Cheng, Z., Chen, C., Zhao, Z., Qian, P., Li, X., & Yang, X. (2023). COCO-TEACH: A Contrastive Co-Teaching Network For Incremental 3D Object Detection. 2023 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip49359.2023.10222538
Abstract:
Deep learning (DL) models for 3D object detection from
point clouds have shown remarkable progress in various
autonomous perception scenarios. However, the issue of
catastrophic forgetting seriously hinders the deployment of
these models in real-world applications where new classes
are encountered over time. In order to address this issue,
we present the Contrastive Co-Teaching Network (COCO-
TEACH) framework for class-incremental 3D object de-
tection. Our proposed framework consists of two teacher
networks: a primary teacher network that detects old class
objects in new data and provides them with pseudo-labels
and an auxiliary teacher network that leverages the unla-
belled objects in new data. The two teacher models transfer
their learned knowledge to the target student model through
a class-aware consistency loss. To enhance this transfer, a
supervised contrastive loss is further incorporated into the
loss function. We evaluate the performance of our proposed
method against baseline methods through extensive experi-
ments on two benchmark datasets. The results show that our
proposed framework achieves state-of-the-art performance
on incremental 3D object detection
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - AI Singapore Programme
Grant Reference no. : AISG2-RP-2021-027