Learning coarse-to-fine sparselets for efficient object detection and scene classification

By Gong Cheng, Junwei Han, Lei Guo, Tianming Liu. 2015

58 citations recorded by the source corpus.

Subfields: 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 Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Peicheng Zhou
4. Xiwen Yao
5. Lei Guo
6. Guisong Xia
7. Chao Yao
8. Shuhui Bu

Citations in the corpus

• Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2015, background): “…gradient (HoG) Cheng et al. 2013b) or mid-level features (e.g. bag-of-words (BoW) Csurka et al. 2004, locality-constrained linear coding (LLC) Wang et al. 2010, sparsely-constructed Gaussian processes (L1-GPs) Liu et al. 2014, and Sparselets Cheng et al. 2015a,b,c), to describe image patches.”
• RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection (2016, methodology): “The DPM [30] and its variants [20, 22, 23, 34-39] have been the leading methods for object detection tasks for years owing to the carefully crafted features like HOG [32].”
• Learning Layer-Skippable Inference Network (2020, background): “by researchers [16]–[19] since it is very useful for real-world”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “have been undertaken driven by its real-world applications, such as urban planning [5], [6], natural hazards detection [7]–[9], environment monitoring [10]–[12], vegetation mapping [13], [14], and geospatial object detection [15]–[22].”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…methods (Cheng et al., 2013b; Cheng et al., 2014a, b; Zhang et al., 2014b; Zhang et al., 2015b) and the sparelets work (Cheng et al., 2015a; Cheng et al., 2015b; Cheng et al., 2015c) -norm: , -norm: ‹ where x is a small constant and the results are insensitive to the value of x over a…”
• Land Use Classification in Remote Sensing Images by Convolutional Neural Networks (2015, background): “High-level features are used also in [38] in the context of semisupervised feature learning.”
• Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature (2016, background): “More recently, with the advances of machine learning, especially the powerful feature representations and classifiers, many approaches regarded object detection as a classification problem [1-22].”
• Scene classification of high resolution remote sensing images using convolutional neural networks (2016, background): “However, most of them are based on handcrafted or shallow learning-based features, such as bag-of-visual-words (BOVW) model [23] and its variations [4, 20, 21], sparse coding-based features [12, 19], midlevel visual elements-oriented features [9, 11, 15, 18] and so on.”