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[arXiv]
[bibtex]@InProceedings{Kilickaya_2023_CVPR, author = {Kilickaya, Mert and Vanschoren, Joaquin}, title = {Are Labels Needed for Incremental Instance Learning?}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {2401-2409} }
Are Labels Needed for Incremental Instance Learning?
Abstract
In this paper, we learn to classify visual object instances, incrementally and via self-supervision (self-incremental). Our learner observes a single instance at a time, which is then discarded from the dataset. Incremental instance learning is challenging, since longer learning sessions exacerbate forgetfulness, and labeling instances is cumbersome. We overcome these challenges via three contributions: i). We propose VINIL, a self-incremental learner that can learn object instances sequentially, ii). We equip VINIL with self-supervision to by-pass the need for instance labelling, iii). We compare VINIL to label-supervised variants on two large-scale benchmarks [??], and show that VINIL significantly improves accuracy while reducing forgetfulness.
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