Search Results for author:
Found 21 papers, 12 papers with code
Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
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
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps
The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion.
Learning Distilled Collaboration Graph for Multi-Agent Perception
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
Vertebra-Focused Landmark Detection for Scoliosis Assessment
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.
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes
In this paper, we propose a new box-based cell instance segmentation method.
Error-Bounded Correction of Noisy Labels
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
Learning with Feature-Dependent Label Noise: A Progressive Approach
Label noise is frequently observed in real-world large-scale datasets.
Ranked #12 on
Learning with noisy labels
on ANIMAL
ASSD: Attentive Single Shot Multibox Detector
This paper proposes a new deep neural network for object detection.
A Topological Filter for Learning with Label Noise
Noisy labels can impair the performance of deep neural networks.
MRI Reconstruction via Cascaded Channel-wise Attention Network
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate.
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling
This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video.
Object-Guided Instance Segmentation for Biological Images
Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects.
Point Cloud Processing via Recurrent Set Encoding
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.
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
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.
Enhanced MRI Reconstruction Network using Neural Architecture Search
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.
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
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.
Learning to Abstain in the Presence of Uninformative Data
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.
Label Cleaning with Likelihood Ratio Test
To collect large scale annotated data, it is inevitable to introduce label noise, i. e., incorrect class labels.
Learning to Abstain From Uninformative Data
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.
Multimodality-guided Image Style Transfer using Cross-modal GAN Inversion
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.
