Towards Generalized and Incremental Few-Shot Object Detection

no code implementations • 23 Sep 2021 • Yiting Li, Haiyue Zhu, Jun Ma, Chek Sing Teo, Cheng Xiang, Prahlad Vadakkepat, Tong Heng Lee

We conduct experiments on both Pascal VOC and MS-COCO, which demonstrate that our method can effectively solve the problem of incremental few-shot detection and significantly improve the detection accuracy on both base and novel classes.

Autonomous Driving Continual Learning +4

Few-Shot Object Detection via Classification Refinement and Distractor Retreatment

no code implementations • CVPR 2021 • Yiting Li, Haiyue Zhu, Yu Cheng, Wenxin Wang, Chek Sing Teo, Cheng Xiang, Prahlad Vadakkepat, Tong Heng Lee

The failure modes of FSOD are investigated that the performance degradation is mainly due to the classification incapability (false positives), which motivates us to address it from a novel aspect of hard example mining.

Classification Few-Shot Object Detection +1

Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation

no code implementations • 20 Aug 2020 • Haiyue Zhu, Yiting Li, Fengjun Bai, Wenjie Chen, Xiaocong Li, Jun Ma, Chek Sing Teo, Pey Yuen Tao, Wei. Lin

The proposed grasping detection network specially provides a prediction uncertainty estimation mechanism by leveraging on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning utilizes such uncertainty information to emphasizing the consistency loss only for those unlabelled data with high confidence, which we referred it as the confidence-driven mean teacher.

Domain Adaptation Semi-supervised Domain Adaptation

Incremental Few-Shot Object Detection for Robotics

no code implementations • 6 May 2020 • Yiting Li, Haiyue Zhu, Sichao Tian, Fan Feng, Jun Ma, Chek Sing Teo, Cheng Xiang, Prahlad Vadakkepat, Tong Heng Lee

Incremental few-shot learning is highly expected for practical robotics applications.

Classification Continual Learning +6