Which Looks Like Which: Exploring Inter-class Relationships in Fine-Grained Visual Categorization
By Jian Pu, Yu-Gang Jiang, Jun Wang, Xiangyang Xue. 2014
33 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Pietro Perona
- Jonathan Krause
- Grant Van Horn
- Fei-Fei Li
- Benjamin Sapp
- Alexander Toshev
- Li Fei-Fei
- Tom Duerig
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Andrew Zisserman
- Xiaogang Wang
- Dacheng Tao
- Trevor Darrell
- Jitendra Malik
- Alan L. Yuille
- Yi Yang
In Media Technology
- Pietro Perona
- Alexander Toshev
- James Philbin
- Fei-Fei Li
- Jonathan Krause
- Grant Van Horn
- Benjamin Sapp
- Andrew Zisserman
Citations in the corpus
- The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition (2015, background): “The majority of research in fine-grained recognition has focused on developing improved models for classification [2, 5, 7, 15, 17, 18, 24, 30, 37, 44, 45, 58, 59, 60, 63, 64].”
- Deep Image: Scaling up Image Recognition (2015, neutral): “ categorization results on CUB-200-2011 Method Training Testing Accura BBox Part BBox Part cy(%) Alignments [17] 53.6 Attention [49] 77.9 B-CNN (D,M) [30] 80.9 Part discovery [44] 81.0 Ours 84.9 GMTL [38] √ √ 44.2 Symbiotic [6] √ √ 59.4 Alignments [17] √ √ 67.0 B-CNN (D,M) [30] √ 81.9 Ours+BBox √ 85”
- The Devil is in the Tails: Fine-grained Classification in the Wild (2017, background): “Most fine-grained research papers present a novel model for classification [4, 6, 7, 12, 15, 17, 25, 31, 40, 45, 46, 57, 58, 59, 61, 62].”
- Fine-grained recognition without part annotations (2015, unknown): “GMTL [36] BBox BBox 44.”