From categories to subcategories: Large-scale image classification with partial class label refinement

By Marko Ristin, Juergen Gall, Matthieu Guillaumin, Luc Van Gool. 2015

59 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Gang Niu
2. Masashi Sugiyama
3. Erwin M. Bakker
4. Y. Liu
5. Yanming Guo
6. Michael S. Lew
7. Hadi Kazemi
8. Yuanhao Guo

In Computer Vision and Pattern Recognition

1. Yanming Guo
2. Gang Niu
3. Erwin M. Bakker
4. Y. Liu
5. Michael S. Lew
6. Hadi Kazemi
7. Yuanhao Guo
8. Fariborz Taherkhani

Citations in the corpus

• A Weakly Supervised Fine Label Classifier Enhanced by Coarse Supervision (2019, methodology): “We arrange our experimental setup as described in [28] and [13].”
• Deep learning for visual understanding (2017, unknown): “We perform our experiments on three well-known datasets: CIFAR-100 [278], ImageNet 2012 [21] and a subset of ImageNet 2010 [265].”
• Does Distributionally Robust Supervised Learning Give Robust Classifiers? (2016, background): “A sub-category (Ristin et al., 2015) is a refined category of a class label, e.”
• CNN-RNN: a large-scale hierarchical image classification framework (2017, methodology): “In contrast to other methods listed in [30], RNN achieves superior classification performance by inherently exploiting the relationship between the coarse and fine categories.”