Towards Reversal-Invariant Image Representation

By Lingxi Xie, Jingdong Wang, Weiyao Lin, Qi Tian. 2017

40 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. Chenxi Liu
2. Lingxi Xie
3. Yuxin Peng
4. Alan L. Yuille
5. Xiangteng He
6. Junjie Zhao
7. Liang-Chieh Chen
8. Xiaohui Shen

In Computer Vision and Pattern Recognition

1. Alan L. Yuille
2. Yuxin Peng
3. Lingxi Xie
4. Chenxi Liu
5. Xiangteng He
6. Luc Van Gool
7. Dacheng Tao
8. Quoc V. Le

Citations in the corpus

• Genetic CNN (2017, background): “Efforts are also made to add invariance into the network structure [38].”
• Object-Part Attention Model for Fine-Grained Image Classification (2017, methodology): “…Our OPAM Approach 97.10 VGGNet InterActive [30] 96.40 VGGNet PBC [57] 96.10 GoogleNet TL Atten [15] 95.76 VGGNet NAC [20] 95.34 VGGNet RIIR [58] 94.01 VGGNet Deep Optimized [51] 91.30 AlexNet SDR [51] 90.50 AlexNet MML [52] 89.45 CNN CNN Feature [59] 86.80 CNN Generalized Max Pooling [54]…”
• Object Recognition with and without Objects (2016, unknown): “, 2015b], object recognition [Xie et al., 2017], etc.”
• SORT: Second-Order Response Transform for Visual Recognition (2017, background): “The intermediate responses of CNNs can be used as effective image descriptors [6], and widely applied to various types of vision applications including image classification [22][52], image retrieval [40][50] and object detection [10].”