Towards Non Co-occurrence Incremental Object Detection with Unlabeled In-the-Wild Data

Author:

Dong NaORCID,Zhang Yongqiang,Ding Mingli,Lee Gim Hee

Funder

National Research Foundation Singapore

Ministry of Education - Singapore

Publisher

Springer Science and Business Media LLC

Reference37 articles.

1. Castro, F. M., Marín-Jiménez, M. J., Guil, N., Schmid, C. & Alahari, K. (2018). End-to-end incremental learning. In Proceedings of the European conference on computer vision (ECCV) (pp. 233–248).

2. Chen, L., Yu, C. & Chen, L. (2019). A new knowledge distillation for incremental object detection. In 2019 international joint conference on neural networks (IJCNN) (pp. 1–7). IEEE.

3. Dai, J., Li, Y., He, K. & Sun, J. (2016). R-FCN: Object detection via region-based fully convolutional networks. In Advances in neural information processing systems (pp. 379–387).

4. Dhar, P., Singh, R. V., Peng, K. C., Wu, Z. & Chellappa, R. (2019). Learning without memorizing. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5138–5146).

5. Dong, N., Zhang, Y., Ding, M., & Lee, G. H. (2021). Bridging non co-occurrence with unlabeled in-the-wild data for incremental object detection. Advances in Neural Information Processing Systems, 34, 30492–30503.

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