CLAREL: classification via retrieval loss for zero-shot learning

Boris N. Oreshkin, Negar Rostamzadeh, Pedro O. Pinheiro, Christopher Pal

Keywords: zero-shot learning, representation learning, fine-grained classification

TL;DR: We propose an instance-based deep metric learning approach in joint visual and textual space. We show that per-image semantic supervision leads to substantial improvement over class-only supervision in zero shot classification.

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.