Salient Object Detection: A Benchmark
University of Wisconsin–Milwaukee · University of Oxford · +3 more institutions
Abstract
We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted three years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas,…
Citation impact
- FWCI
- 73.74
- Percentile
- 100%
- References
- 139
Authors
4- ABAli BorjiCorresponding
University of Wisconsin–Milwaukee
- MCMing-Ming Cheng
University of Oxford
- HJHuaizu Jiang
University of Massachusetts Amherst, Amherst College
- JLJia Li
Beihang University
Topics & keywords
- Benchmarking
- Salient
- Benchmark (surveying)
- Segmentation
- Object detection
- Object (grammar)
- Set (abstract data type)
- Pattern recognition (psychology)