no code implementations • 17 May 2024 • Kyle Gao, Dening Lu, Hongjie He, Linlin Xu, Jonathan Li
3D urban scene reconstruction and modelling is a crucial research area in remote sensing with numerous applications in academia, commerce, industry, and administration.
no code implementations • 2 May 2024 • Zhengsen Xu, Jonathan Li, Linlin Xu
In this technical review, we describe the options for independent variables, data processing techniques, models, independent variables collinearity and importance estimation methods, and model performance evaluation metrics.
no code implementations • 20 Apr 2024 • Ling Yue, Jonathan Li, Md Zabirul Islam, Bolun Xia, Tianfan Fu, Jintai Chen
The clinical trial process, also known as drug development, is an indispensable step toward the development of new treatments.
no code implementations • 18 Apr 2024 • Rohan Bhambhoria, Samuel Dahan, Jonathan Li, Xiaodan Zhu
This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients.
no code implementations • 8 Apr 2024 • Zoltan Csaki, Bo Li, Jonathan Li, Qiantong Xu, Pian Pawakapan, Leon Zhang, Yun Du, Hengyu Zhao, Changran Hu, Urmish Thakker
In this paper, we present a comprehensive investigation into the adaptation of LLMs to new languages.
1 code implementation • 31 Aug 2023 • Binjie Chen, Yunzhou Xia, Yu Zang, Cheng Wang, Jonathan Li
In this work, we propose to decouple the explicit modelling of spatial relations from local aggregation.
Semantic Segmentation Supervised Only 3D Point Cloud Classification
1 code implementation • 29 Jun 2023 • Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, Jonathan Li, José Marcato Junior
Segmentation is an essential step for remote sensing image processing.
1 code implementation • 8 Jun 2023 • Ali Jamali, Swalpa Kumar Roy, Jonathan Li, Pedram Ghamisi
In the domain of remote sensing image interpretation, road extraction from high-resolution aerial imagery has already been a hot research topic.
no code implementations • 30 May 2023 • Dening Lu, Jun Zhou, Kyle Yilin Gao, Dilong Li, Jing Du, Linlin Xu, Jonathan Li
Specifically, we propose novel semantic feature-based dynamic sampling and clustering methods in the encoder, which enables the model to be aware of local semantic homogeneity for local feature aggregation.
1 code implementation • 20 May 2023 • Jonathan Li, Will Aitken, Rohan Bhambhoria, Xiaodan Zhu
Parameter-efficient tuning aims to mitigate the large memory requirements of adapting pretrained language models for downstream tasks.
no code implementations • 4 May 2023 • Diogo Nunes Goncalves, Jose Marcato Junior, Pedro Zamboni, Hemerson Pistori, Jonathan Li, Keiller Nogueira, Wesley Nunes Goncalves
After the backbone feature extraction, two feature maps are learned for each task.
no code implementations • 25 Oct 2022 • Jonathan Li, Rohan Bhambhoria, Xiaodan Zhu
Unfortunately, parameter-efficient methods perform poorly with small amounts of data, which are common in the legal domain (where data labelling costs are high).
no code implementations • 1 Oct 2022 • Kyle Gao, Yina Gao, Hongjie He, Dening Lu, Linlin Xu, Jonathan Li
Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis.
no code implementations • 21 Sep 2022 • Dening Lu, Kyle Gao, Qian Xie, Linlin Xu, Jonathan Li
This paper presents a novel point cloud representational learning network, called 3D Dual Self-attention Global Local (GLocal) Transformer Network (3DGTN), for improved feature learning in both classification and segmentation tasks, with the following key contributions.
no code implementations • 3 Jun 2022 • Qiqi Ding, Peng Li, Xuefeng Yan, Ding Shi, Luming Liang, Weiming Wang, Haoran Xie, Jonathan Li, Mingqiang Wei
To our knowledge, RSOD is the first quantitatively evaluated and graded snowy OD dataset.
no code implementations • 16 May 2022 • Dening Lu, Qian Xie, Mingqiang Wei, Kyle Gao, Linlin Xu, Jonathan Li
To demonstrate the superiority of Transformers in point cloud analysis, we present comprehensive comparisons of various Transformer-based methods for classification, segmentation, and object detection.
