CLAREL: Classification via retrieval loss for zero-shot learning
Abstract
We address the problem of learning fine-grained cross-modal representations. We propose an instance-based deep metric learning approach in joint visual and textual space. The key novelty of this paper is that it shows that using per-image semantic supervision leads to substantial improvement in zero-shot performance over using class-only supervision. On top of that, we provide a probabilistic justification for a metric rescaling approach that solves a very common problem in the generalized zero-shot learning setting, i.e., classifying test images from unseen classes as one of the classes seen during training. We evaluate our approach on two fine-grained zero-shot learning datasets: CUB and FLOWERS. We find that on the generalized zero-shot classification task CLAREL consistently outperforms the existing approaches on both datasets.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2019
- DOI:
- 10.48550/arXiv.1906.11892
- arXiv:
- arXiv:1906.11892
- Bibcode:
- 2019arXiv190611892O
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning;
- Statistics - Machine Learning