State-of-the-Art Object Proposal Benchmark

Quantitative evaluation of the current object proposal techniques

Per-Category Evaluation

Per-category Average Best Overlap (ABO) on COCO val 2014. The categories are sorted by category difficulty, that is, by the mean ABO over all state-of-the-art methids. All techniques are evaluated at around 650 proposals per image, so for those techniques producing a non-ranked set of proposals larger than 650, we randomly selected 650 out of them on every image.

Specific Category Evaluation

Click on a specific category in the plot above to see the detailed evaluation on that category. Each technique is in this case evalauted in their "native" number of proposals.


  • Solid lines represent ranked proposals methods, i.e., you can generate them once, and then pick the top N.
  • Dashed lines joining points represent methods that have to be run with different parameterizations to get different number of proposals.
  • Isolated points where methods run only for one parameterization.
  • Dashed lines without points represent baseline methods.


  • SharpMaskRegions: Learning to Refine Object Segments, P. O. Pinheiro, T-Y Lin, R. Collobert, and P. Dollár, ECCV 2016.
  • DeepMaskRegions: Learning to Segment Object Candidates, P. O. Pinheiro, R. Collobert, and P. Dollár, NIPS 2015.
  • COB: Convolutional Oriented Boundaries, K.K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. Van Gool, ECCV 2016.
  • POISE: The Middle Child Problem: Revisiting Parametric Min-cut and Seeds for Object Proposals, A. Humayun, F. Li, and J.M. Rehg, ICCV 2015.
  • MCG and SCG: Multiscale Combinatorial Grouping, P. Arbeláez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik, CVPR 2014.
  • LPO: Learning to Propose Objects, P. Krähenbühl, and V. Koltun, CVPR 2015.
  • GOP: Geodesic Object Proposals, P. Krähenbühl, and V. Koltun, ECCV 2014.
  • GLS: Generating Object Segmentation Proposals using Global and Local Search, P. Rantalankila, J. Kannala, and E. Rahtu, CVPR 2014.
  • RIGOR: Recycling Inference in Graph Cuts for generating Object Regions, A. Humayun, F. Li, and J.M. Rehg, CVPR 2014.
  • SeSe: Selective Search for Object Recognition, J.R.R. Uijlings, K.E.A. van de Sande, T. Gevers, and A.W.M. Smeulders, IJCV 2013.
  • QT: Baseline method consisting in a homogenous Quad-Tree, regardless of the image content.
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