeling structure of the attribute space. Un- fortunately, learning of general structure from empirical samples is hard with typical dataset sizes. Here we describe LAGO1, a probabilistic model designed to capture natural soft and- or relations across groups of attributes. We show how this model can be learned end-to- end with a deep attribute-detection model. The soft group structure can be learned from data jointly as part of the model, and can also read- ily incorporate prior knowledge about groups if available. The soft and-or structure suc- ceeds to capture meaningful and predictive structures, improving the accuracy of zero-shot learning on two of three benchmarks. Finally, LAGO reveals a unified formulation over two ZSL approaches: DAP (Lampert et al., 2009) and ESZSL (Romera-Paredes & Torr, 2015). Interestingly, taking only one sin- gleton group for each attribute, introduces a new soft-relaxation of DAP, that outperforms DAP by $\tilde$40%. %Z Reissued by PMLR on 04 October 2026.
APA
Atzmon, Y. & Chechik, G.. (2018). Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:381-391 Available from https://proceedings.mlr.press/r16/atzmon18a.html. Reissued by PMLR on 04 October 2026.

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