185 citations · 363 across the 17 of their papers we have counts for
17 papers
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling
Shishira R Maiya, Sharath Girish, Max Ehrlich +6
Implicit Neural Representations (INR) have recently shown to be powerful tool for high-quality video compression. However, existing works are limiting as they do not explicitly exp…
Learning Distilled Collaboration Graph for Multi-Agent Perception
Yiming Li, Shunli Ren, Pengxiang Wu +3
To promote better performance-bandwidth trade-off for multi-agent perception, we propose a novel distilled collaboration graph (DiscoGraph) to model trainable, pose-aware, and adap…
Learning with Feature-Dependent Label Noise: A Progressive Approach
Yikai Zhang, Songzhu Zheng, Pengxiang Wu +2
Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing…
A Topological Filter for Learning with Label Noise
Pengxiang Wu, Songzhu Zheng, Mayank Goswami +2
Noisy labels can impair the performance of deep neural networks. To tackle this problem, in this paper, we propose a new method for filtering label noise. Unlike most existing meth…
Error-Bounded Correction of Noisy Labels
Songzhu Zheng, Pengxiang Wu, Aman Goswami +3
To collect large scale annotated data, it is inevitable to introduce label noise, i.e., incorrect class labels. To be robust against label noise, many successful methods rely on th…
Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
Jingru Yi, Pengxiang Wu, Bo Liu +3
Oriented object detection in aerial images is a challenging task as the objects in aerial images are displayed in arbitrary directions and are usually densely packed. Current orien…
Enhanced MRI Reconstruction Network using Neural Architecture Search
Qiaoying Huang, Dong Yang, Yikun Xian +4
The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary comple…
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
Meng Ye, Qiaoying Huang, Dong Yang +4
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.…
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
Hui Qu, Pengxiang Wu, Qiaoying Huang +7
Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise a…
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps
Pengxiang Wu, Siheng Chen, Dimitris Metaxas
The ability to reliably perceive the environmental states, particularly the existence of objects and their motion behavior, is crucial for autonomous driving. In this work, we prop…
Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter
Yingheng Tang, Keisuke Kojima, Toshiaki Koike-Akino +6
We propose a novel Conditional Variational Autoencoder (CVAE) model, enhanced with adversarial censoring and active learning, for the generation of 550 nm broad bandwidth (1250 nm…
Vertebra-Focused Landmark Detection for Scoliosis Assessment
Jingru Yi, Pengxiang Wu, Qiaoying Huang +2
Adolescent idiopathic scoliosis (AIS) is a lifetime disease that arises in children. Accurate estimation of Cobb angles of the scoliosis is essential for clinicians to make diagnos…
Point Cloud Processing via Recurrent Set Encoding
Pengxiang Wu, Chao Chen, Jingru Yi +1
We present a new permutation-invariant network for 3D point cloud processing. Our network is composed of a recurrent set encoder and a convolutional feature aggregator. Given an un…
Object-Guided Instance Segmentation for Biological Images
Jingru Yi, Hui Tang, Pengxiang Wu +5
Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the ob…
ASSD: Attentive Single Shot Multibox Detector
Jingru Yi, Pengxiang Wu, Dimitris N. Metaxas
This paper proposes a new deep neural network for object detection. The proposed network, termed ASSD, builds feature relations in the spatial space of the feature map. With the gl…
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes
Jingru Yi, Pengxiang Wu, Qiaoying Huang +4
Most existing methods handle cell instance segmentation problems directly without relying on additional detection boxes. These methods generally fails to separate touching cells du…
MRI Reconstruction via Cascaded Channel-wise Attention Network
Qiaoying Huang, Dong Yang, Pengxiang Wu +3
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can practically benefit patient due to reduced time of MRI scan, but it i…