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Scale Optimization for Full-Image-CNN Vehicle Detection
[article]
2018
arXiv
pre-print
Many state-of-the-art general object detection methods make use of shared full-image convolutional features (as in Faster R-CNN). This achieves a reasonable test-phase computation time while enjoys the discriminative power provided by large Convolutional Neural Network (CNN) models. Such designs excel on benchmarks which contain natural images but which have very unnatural distributions, i.e. they have an unnaturally high-frequency of the target classes and a bias towards a "friendly" or
arXiv:1802.06926v1
fatcat:ejwgnvygbjhaxg5istt6kj4vzi