National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Temperature sensor based on surface plasmon resonance within selectively coated photonic crystal fiber

Abstract: We demonstrate a temperature sensor based on surface plasmon resonances supported by photonic crystal fibers (PCFs). Within the PCF, to enhance the sensitivity of the sensor, the air holes of the second layer are filled with a large thermo-optic coefficient liquid and some of those air holes are selectively coated with metal. Temperature variations will induce changes of coupling efficiencies between the fundamental core mode and the plasmonic mode, thus leading to different loss spectra that will be recorded. In this paper, variations of the dielectric constants of all components, including the metal, the filled liquid, and the fused silica, are considered. We conduct numerical calculations to analyze the mode profile and evaluate the power loss, demonstrating a temperature sensitivity as high as 720 pm/°C.

Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing

Abstract: Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc. To this end, models need to comprehensively perceive the semantic information and the differences between instances in a multi-human image, which is recently defined as the multi-human parsing task. In this paper, we present a new large-scale database "Multi-Human Parsing (MHP)" for algorithm development and evaluation, and advances the state-of-the-art in understanding humans in crowded scenes. MHP contains 25,403 elaborately annotated images with 58 fine-grained semantic category labels, involving 2-26 persons per image and captured in real-world scenes from various viewpoints, poses, occlusion, interactions and background. We further propose a novel deep Nested Adversarial Network (NAN) model for multi-human parsing. NAN consists of three Generative Adversarial Network (GAN)-like sub-nets, respectively performing semantic saliency prediction, instance-agnostic parsing and instance-aware clustering. These sub-nets form a nested structure and are carefully designed to learn jointly in an end-to-end way. NAN consistently outperforms existing state-of-the-art solutions on our MHP and several other datasets, and serves as a strong baseline to drive the future research for multi-human parsing.