no code implementations • 29 Mar 2024 • Muhammad Sakib Khan Inan, Kewen Liao, Haifeng Shen, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Ming Jian Tang
Internet of Things (IoT) sensor data or readings evince variations in timestamp range, sampling frequency, geographical location, unit of measurement, etc.
1 code implementation • 5 Feb 2024 • Sheng Luo, Wei Chen, Wanxin Tian, Rui Liu, Luanxuan Hou, Xiubao Zhang, Haifeng Shen, Ruiqi Wu, Shuyi Geng, Yi Zhou, Ling Shao, Yi Yang, Bojun Gao, Qun Li, Guobin Wu
Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems.
1 code implementation • 21 Mar 2022 • Zhibin Liao, Kewen Liao, Haifeng Shen, Marouska F. van Boxel, Jasper Prijs, Ruurd L. Jaarsma, Job N. Doornberg, Anton Van Den Hengel, Johan W. Verjans
Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems.
no code implementations • 1 Dec 2021 • Jie Zhu, Bo Peng, Wanqing Li, Haifeng Shen, Zhe Zhang, Jianjun Lei
It is built upon Transformer and is capable of extracting dense features with global context and 3D consistency, which are crucial to achieving reliable matching for MVS.
no code implementations • 4 Apr 2021 • Omid Tarkhaneh, Neda Alipour, Amirahmad Chapnevis, Haifeng Shen
This paper proposes a novel nature-inspired meta-heuristic algorithm called the Golden Tortoise Beetle Optimizer (GTBO) to solve optimization problems.
no code implementations • 26 Oct 2020 • Luanxuan Hou, Jie Cao, Yuan Zhao, Haifeng Shen, Jian Tang, Ran He
We propose a refinement stage for the pyramid features to further boost the accuracy of our network.
2 code implementations • ECCV 2020 • Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao, Haifeng Shen
Informative features play a crucial role in the single image super-resolution task.
Ranked #2 on Image Super-Resolution on Urban100 - 8x upscaling
no code implementations • CVPR 2020 • Xiehe Huang, Weihong Deng, Haifeng Shen, Xiubao Zhang, Jieping Ye
Deep learning technique has dramatically boosted the performance of face alignment algorithms.
Ranked #4 on Face Alignment on WFLW
no code implementations • ECCV 2020 • Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye
In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliary task.
no code implementations • 29 Mar 2020 • Zhenpeng Li, Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye
However, in practice the labeled data can come from multiple source domains with different distributions.
no code implementations • 17 Mar 2020 • Luanxuan Hou, Jie Cao, Yuan Zhao, Haifeng Shen, Yiping Meng, Ran He, Jieping Ye
At last, we proposed a differentiable auto data augmentation method to further improve estimation accuracy.
no code implementations • 25 Oct 2019 • Jian Li, Yan Wang, Xiubao Zhang, Weihong Deng, Haifeng Shen
In this paper, we train a validation classifier to normalize the decision threshold, which means that the result can be obtained directly without replacing the threshold.
no code implementations • 19 Apr 2019 • Siyang Sun, Yingjie Yin, Xingang Wang, De Xu, Yuan Zhao, Haifeng Shen
To address this problem, we propose a multiple receptive field and small-object-focusing weakly-supervised segmentation network (MRFSWSnet) to achieve fast object detection.
no code implementations • 24 Feb 2019 • Yiwei Zhang, Chunbiao Zhu, Ge Li, Yuan Zhao, Haifeng Shen
A fast and effective motion deblurring method has great application values in real life.
2 code implementations • NeurIPS 2018 • Binghui Chen, Weihong Deng, Haifeng Shen
Recently, learning discriminative features to improve the recognition performances gradually becomes the primary goal of deep learning, and numerous remarkable works have emerged.
no code implementations • 20 Nov 2018 • Wanxin Tian, Zixuan Wang, Haifeng Shen, Weihong Deng, Yiping Meng, Binghui Chen, Xiubao Zhang, Yuan Zhao, Xiehe Huang
We assume that problems inside are inadequate use of supervision information and imbalance between semantics and details at all level feature maps in CNN even with Feature Pyramid Networks (FPN).