RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection

By Gong Cheng, Peicheng Zhou, Junwei Han. 2016

171 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Guisong Xia
2. Jian Ding
3. Zhenwei Shi
4. Yair Weiss
5. Aharon Azulay
6. Junwei Han
7. Xiaoqiang Lu
8. Gong Cheng

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Dacheng Tao
3. Gong Cheng
4. Xiaogang Wang
5. Luc Van Gool
6. Ming-Hsuan Yang
7. Yi Yang
8. Ling Shao

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “models have shown impressive feature representation capability for a wide range of applications including remote sensing image scene classification [13, 17, 45, 50, 82, 143-158].”
• Why do deep convolutional networks generalize so poorly to small image transformations? (2018, methodology): “Alternatively, one could use specially designed features such as SIFT in which invariance is hard coded [7, 29, 30, 31] or neural network architectures that explicitly enforce invariance [36, 19, 5, 6, 11, 12, 47, 46, 8].”
• RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks (2018, methodology): “While deep convolutional neural networks (CNN) have been widely used in computer vision and image processing applications, they are not designed to handle large group actions like rotations, which degrade the performance of CNN in many tasks [2, 9, 15, 21, 23].”
• Oriented Response Networks (2017, background): “In Deep Convolution Neural Networks (DCNNs), the inherent properties of convolution and pooling alleviate the effect of local transitions and warps; however, lacking the capability to handle large image rotation limits DCNN’s performance in many visual tasks including object boundary detection [16, ”
• Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey (2018, background): “Based on such capability, a large amount of the modern object detection approaches [2], [19], [34], [36], [42], [58], [59], [62] can benefit from a simple but effective pretraining phase.”
• Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, background): “…improve the performance of deep learning based methods for object detection, such as feature enhancement (Cai et al., 2016; Cheng et al., 2019; Cheng et al., 2016b; Kong et al., 2016; Liu et al., 2017b), hard negative mining (Lin et al., 2017c; Shrivastava et al., 2016), contextual…”
• Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection (2019, unknown): “A preliminary version of this work was published in CVPR 2016 [69].”
• Object Detection in 20 Years: A Survey (2019, methodology): “After the great success of RCNN in 2014, deep CNN has been soon applied to remote sensing target detection [275, 276, 400, 401].”
• Learning RoI Transformer for Oriented Object Detection in Aerial Images (2019, background): “With more and more aerial images being available, object detection in aerial images has been a specific but active topic in computer vision [3, 29, 36, 6].”
• Deep Learning for Generic Object Detection: A Survey (2018, background): “Some approaches have been presented to enhance the robustness of CNN representations, aiming at learning invariant CNN representations with respect to different types of transformations such as scale [101, 18], rotation [18, 32, 218, 248], or both [100].”