Chongruo Wu

Affiliations:
  • University of California, Davis, Department of Computer Science, CA, USA
  • SenseTime Group, Beijing, China


According to our database1, Chongruo Wu authored at least 13 papers between 2019 and 2021.

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

Timeline

Legend:

Book 
In proceedings 
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PhD thesis 
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Bibliography

2021
Predicting ASD diagnosis in children with synthetic and image-based eye gaze data.
Signal Process. Image Commun., 2021

A Unified Efficient Pyramid Transformer for Semantic Segmentation.
Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020
A Comprehensive Study of Deep Video Action Recognition.
CoRR, 2020

Improving Semantic Segmentation via Self-Training.
CoRR, 2020

A Tailored Pre-Training Model for Task-Oriented Dialog Generation.
CoRR, 2020

ResNeSt: Split-Attention Networks.
CoRR, 2020

Machine Learning Based Autism Spectrum Disorder Detection from Videos.
Proceedings of the 22nd IEEE International Conference on E-health Networking, 2020

2019
Recognizing road from satellite images by structured neural network.
Neurocomputing, 2019

Predicting Autism Diagnosis using Image with Fixations and Synthetic Saccade Patterns.
Proceedings of the IEEE International Conference on Multimedia & Expo Workshops, 2019

Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network.
Proceedings of the 7th International Conference on Learning Representations, 2019

Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-Scale Point Clouds.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019


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