no code implementations • 18 May 2023 • Liangchen Song, Liangliang Cao, Hongyu Xu, Kai Kang, Feng Tang, Junsong Yuan, Yang Zhao
The proposed framework consists of two significant components: Geometry Guided Diffusion and Mesh Optimization.
2 code implementations • ICCV 2019 • Zhen-Yu Wu, Karthik Suresh, Priya Narayanan, Hongyu Xu, Heesung Kwon, Zhangyang Wang
Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful.
no code implementations • 28 Nov 2018 • Hongyu Xu, Xutao Lv, Xiaoyu Wang, Zhou Ren, Navaneeth Bodla, Rama Chellappa
The deep regionlets framework consists of a region selection network and a deep regionlet learning module.
no code implementations • 20 Sep 2018 • Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng, Hongyu Xu, Joshua Gleason, Boyu Lu, Anirudh Nanduri, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa
We provide evaluation results of the proposed face detector on challenging unconstrained face detection datasets.
no code implementations • 16 Apr 2018 • Hongyu Xu, Zhangyang Wang, Haichuan Yang, Ding Liu, Ji Liu
The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process.
no code implementations • 12 Apr 2018 • Hongyu Xu, Jingjing Zheng, Azadeh Alavi, Rama Chellappa
These intermediate domains form a smooth path and bridge the gap between the source and target domains.
no code implementations • 3 Apr 2018 • Rajeev Ranjan, Ankan Bansal, Hongyu Xu, Swami Sankaranarayanan, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa
We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.
no code implementations • ECCV 2018 • Hongyu Xu, Xutao Lv, Xiaoyu Wang, Zhou Ren, Navaneeth Bodla, Rama Chellappa
The deep regionlets framework consists of a region selection network and a deep regionlet learning module.
no code implementations • 15 Feb 2017 • Navaneeth Bodla, Jingxiao Zheng, Hongyu Xu, Jun-Cheng Chen, Carlos Castillo, Rama Chellappa
Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets.