Hao Chen

Orcid: 0000-0003-4417-614X

Affiliations:
  • University of Adelaide, School of Computer Science, SA, Australia


According to our database1, Hao Chen authored at least 32 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Instance and Panoptic Segmentation Using Conditional Convolutions.
IEEE Trans. Pattern Anal. Mach. Intell., 2023

A Dynamic Feature Interaction Framework for Multi-task Visual Perception.
Int. J. Comput. Vis., 2023

HumanRecon: Neural Reconstruction of Dynamic Human Using Geometric Cues and Physical Priors.
CoRR, 2023

FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models.
CoRR, 2023

Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
FCOS: A Simple and Strong Anchor-Free Object Detector.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

ABCNet v2: Adaptive Bezier-Curve Network for Real-Time End-to-End Text Spotting.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration.
Int. J. Comput. Vis., 2022

Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation.
CoRR, 2022

2021
Learning deep part-aware embedding for person retrieval.
Pattern Recognit., 2021

NAS-FCOS: Efficient Search for Object Detection Architectures.
Int. J. Comput. Vis., 2021

Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection.
Int. J. Comput. Vis., 2021

Deep Learning for Source Code Modeling and Generation: Models, Applications, and Challenges.
ACM Comput. Surv., 2021

Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline.
CoRR, 2021

BoxInst: High-Performance Instance Segmentation With Box Annotations.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Generic Perceptual Loss for Modeling Structured Output Dependencies.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization.
IEEE Trans. Pattern Anal. Mach. Intell., 2020

Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions.
CoRR, 2020

Architecture Search of Dynamic Cells for Semantic Video Segmentation.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020

Conditional Convolutions for Instance Segmentation.
Proceedings of the Computer Vision - ECCV 2020, 2020

Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

NAS-FCOS: Fast Neural Architecture Search for Object Detection.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Exploring the Capacity of Sequential-free Box Discretization Network for Omnidirectional Scene Text Detection.
CoRR, 2019

DirectPose: Direct End-to-End Multi-Person Pose Estimation.
CoRR, 2019

IR-NAS: Neural Architecture Search for Image Restoration.
CoRR, 2019

Training Compact Neural Networks via Auxiliary Overparameterization.
CoRR, 2019

NAS-FCOS: Fast Neural Architecture Search for Object Detection.
CoRR, 2019

Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

FCOS: Fully Convolutional One-Stage Object Detection.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019


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