no code implementations • 4 Dec 2023 • Hanyu Wang, Pengxiang Wu, Kevin Dela Rosa, Chen Wang, Abhinav Shrivastava
Compared to IIST, such approaches provide more flexibility with text-specified styles, which are useful in scenarios where the style is hard to define with reference images.
no code implementations • 25 Sep 2023 • Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen
Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high.
1 code implementation • CVPR 2023 • Shishira R Maiya, Sharath Girish, Max Ehrlich, Hanyu Wang, Kwot Sin Lee, Patrick Poirson, Pengxiang Wu, Chen Wang, Abhinav Shrivastava
This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video.
2 code implementations • NeurIPS 2021 • Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng, Wenjun Zhang
Our approach is validated on V2X-Sim 1. 0, a large-scale multi-agent perception dataset that we synthesized using CARLA and SUMO co-simulation.
Ranked #4 on 3D Object Detection on V2XSet
no code implementations • 29 Sep 2021 • Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Yuriy Nevmyvaka, Chao Chen
Learning and decision making in domains with naturally high noise-to-signal ratios – such as Finance or Public Health – can be challenging and yet extremely important.
1 code implementation • 14 Jun 2021 • Jingru Yi, Pengxiang Wu, Hui Tang, Bo Liu, Qiaoying Huang, Hui Qu, Lianyi Han, Wei Fan, Daniel J. Hoeppner, Dimitris N. Metaxas
To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
1 code implementation • ICLR 2021 • Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen
Label noise is frequently observed in real-world large-scale datasets.
Ranked #12 on Learning with noisy labels on ANIMAL
1 code implementation • NeurIPS 2020 • Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris Metaxas, Chao Chen
Noisy labels can impair the performance of deep neural networks.
3 code implementations • ICML 2020 • Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, Chao Chen
To be robust against label noise, many successful methods rely on the noisy classifiers (i. e., models trained on the noisy training data) to determine whether a label is trustworthy.
Ranked #40 on Image Classification on Clothing1M
no code implementations • 19 Aug 2020 • Qiaoying Huang, Dong Yang, Yikun Xian, Pengxiang Wu, Jingru Yi, Hui Qu, Dimitris Metaxas
The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary complex neural network architectures.
no code implementations • 18 Aug 2020 • Meng Ye, Qiaoying Huang, Dong Yang, Pengxiang Wu, Jingru Yi, Leon Axel, Dimitris Metaxas
The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation.
1 code implementation • 17 Aug 2020 • Jingru Yi, Pengxiang Wu, Bo Liu, Qiaoying Huang, Hui Qu, Dimitris Metaxas
To address this issue, in this work we extend the horizontal keypoint-based object detector to the oriented object detection task.
Ranked #11 on Oriented Object Detection on DOTA 1.0
no code implementations • 10 Jul 2020 • Hui Qu, Pengxiang Wu, Qiaoying Huang, Jingru Yi, Zhennan Yan, Kang Li, Gregory M. Riedlinger, Subhajyoti De, Shaoting Zhang, Dimitris N. Metaxas
To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i. e., only a small portion of nuclei locations in each image are labeled.
2 code implementations • CVPR 2020 • Pengxiang Wu, Siheng Chen, Dimitris Metaxas
The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion.
1 code implementation • 9 Jan 2020 • Jingru Yi, Pengxiang Wu, Qiaoying Huang, Hui Qu, Dimitris N. Metaxas
The comparison results demonstrate the merits of our method in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images.
no code implementations • 25 Nov 2019 • Pengxiang Wu, Chao Chen, Jingru Yi, Dimitris Metaxas
The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation.
no code implementations • 20 Nov 2019 • Jingru Yi, Hui Tang, Pengxiang Wu, Bo Liu, Daniel J. Hoeppner, Dimitris N. Metaxas, Lianyi Han, Wei Fan
Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects.
1 code implementation • 27 Sep 2019 • Jingru Yi, Pengxiang Wu, Dimitris N. Metaxas
This paper proposes a new deep neural network for object detection.
no code implementations • 25 Sep 2019 • Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, Chao Chen
To collect large scale annotated data, it is inevitable to introduce label noise, i. e., incorrect class labels.
1 code implementation • 22 Jul 2019 • Jingru Yi, Pengxiang Wu, Qiaoying Huang, Hui Qu, Bo Liu, Daniel J. Hoeppner, Dimitris N. Metaxas
In this paper, we propose a new box-based cell instance segmentation method.
1 code implementation • 18 Oct 2018 • Qiaoying Huang, Dong Yang, Pengxiang Wu, Hui Qu, Jingru Yi, Dimitris Metaxas
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate.