Ming Li

TL;DR: In this paper, the angular softmax (A-softmax) loss was proposed to learn angularly discriminative features for deep face recognition under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal interclass distance under a suitably chosen metric space.

TL;DR: This paper proposes the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features in deep face recognition (FR) problem under open-set protocol.

TL;DR: This study demonstrates how to adjust the nominal NBT composition for dielectric-based applications and gives NBT-based materials an unexpected role as a completely new family of oxide ion conductors with potential applications in intermediate-temperature SOFCs and opens up a new direction to design oxide ions conductors in perovskite oxides.

TL;DR: A novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data and an optimal selection strategy for detecting the best drivable region is presented.

TL;DR: In this article, a unified and interpretable end-to-end system for both speaker and language recognition is developed, where the encoding layer plays a role in aggregating the variable-length input sequence into an utterance level representation.