1 code implementation • 2 Mar 2022 • Dening Lu, Qian Xie, Linlin Xu, Jonathan Li
This paper presents a novel hierarchical framework that incorporates convolution with Transformer for point cloud classification, named 3D Convolution-Transformer Network (3DCTN), to combine the strong and efficient local feature learning ability of convolution with the remarkable global context modeling capability of Transformer.
no code implementations • 9 Jan 2022 • Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li, Yulan Guo
In this paper, we introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision.
no code implementations • 29 Sep 2021 • Saeed Marzban, Erick Delage, Jonathan Li
Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider coherent risk measures.
no code implementations • 29 Sep 2021 • Saeed Marzban, Erick Delage, Jonathan Li
In this paper, we present a new portfolio policy network architecture for deep reinforcement learning (DRL) that can exploit more effectively cross-asset dependency information and achieve better performance than state-of-the-art architectures.
no code implementations • 15 Jun 2021 • Sen Deng, Yidan Feng, Mingqiang Wei, Haoran Xie, Yiping Chen, Jonathan Li, Xiao-Ping Zhang, Jing Qin
Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image.
no code implementations • 18 Mar 2021 • Sarah Narges Fatholahi, Hongjie He, Lanying Wang, Awase Syed, Jonathan Li
Surface displacements associated with the average subsidence due to hydrocarbon exploitation in southwest of Iran which has a long history in oil production, can lead to significant damages to surface and subsurface structures, and requires serious consideration.
no code implementations • 16 Mar 2021 • Hongjie He, Ke Yang, Yuwei Cai, Zijian Jiang, Qiutong Yu, Kun Zhao, JunBo Wang, Sarah Narges Fatholahi, Yan Liu, Hasti Andon Petrosians, Bingxu Hu, Liyuan Qing, Zhehan Zhang, Hongzhang Xu, Siyu Li, Kyle Gao, Linlin Xu, Jonathan Li
Building rooftop data are of importance in several urban applications and in natural disaster management.
no code implementations • 8 Feb 2021 • Mauro dos Santos de Arruda, Lucas Prado Osco, Plabiany Rodrigo Acosta, Diogo Nunes Gonçalves, José Marcato Junior, Ana Paula Marques Ramos, Edson Takashi Matsubara, Zhipeng Luo, Jonathan Li, Jonathan de Andrade Silva, Wesley Nunes Gonçalves
For the tree dataset, our method returned a mean absolute error (MAE) of 2. 05, a root-mean-squared error (RMSE) of 2. 87 and a coefficient of determination (R$^2$) of 0. 986.
no code implementations • 8 Feb 2021 • Patrik Olã Bressan, José Marcato Junior, José Augusto Correa Martins, Diogo Nunes Gonçalves, Daniel Matte Freitas, Lucas Prado Osco, Jonathan de Andrade Silva, Zhipeng Luo, Jonathan Li, Raymundo Cordero Garcia, Wesley Nunes Gonçalves
Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks.
no code implementations • 5 Feb 2021 • Diogo Nunes Gonçalves, Mauro dos Santos de Arruda, Hemerson Pistori, Vanessa Jordão Marcato Fernandes, Ana Paula Marques Ramos, Danielle Elis Garcia Furuya, Lucas Prado Osco, Hongjie He, Jonathan Li, José Marcato Junior, Wesley Nunes Gonçalves
This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) for the displacement vectors between the plants.
1 code implementation • 24 Jan 2021 • Nannan Qin, Weikai Tan, Lingfei Ma, Dedong Zhang, Jonathan Li
Ground filtering has remained a widely studied but incompletely resolved bottleneck for decades in the automatic generation of high-precision digital elevation model, due to the dramatic changes of topography and the complex structures of objects.
no code implementations • 22 Jan 2021 • Lucas Prado Osco, José Marcato Junior, Ana Paula Marques Ramos, Lúcio André de Castro Jorge, Sarah Narges Fatholahi, Jonathan de Andrade Silva, Edson Takashi Matsubara, Hemerson Pistori, Wesley Nunes Gonçalves, Jonathan Li
In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields.
no code implementations • 31 Dec 2020 • Lucas Prado Osco, Mauro dos Santos de Arruda, Diogo Nunes Gonçalves, Alexandre Dias, Juliana Batistoti, Mauricio de Souza, Felipe David Georges Gomes, Ana Paula Marques Ramos, Lúcio André de Castro Jorge, Veraldo Liesenberg, Jonathan Li, Lingfei Ma, José Marcato Junior, Wesley Nunes Gonçalves
In the corn plantation datasets (with both growth phases, young and mature), our approach returned a mean absolute error (MAE) of 6. 224 plants per image patch, a mean relative error (MRE) of 0. 1038, precision and recall values of 0. 856, and 0. 905, respectively, and an F-measure equal to 0. 876.
no code implementations • 21 Oct 2020 • Weikai Tan, Dedong Zhang, Lingfei Ma, Ying Li, Lanying Wang, Jonathan Li
Stack interchanges are essential components of transportation systems.
no code implementations • 2 Jul 2020 • Zichang Liu, Zhaozhuo Xu, Alan Ji, Jonathan Li, Beidi Chen, Anshumali Shrivastava
Efficient inference for wide output layers (WOLs) is an essential yet challenging task in large scale machine learning.
no code implementations • 20 May 2020 • Ying Li, Lingfei Ma, Zilong Zhong, Fei Liu, Dongpu Cao, Jonathan Li, Michael A. Chapman
In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification.
