Attentive Object Detection Using an Information Theoretic Saliency Measure
By Gerald Fritz, Christin Seifert, Lucas Paletta, Horst Bischof. 2004
34 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
- Shuicheng Yan
- Erich Rome
- Qi Zhao
- Henrik Iskov Christensen
- Simone Frintrop
- Richard Socher
- Caiming Xiong
- Tam V. Nguyen
In Computer Vision and Pattern Recognition
- Shuicheng Yan
- Ming-Hsuan Yang
- Caiming Xiong
- Luc Van Gool
- Dacheng Tao
- Jiashi Feng
- Yi Yang
- Ming-Ming Cheng
Citations in the corpus
- Learning World Graphs to Accelerate Hierarchical Reinforcement Learning (2019, positive): “In addition, th WN mechanism borrows ideas from attentive object understanding [31, 5, 95] in vision.”
- Computational visual attention systems and their cognitive foundations: A survey (2010, methodology): “In these approaches, for example, all objects of a database that might occur in a scene are investigated in advance and their most discriminative regions are determined, that is, the regions that distinguish an object best from all others in the database [Fritz et al. 2004; Pessoa and Exel 1999].”
- Attentive Systems: A Survey (2017, background): “Fritz et al. (2004) introduce a saliency-based approach for object detection.”