ME R-CNN: Multi-Expert Region-based CNN for Object Detection

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Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection (2019, methodology): “After that, lots of algorithms are proposed to improve its performance, including architecture redesign and reform [4, 9, 5, 28, 30], context and attention mechanism [2, 51, 38, 7, 44], multiscale training and testing [54, 41], training strategy and loss function [40, 52, 61, 17], feature fusion and”
Single-Shot Refinement Neural Network for Object Detection (2017, methodology): “After that, numerous effective techniques are proposed to further improve the performance, such as architecture diagram [5, 25, 55], training strategy [31, 41, 48, 50], contextual reasoning [1, 14, 40, 51] and multiple layers exploiting [3, 24, 26, 42].”
Learning Efficient Single-Stage Pedestrian Detectors by Asymptotic Localization Fitting (2018, background): “Recently, in the two-satage framework, numerous methods have tried to improve the detection performance by focusing on network architecture [8,22,23,25], training strategy [34,39], auxiliary context mining [1,15,35], and so on, while the heavy computational burden is still an unavoidable problem.”