Active learning and discovery of object categories in the presence of unnameable instances
By Christoph Käding, Alexander Freytag, Erik Rodner, Paul Bodesheim, Joachim Denzler. 2015
58 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning
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
- Harsh Agrawal
- Aroma Mahendru
- Dhruv Batra
- Neelima Chavali
- Devi Parikh
- Mohit Bansal
- Jianfeng Gao
- Zhe Gan
In Computer Vision and Pattern Recognition
- Harsh Agrawal
- Aroma Mahendru
- Neelima Chavali
- Dhruv Batra
- Devi Parikh
- Luc Van Gool
- Xiaogang Wang
- Trevor Darrell
Citations in the corpus
- Object-Proposal Evaluation Protocol is ‘Gameable’ (2015, background): “While this is a valid and significant concern, it is orthogonal to the ‘gameability’ and bias in evaluation protocol, which to the best of our knowledge has not been previously addressed.”
- BING: Binarized normed gradients for objectness estimation at 300fps (2014, unknown): “A generic objectness measure has great potential to be used as a pre-filter for many vision tasks, including object detection [3–5], visual tracking [6, 7], object discovery [8, 9], semantic segmentation [10, 11], content aware image retargeting [12], and action recognition [13].”