awesome-object-proposals

A curated list of object proposals resources for object detection.

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Table of Contents

• Introduction
• Tutorials
• Papers
  • Objectness Scoring
  • Similarity Grouping
  • Supervised Learning
  • Hybrid & Part-based
  • RGB-D
  • Re-ranking & Refinement
  • Spatio-Temporal
  • Low-Level Processing
  • Evaluation
• Datasets

Introduction

• [A Seismic Shift in Object Detection] (https://pdollar.wordpress.com/2013/12/10/a-seismic-shift-in-object-detection/) by Piotr Dollár.
• [Generating Object Proposals] (https://pdollar.wordpress.com/2013/12/22/generating-object-proposals/) by Piotr Dollár.

Tutorials

• [ICCV 2015 Tutorial on Tools for Efficient Object Detection] (http://mp7.watson.ibm.com/ICCV2015/ObjectDetectionICCV2015.html)
  • Jan Hosang, Region Proposals.

Papers

Objectness Scoring

• Objectness [Project]
  • Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari, What is an object?, CVPR, 2010.
  • Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari, Measuring the Objectness of Image Windows, TPAMI, 2012.
• Rahtu [Project]
  • Esa Rahtu, Juho Kannala, and Matthew Blaschko, Learning a Category Independent Object Detection Cascade, ICCV, 2011.
• Cascaded Ranking SVMs [Code]
  • Ziming Zhang, Jonathan Warrell, and Philip H. S. Torr, Proposal generation for object detection using cascaded ranking SVMs, CVPR, 2011.
• Salient
  • Jie Feng, Yichen Wei, Litian Tao, Chao Zhang, and Jian Sun, Salient Object Detection by Composition, ICCV, 2011.
• RandomizedSeeds [Project]
  • Michael Van den Bergh, Gemma Roig, Xavier Boix, Santiago Manen, Luc Van Gool, Online Video SEEDS for Temporal Window Objectness, ICCV, 2013.
• BING [Project]
  • Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, and Philip Torr, BING: Binarized Normed Gradients for Objectness Estimation at 300fps, CVPR, 2014.
• CrackingBING
  • Qiyang Zhao, Zhibin Liu, Baolin Yin, Cracking BING and Beyond, BMVC, 2014.
• BING++
  • Ziming Zhang, Yun Liu, Tolga Bolukbasi, Ming-Ming Cheng, and Venkatesh Saligrama, BING++: A Fast High Quality Object Proposal Generator at 100fps, arXiv:1511.04511.
  • Ziming Zhang, Xi Chen, Yanjun Zhu, Zhiguo Cao, Venkatesh Saligrama, and Philip H.S. Torr, Sequential Optimization for Efficient High-Quality Object Proposal Generation, arXiv:1511.04511v2.
• EdgeBoxes [Project] [Code]
  • Piotr Dollár and C. Lawrence Zitnick, Edge Boxes: Locating Object Proposals from Edges, ECCV, 2014.
• ContourBox
  • Cewu Lu , Shu Liu, Jiaya Jia and Chi-Keung Tang, Contour Box: Rejecting Object Proposals Without Explicit Closed Contours, ICCV, 2015.

Similarity Grouping

• CPMC [Project]
  • Joao Carreira and Cristian Sminchisescu, Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR, 2010.
  • Joao Carreira and Cristian Sminchisescu, CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts, TPAMI, 2012.
• Endres [Project]
  • Ian Endres and Derek Hoiem, Category Independent Object Proposals, ECCV, 2010.
  • Ian Endres and Derek Hoiem, Category-Independent Object Proposals With Diverse Ranking, TPAMI, 2014.
• Selective Search [Project]
  • Koen E. A. van de Sande, Jasper R. R. Uijlings, Theo Gevers, and Arnold W. M. Smeulders, Segmentation As Selective Search for Object Recognition, ICCV, 2011.
  • Jasper R. R. Uijlings, Koen E. A. van de Sande, Theo Gevers, and Arnold W. M. Smeulders, Selective Search for Object Recognition, IJCV, 2013.
• ObjSal [Project]
  • Kai-Yueh Chang, Tyng-Luh Liu, Hwann-Tzong, and Chen Shang-Hong Lai, Fusing Generic Objectness and Visual Saliency for Salient Object Detection, ICCV, 2011.
• RandomizedPrim [Project]
  • Santiago Manen, Matthieu Guillaumin, and Luc Van Gool, Prime Object Proposals with Randomized Prim's Algorithm , ICCV, 2013.
• Rantalankila
  • Pekka Rantalankila, Juho Kannala, and Esa Rahtu, Generating Object Segmentation Proposals Using Global and Local Search , CVPR, 2014.
• RIGOR [Project]
  • Ahmad Humayun, Fuxin Li, and James M. Rehg, RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions, CVPR, 2014.
• GOP [Project]
  • Philipp Krähenbühl and Vladlen Koltun, Geodesic Object Proposals, ECCV, 2014.
• MCG [Project]
  • Pablo Arbelaez*, Jordi Pont-Tuset*, Jonathan T. Barron, Ferran Marques, Jitendra Malik, Multiscale Combinatorial Grouping, CVPR, 2014.
  • Jordi Pont-Tuset*, Pablo Arbelaez*, Jonathan T. Barron, Ferran Marques, Jitendra Malik, Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation, TPAMI, 2017.

