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BAOD: Budget-Aware Object Detection
[article]
2021
arXiv
pre-print
We study the problem of object detection from a novel perspective in which annotation budget constraints are taken into consideration, appropriately coined Budget Aware Object Detection (BAOD). When provided with a fixed budget, we propose a strategy for building a diverse and informative dataset that can be used to optimally train a robust detector. We investigate both optimization and learning-based methods to sample which images to annotate and what type of annotation (strongly or weakly
arXiv:1904.05443v2
fatcat:2hwp6aor7fe3ldr6icvzmqcuki