E. Zhixuan Zeng

Orcid: 0000-0002-8034-596X

According to our database1, E. Zhixuan Zeng authored at least 11 papers between 2021 and 2024.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
MetaGraspNetV2: All-in-One Dataset Enabling Fast and Reliable Robotic Bin Picking via Object Relationship Reasoning and Dexterous Grasping.
IEEE Trans Autom. Sci. Eng., July, 2024

COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring.
Sensors, March, 2024

Decoding Diffusion: A Scalable Framework for Unsupervised Analysis of Latent Space Biases and Representations Using Natural Language Prompts.
CoRR, 2024

Understanding the Limitations of Diffusion Concept Algebra Through Food.
CoRR, 2024

2023
Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability.
CoRR, 2023

ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping.
CoRR, 2023

MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
MMRNet: Improving Reliability for Multimodal Computer Vision for Bin Picking via Multimodal Redundancy.
CoRR, 2022

COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning.
CoRR, 2022

MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis.
Proceedings of the 18th IEEE International Conference on Automation Science and Engineering, 2022

2021
MetaGraspNet: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis.
CoRR, 2021


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