Advancing Incremental Few-Shot Semantic Segmentation via Semantic-Guided Relation Alignment and Adaptation

Author:

Zhou YuanORCID,Chen XinORCID,Guo YanrongORCID,Yu JunORCID,Hong RichangORCID,Tian QiORCID

Publisher

Springer Nature Switzerland

Reference28 articles.

1. Caesar, H., Uijlings, J., Ferrari, V.: Coco-stuff: thing and stuff classes in context. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 1209–1218 (2018)

2. Castro, F.M., Marín-Jiménez, M.J., Guil, N., Schmid, C., Alahari, K.: End-to-end incremental learning. In: Proceedings of the European Conference on Computer Vision, pp. 233–248 (2018)

3. Cermelli, F., Mancini, M., Bulo, S.R., Ricci, E., Caputo, B.: Modeling the background for incremental learning in semantic segmentation. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 9233–9242 (2020)

4. Cermelli, F., Mancini, M., Xian, Y., Akata, Z., Caputo, B.: Prototype-based incremental few-shot semantic segmentation. In: Proceedings of the British Machine Vision Conference, BMVC 2021, p. 484. (2021)

5. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge 2012 (VOC2012) results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html

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