Chao Li

Affiliations:
  • Baidu Inc., Department of Computer Vision, Beijing, China
  • University of Queensland, Australia (PhD 2018)


According to our database1, Chao Li authored at least 19 papers between 2019 and 2023.

Collaborative distances:

Timeline

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Bibliography

2023
Generative Action Description Prompts for Skeleton-based Action Recognition.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023


NTIRE 2023 Challenge on HR Depth from Images of Specular and Transparent Surfaces.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Hypergraph Transformer for Skeleton-based Action Recognition.
CoRR, 2022

MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition.
CoRR, 2022

Language Supervised Training for Skeleton-based Action Recognition.
CoRR, 2022

Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition.
Proceedings of the Computer Vision - ECCV 2022, 2022

CDAD: A Common Daily Action Dataset with Collected Hard Negative Samples.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

SP-ViT: Learning 2D Spatial Priors for Vision Transformers.
Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021
Image Inpainting by End-to-End Cascaded Refinement With Mask Awareness.
IEEE Trans. Image Process., 2021

2020
Deep Concept-wise Temporal Convolutional Networks for Action Localization.
Proceedings of the MM '20: The 28th ACM International Conference on Multimedia, 2020



NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation.
CoRR, 2019

Image Inpainting With Learnable Bidirectional Attention Maps.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019


Adapting Image Super-Resolution State-Of-The-Arts and Learning Multi-Model Ensemble for Video Super-Resolution.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019



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