Learning RoI Transformer for Oriented Object Detection in ...
avoid a large number of anchors designed for oriented object detection. • We design a Rotated Position Sensitive RoI Align-ment module for spatially invariant feature extraction, which can effectively boost the object classification andlocationregression. Themoduleisacrucialdesign when using the light-head RoI-wise operation, which
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What Have We Learned From Deep Representations for …
openaccess.thecvf.comwhat these powerful models actually have learned. In this paper we shed light on deep spatiotemporal net-works by visualizing what excites the learned models us-ing activation maximization by backpropagating on the in-put. We are the first to visualize the hierarchical features
Finding Tiny Faces in the Wild With Generative Adversarial ...
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Squeeze-and-Excitation Networks - openaccess.thecvf.com
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Class-Balanced Loss Based on Effective Number of Samples
openaccess.thecvf.comand large-scale datasets including ImageNet and iNatural-ist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve signifi-cant performance gains on long-tailed datasets. 1. Introduction The recent success of deep Convolutional Neural Net-works (CNNs) for visual recognition [26, 37, 38, 16] owes
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