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Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling. (arXiv:2205.05155v1 [cs.CV])
cs.CV updates on arXiv.org arxiv.org
Every day, a new method is published to tackle Few-Shot Image Classification,
showing better and better performances on academic benchmarks. Nevertheless, we
observe that these current benchmarks do not accurately represent the real
industrial use cases that we encountered. In this work, through both
qualitative and quantitative studies, we expose that the widely used benchmark
tieredImageNet is strongly biased towards tasks composed of very semantically
dissimilar classes e.g. bathtub, cabbage, pizza, schipperke, and cardoon. This
makes tieredImageNet (and similar benchmarks) …
arxiv benchmarks classification cv image reality sampling semantic