2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-5.4
Paper Title BIAS: BIJECTIVE INPUT AND SURJECTIVITY IN ZERO SHOT LEARNING
Authors Rishabh Singh, Eidgenössische Technische Hochschule Zürich, Switzerland
SessionMLR-APPL-IVASR-5: Machine learning for image and video analysis, synthesis, and retrieval 5
LocationArea C
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Zero-shot learning suffers from the issue of generalization due to domain shift across seen and unseen classes. In this paper, we propose a method that extends the usual approach of learning a mapping between semantic and visual embedding spaces by ensuring it to be surjective. This functional constraint along with triplet loss prevents the model from over-fitting to seen classes. We also use a bijective feature extractor to complement our proposal. Experimental results on benchmark datasets depict that our method outperforms standard approaches in conventional and generalized scenarios.