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- A zeroshot learning method for recognizing objects using lowpower devicesPublication . Patrício, Cristiano Pires; Neves, João Carlos Raposo; Proença, Hugo Pedro Martins CarriçoZeroShot Learning (ZSL) has been a subject of increasing interest due to its revolutionary paradigm that simulates human behavior in recognizing objects that have never seen before. The ZSL models must be capable of recognizing classes that do not appear during training, using only the provided textual descriptions of the unseen classes as an aid. Despite the vast benchmarking around the ZSL paradigm, few works have assessed the computational performance of the developed strategy regarding inference time. Furthermore, no work has evaluated the effects of using different CNN architectures, such as lightweight architectures, apart from the de facto standard ResNet101 architecture, and the feasibility of deploying zeroshot learning approaches in a realworld scenario, particularly when using lowpower devices. Consequently, in this dissertation, we carried out an extensive benchmarking toward analyzing the impact of using lightweight CNN architectures on ZSL performance, allowing us to perceive how the ZSL methods perform in realworld scenarios, mainly when run in lowpower devices. Our experimental results demonstrate that the impact on the ZSL accuracy is not significant when a lightweight architecture is adopted, indicating the effectiveness of such lowpower devices in performing ZSL methods.