20 citations · 35 across the 5 of their papers we have counts for
8 papers
What Makes Convolutional Models Great on Long Sequence Modeling?
Yuhong Li, Tianle Cai, Yi Zhang +2
Convolutional models have been widely used in multiple domains. However, most existing models only use local convolution, making the model unable to handle long-range dependency ef…
Nuisance-Label Supervision: Robustness Improvement by Free Labels
Xinyue Wei, Weichao Qiu, Yi Zhang +2
In this paper, we present a Nuisance-label Supervision (NLS) module, which can make models more robust to nuisance factor variations. Nuisance factors are those irrelevant to a tas…
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning
Yi Zhang, Sheng Huang, Xi Peng +1
Data augmentation is a powerful technique for improving the performance of the few-shot classification task. It generates more samples as supplements, and then this task can be tra…
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
Yingda Xia, Yi Zhang, Fengze Liu +2
The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of…
Identity Preserve Transform: Understand What Activity Classification Models Have Learnt
Jialing Lyu, Weichao Qiu, Xinyue Wei +3
Activity classification has observed great success recently. The performance on small dataset is almost saturated and people are moving towards larger datasets. What leads to the p…
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition
Yi Zhang, Xinyue Wei, Weichao Qiu +3
Despite the rapid growth in datasets for video activity, stable robust activity recognition with neural networks remains challenging. This is in large part due to the explosion of…