1 code implementation • 11 Jan 2024 • Rouwan Wu, Xiaoya Cheng, Juelin Zhu, Xuxiang Liu, Maojun Zhang, Shen Yan
Despite significant progress in global localization of Unmanned Aerial Vehicles (UAVs) in GPS-denied environments, existing methods remain constrained by the availability of datasets.
no code implementations • 23 Sep 2023 • Wenjie Liu, Peipei Gao, Yuxiang Wang, Wenbin Yu, Maojun Zhang
In order to solve the problem of non-ideal training sets (i. e., the less-complete or over-complete sets) and implement one-iteration learning, a novel efficient quantum perceptron algorithm based on unitary weights is proposed, where the singular value decomposition of the total weight matrix from the training set is calculated to make the weight matrix to be unitary.
no code implementations • CVPR 2023 • Shen Yan, Yu Liu, Long Wang, Zehong Shen, Zhen Peng, Haomin Liu, Maojun Zhang, Guofeng Zhang, Xiaowei Zhou
Despite the remarkable advances in image matching and pose estimation, image-based localization of a camera in a temporally-varying outdoor environment is still a challenging problem due to huge appearance disparity between query and reference images caused by illumination, seasonal and structural changes.
no code implementations • 13 Feb 2023 • Shen Yan, Xiaoya Cheng, Yuxiang Liu, Juelin Zhu, Rouwan Wu, Yu Liu, Maojun Zhang
Despite the significant progress in 6-DoF visual localization, researchers are mostly driven by ground-level benchmarks.
no code implementations • ICCV 2023 • Long Wang, Shen Yan, Jianan Zhen, Yu Liu, Maojun Zhang, Guofeng Zhang, Xiaowei Zhou
Specifically, given an initial pose, we project the object model to the image plane to obtain the initial contour and use a lightweight network to predict how the contour should move to match the true object boundary, which provides the gradients to optimize the object pose.
no code implementations • 24 Jul 2021 • Maojun Zhang, Guangxu Zhu, Shuai Wang, Jiamo Jiang, Caijun Zhong, Shuguang Cui
Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem.
no code implementations • 17 Sep 2020 • Shen Yan, Yang Pen, Shiming Lai, Yu Liu, Maojun Zhang
Conventional image retrieval techniques for Structure-from-Motion (SfM) suffer from the limit of effectively recognizing repetitive patterns and cannot guarantee to create just enough match pairs with high precision and high recall.
no code implementations • 13 Jul 2020 • Shunjie Dong, Jinlong Zhao, Maojun Zhang, Zhengxue Shi, Jianing Deng, Yiyu Shi, Mei Tian, Cheng Zhuo
In this paper, we propose a novel Deformable U-Net (DeU-Net) to fully exploit spatio-temporal information from 3D cardiac MRI video, including a Temporal Deformable Aggregation Module (TDAM) and a Deformable Global Position Attention (DGPA) network.
no code implementations • CVPR 2018 • Huaxin Xiao, Jiashi Feng, Guosheng Lin, Yu Liu, Maojun Zhang
In this paper, we propose a novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i. e., frame representation learning and segmentation refinement.
no code implementations • ECCV 2018 • Xiaoqing Yin, Xinchao Wang, Jun Yu, Maojun Zhang, Pascal Fua, DaCheng Tao
Images captured by fisheye lenses violate the pinhole camera assumption and suffer from distortions.
no code implementations • 13 Apr 2018 • Xiaoqing Yin, Xiyang Dai, Xinchao Wang, Maojun Zhang, DaCheng Tao, Larry Davis
In this paper, we propose the first dedicated end-to-end deep learning approach for motion boundary detection, which we term as MoBoNet.
no code implementations • 18 Nov 2017 • Huaxin Xiao, Yunchao Wei, Yu Liu, Maojun Zhang, Jiashi Feng
The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations.
no code implementations • 18 Aug 2017 • Huaxin Xiao, Jiashi Feng, Yunchao Wei, Maojun Zhang
Through visualizing the differences, we can interpret the capability of different deep neural networks based saliency detection models and demonstrate that our proposed model indeed uses more reasonable structure for salient object detection.