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Learning with an Evolving Class Ontology. (arXiv:2210.04993v2 [cs.CV] UPDATED)
Oct. 13, 2022, 1:17 a.m. | Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang, Deva Ramanan, Shu Kong
cs.CV updates on arXiv.org arxiv.org
Lifelong learners must recognize concept vocabularies that evolve over time.
A common yet underexplored scenario is learning with class labels over time
that refine/expand old classes. For example, humans learn to recognize ${\tt
dog}$ before dog breeds. In practical settings, dataset $\textit{versioning}$
often introduces refinement to ontologies, such as autonomous vehicle
benchmarks that refine a previous ${\tt vehicle}$ class into ${\tt school-bus}$
as autonomous operations expand to new cities. This paper formalizes a protocol
for studying the problem of $\textit{Learning …
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