36 citations · 60 across the 7 of their papers we have counts for
7 papers
One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching
Siyuan Yang, Jun Liu, Shijian Lu +2
One-shot skeleton action recognition, which aims to learn a skeleton action recognition model with a single training sample, has attracted increasing interest due to the challenge…
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization
Siyuan Yang, Jun Liu, Shijian Lu +3
3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large numbe…
Progressive Channel-Shrinking Network
Jianhong Pan, Siyuan Yang, Lin Geng Foo +4
Currently, salience-based channel pruning makes continuous breakthroughs in network compression. In the realization, the salience mechanism is used as a metric of channel salience…
Temporal Coherent Test-Time Optimization for Robust Video Classification
Chenyu Yi, Siyuan Yang, Yufei Wang +3
Deep neural networks are likely to fail when the test data is corrupted in real-world deployment (e.g., blur, weather, etc.). Test-time optimization is an effective way that adapts…
Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions
Chenyu Yi, Siyuan Yang, Haoliang Li +2
The state-of-the-art deep neural networks are vulnerable to common corruptions (e.g., input data degradations, distortions, and disturbances caused by weather changes, system error…
Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning
Siyuan Yang, Jun Liu, Shijian Lu +2
Skeleton-based human action recognition has attracted increasing attention in recent years. However, most of the existing works focus on supervised learning which requiring a large…
UG Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
Ye Yuan, Wenhan Yang, Wenqi Ren +3
The UG challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual…