Incremental Few-Shot Learning via Implanting and Compressing

no code implementations • 19 Mar 2022 • Yiting Li, Haiyue Zhu, Xijia Feng, Zilong Cheng, Jun Ma, Cheng Xiang, Prahlad Vadakkepat, Tong Heng Lee

Specifically, in the \textbf{Implanting} step, we propose to mimic the data distribution of novel classes with the assistance of data-abundant base set, so that a model could learn semantically-rich features that are beneficial for discriminating between the base and other unseen classes.

Few-Shot Learning Image Classification +2

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

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