DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks

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S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning (2023, background): “In recent years, Deep Neural Networks (DNN) have shown significant performance improvement on various computer vision applications [19,27,29].”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “They are, in effect, black box systems that can deliver, with minimal human attention, excellent performance in applications that require insights from unstructured, high-dimensional data [4] [5] [6] [7] [8] [9].”
Deep Regionlets for Object Detection (2017, unknown): “Recent advances [33, 6, 32] have achieved promising results by extending the conventional DPM-based detection methodology with the deep neural network architectures.”
Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, background): “…hard negative mining (Lin et al., 2017c; Shrivastava et al., 2016), contextual information fusion (Bell et al., 2016; Gidaris and Komodakis, 2015; Shrivastava and Gupta, 2016; Zhu et al., 2015b), modeling object deformations (Mordan et al., 2018; Ouyang et al., 2017; Xu et al., 2017), and so on.”
Deep Learning for Computer Vision: A Brief Review (2018, background): “A vast majority of works on object detection using deep learning apply a variation of CNNs, for example, [8, 67, 68]”