D
Di Huang
Researcher at Beihang University
Publications - 182
Citations - 6798
Di Huang is an academic researcher from Beihang University. The author has contributed to research in topics: Facial recognition system & Feature extraction. The author has an hindex of 33, co-authored 171 publications receiving 4661 citations. Previous affiliations of Di Huang include University of Lyon & École centrale de Lyon.
Papers
More filters
Book ChapterDOI
Receptive Field Block Net for Accurate and Fast Object Detection
TL;DR: Zhang et al. as discussed by the authors proposed a novel Receptive Fields (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness.
Journal ArticleDOI
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
TL;DR: As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial imageAnalysis, are also highlighted.
Posted Content
Learning Spatial Fusion for Single-Shot Object Detection
TL;DR: This work proposes a novel and data driven strategy for pyramidal feature fusion, referred to as adaptively spatial feature fusion (ASFF), which learns the way to spatially filter conflictive information to suppress the inconsistency, thus improving the scale-invariance of features, and introduces nearly free inference overhead.
Proceedings ArticleDOI
Adaptive NMS: Refining Pedestrian Detection in a Crowd
TL;DR: This paper proposes adaptive-NMS, which applies a dynamic suppression threshold to an instance, according to the target density, and designs an efficient subnetwork to learn density scores, which can be conveniently embedded into both the single-stage and two-stage detectors.
Proceedings ArticleDOI
Learning Face Age Progression: A Pyramid Architecture of GANs
TL;DR: In this article, a generative adversarial network based approach is proposed to model the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable.