Zero-shot learning (ZSL) aims to classify images of unseen classes by learning relationship between visual and semantic features. Existing works have been improving recognition accuracy from various approaches, but they employ computationally intensive algorithms that require iterative optimization. In this work, we revisit the primary approach of the pattern recognition, ı.e., nearest neighbor classifiers, to solve the ZSL task by an extremely simple and fast way, called SimpleZSL. Our algorithm consists of the following three simple techniques: (1) just averaging feature vectors to obtain visual prototypes of seen classes, (2) calculating a pseudo-inverse matrix via singular value decomposition to generate visual features of unseen classes, and (3) inferring unseen classes by a nearest neighbor classifier in which cosine similarity is used to measure distance between feature vectors. Through the experiments on common datasets, the proposed method achieves good recognition accuracy with drastically small computational costs. The execution time of the proposed method on a single CPU is more than 100 times faster than those of the GPU implementations of the existing methods with comparable accuracies.

A 60-GHz injection-locked frequency divider (ILFD) is presented. A multi-order LC oscillator topology is proposed to enhance the locking range of the divider. A design guideline is described based on a theoretical analysis of the locking range enhancement. A test chip is fabricated in 65 nm CMOS. Measured locking range with 0 dBm input power is 48.5–62.9 GHz (25.9%), which is 63.6% wider compared to the previously reported ILFD. Power consumption excluding buffers and biasing circuits is 1.65 mW from 1.2 V supply. The core ILFD area is 0.0157 mm2 even with an extra pair of inductors.