1 code implementation • 18 Mar 2020 • Weikai Tan, Nannan Qin, Lingfei Ma, Ying Li, Jing Du, Guorong Cai, Ke Yang, Jonathan Li
Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping.
no code implementations • 29 Oct 2019 • Mengge Chen, Jonathan Li
As one of the most destructive disasters in the world, earthquake causes death, injuries, destruction and enormous damage to the affected area.
no code implementations • 9 Oct 2019 • Bowen Yang, Jian Zhang, Jonathan Li, Christopher Ré, Christopher R. Aberger, Christopher De Sa
Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware utilization.
1 code implementation • CVPR 2020 • Zilong Zhong, Zhong Qiu Lin, Rene Bidart, Xiaodan Hu, Ibrahim Ben Daya, Zhifeng Li, Wei-Shi Zheng, Jonathan Li, Alexander Wong
The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features.
Ranked #6 on Semantic Segmentation on PASCAL VOC 2012 test
1 code implementation • ISPRS Journal of Photogrammetry and Remote Sensing 2019 • Yangbin Lin, Cheng Wang, Dawei Zhai, Wei Li, Jonathan Li
In this paper, we present a simple but effective supervoxel segmentation method for point clouds, which formalizes supervoxel segmentation as a subset selection problem.
no code implementations • 20 Jun 2019 • Jonathan Li, Rongren Wu, Yiping Chen, Qing Zhu, Zhipeng Luo, Cheng Wang
Second, to accurately extract trees from all point clouds, we propose a 3D deep learning network, PointNLM, to semantically segment tree crowns.
1 code implementation • CVPR 2019 • Xuelun Shen, Cheng Wang, Xin Li, Zenglei Yu, Jonathan Li, Chenglu Wen, Ming Cheng, Zijian He
This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images.
no code implementations • 20 May 2019 • Yangbin Lin, Jialian Li, Cheng Wang, Zhonggui Chen, Zongyue Wang, Jonathan Li
Man-made environments typically comprise planar structures that exhibit numerous geometric relationships, such as parallelism, coplanarity, and orthogonality.
no code implementations • 12 May 2019 • Zilong Zhong, Jonathan Li, David A. Clausi, Alexander Wong
In this paper, we address the hyperspectral image (HSI) classification task with a generative adversarial network and conditional random field (GAN-CRF) -based framework, which integrates a semi-supervised deep learning and a probabilistic graphical model, and make three contributions.
no code implementations • CVPR 2019 • Qing Li, Shaoyang Chen, Cheng Wang, Xin Li, Chenglu Wen, Ming Cheng, Jonathan Li
We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation.
no code implementations • 22 Nov 2018 • Shenlong Lou, Yan Luo, Qiancong Fan, Feng Chen, Yiping Chen, Cheng Wang, Jonathan Li
It is widely recognized that the deeper networks or networks with more feature maps have better performance.
no code implementations • 27 Sep 2018 • Peize Zhao, Danfeng Cai, Shaokun Zhang, Feng Chen, Zhemin Zhang, Cheng Wang, Jonathan Li
To forecast the traffic flow across a wide area and overcome the mentioned challenges, we design and propose a promising forecasting model called Layerwise Recurrent Autoencoder (LRA), in which a three-layer stacked autoencoder (SAE) architecture is used to obtain temporal traffic correlations and a recurrent neural networks (RNNs) model for prediction.
no code implementations • 22 Sep 2018 • Zongliang Zhang, Hongbin Zeng, Jonathan Li, Yiping Chen, Chenhui Yang, Cheng Wang
This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e. g., laser scanned point clouds).
no code implementations • CVPR 2018 • Yiping Chen, Jingkang Wang, Jonathan Li, Cewu Lu, Zhipeng Luo, Han Xue, Cheng Wang
Learning autonomous-driving policies is one of the most challenging but promising tasks for computer vision.
no code implementations • 10 Feb 2018 • Zilong Zhong, Jonathan Li
High spectral dimensionality and the shortage of annotations make hyperspectral image (HSI) classification a challenging problem.
no code implementations • 10 Oct 2017 • Shanxin Zhang, Cheng Wang, Zhuang Yang, Chenglu Wen, Jonathan Li, Chenhui Yang
Then, based on the VEM, we proposed the concept of the Visual Recognizability Field (VRF) to reflect the visual recognizability distribution in 3D space and established a Visual Recognizability Evaluation Model (VREM) to measure a traffic sign visual recognizability for a given viewpoint.
1 code implementation • 17 Oct 2016 • Zongliang Zhang, Jonathan Li, Yulan Guo, Yangbin Lin, Ming Cheng, Cheng Wang
However, most geometric model fitting methods are unable to fit an arbitrary geometric model (e. g. a surface with holes) to incomplete data, due to that the similarity metrics used in these methods are unable to measure the rigid partial similarity between arbitrary models.
no code implementations • 29 Dec 2015 • Da-Han Wang, Hanzi Wang, Dong Zhang, Jonathan Li, David Zhang
For character detection, we use the HSC features instead of using the Histograms of Oriented Gradients (HOG) features.