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The recognition of unseen objects from a semantic representation or textualdescription, usually denoted as zero-shot learning, is more prone to be used inreal-world scenarios when compared to traditional object recognition.Nevertheless, no work has evaluated the feasibility of deploying zero-shotlearning approaches in these scenarios, particularly when using low-powerdevices. In this paper, we provide the first benchmark on the inference time ofzero-shot learning, comprising an evaluation of state-of-the-art approachesregarding their speed/accuracy trade-off. An analysis to the processing time ofthe different phases of the ZSL inference stage reveals that visual featureextraction is the major bottleneck in this paradigm, but, we show thatlightweight networks can dramatically reduce the overall inference time withoutreducing the accuracy obtained by the de facto ResNet101 architecture. Also,this benchmark evaluates how different ZSL approaches perform in low-powerdevices, and how the visual feature extraction phase could be optimized in thishardware. To foster the research and deployment of ZSL systems capable ofoperating in real-world scenarios, we release the evaluation framework used inthis benchmark (https://github.com/CristianoPatricio/zsl-methods).

ZSpeedL -- Evaluating the Performance of Zero-Shot Learning Methods using Low-Power Devices