Supervised Learning

• MultiBox [Project]
  • Dumitru Erhan, Christian Szegedy, Alexander Toshev, and Dragomir Anguelov, Scalable Object Detection using Deep Neural Networks, CVPR, 2014.
  • Christian Szegedy, Scott Reed, Dumitru Erhan, and Dragomir Anguelov, Scalable, High-Quality Object Detection, arXiv:1412.1441.
• DeepMask [Code]
  • Pedro O. Pinheiro, Ronan Collobert and Piotr Dollár, Learning to Segment Object Candidates, NIPS, 2015.
• Mid-level Cues
  • Tom Lee, Sanja Fidler, and Sven Dickinson, Learning to Combine Mid-level Cues for Object Proposal Generation, ICCV, 2015.
• LPO [Project]
  • Philipp Krähenbühl and Vladlen Koltun, Learning to Propose Objects, CVPR, 2015.
• RPN [Project]
  • Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NIPS, 2015.
• DeepProposal [Code]
  • Amir Ghodrati, Ali Diba, Marco Pedersoli, Tinne Tuytelaars, and Luc Van Gool, DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers, ICCV, 2015.
• 3DOP [Project]
  • Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew Berneshawi, Huimin Ma, Sanja Fidler, and Raquel Urtasun, 3D Object Proposals for Accurate Object Class Detection, NIPS, 2015.
• Mono3D [Project]
  • Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler, and Raquel Urtasun, Monocular 3D Object Detection for Autonomous Driving, CVPR, 2016.
• HyperNet
  • Tao Kong, Anbang Yao, Yurong Chen, and Fuchun Sun, HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection, CVPR, 2016.
• CRAFT [Project]
  • Bin Yang, Junjie Yan, Zhen Lei, and Stan Z. Li, CRAFT Objects From Images, CVPR, 2016.
• AttractioNet [Project]
  • Spyros Gidaris and Nikos Komodakis, Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization, BMVC, 2016.
• SPOP-net
  • Zequn Jie, Xiaodan Liang, Jiashi Feng, Wen Feng Lu, Eng Hock Francis Tay, and Shuicheng Yan, Scale-Aware Pixelwise Object Proposal Networks, TIP, 2016.
• FCN
  • Zequn Jie, Wen Feng Lu, Siavash Sakhavi, Yunchao Wei, Eng Hock Francis Tay, and Shuicheng Yan, Object Proposal Generation with Fully Convolutional Networks, TCSVT, 2016.
• InstanceFCN
  • Jifeng Dai, Kaiming He, Yi Li, Shaoqing Ren, and Jian Sun, Instance-Sensitive Fully Convolutional Networks, ECCV, 2016.
• MV3D [Project]
  • Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia, Multi-View 3D Object Detection Network for Autonomous Driving, arxiv.1611.07759. 2016.

Hybrid / Part-based

• ShapeSharing [Project]
  • Jaechul Kim and Kristen Grauman, Shape Sharing for Object Segmentation, ECCV, 2012.
• OOP [Project]
  • Shengfeng He and Rynson W.H. Lau, Oriented Object Proposals, ICCV, 2015.
• Object Discovery [Project]
  • Minsu Cho, Suha Kwak, Cordelia Schmid, and Jean Ponce, Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals, CVPR, 2015.
• Adobe Boxes [Code]
  • Authors, Adobe Boxes: Locating Object Proposals Using Object Adobes, TIP, 2016.

RGB-D

• MCG-D [Project]
  • Saurabh Gupta, Ross Girshick, Pablo Arbeláez and Jitendra Malik, Learning Rich Features from RGB-D Images for Object Detection and Segmentation, ECCV, 2014.
• StereoObj [Dataset]
  • Shao Huang, Weiqiang Wang, Shengfeng He, and Rynson W.H. Lau, Stereo Object Proposals​, TIP, 2017.
• Elastic Edge Boxes [Project]
  • Jing Liua, Tongwei Rena, Yuantian Wanga, Sheng-Hua Zhong, Jia Beia, Shengchao Chen, Object proposal on RGB-D images via elastic edge boxes, Neurocomputing, 2017.

Re-ranking & Refinement

• MTSE [Project]
  • Xiaozhi Chen, Huimin Ma, Xiang Wang, Zhichen Zhao, Improving Object Proposals with Multi-Thresholding Straddling Expansion, CVPR, 2015.
  • Xiaozhi Chen, Huimin Ma, Chenzhuo Zhu, Xiang Wang, Zhichen Zhao, Boundary-aware box refinement for object proposal generation, Neurocomputing, 2017.
• DeepBox [Project]
  • Weicheng Kuo, Bharath Hariharan, and Jitendra Malik, DeepBox: Learning Objectness with Convolutional Networks, ICCV, 2015.
• SharpMask [Code]
  • Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, and Piotr Dollár, Learning to Refine Object Segments, ECCV, 2016.

Spatio-Temporal

• STMOP [Project]
  • Katerina Fragkiadaki, Pablo Arbelaez, Panna Felsen, and Jitendra Malik, Learning to Segment Moving Objects in Videos, CVPR, 2015.

Evaluation

• Hosang benchmark [Project] [Code]
  • Jan Hosang, Rodrigo Benenson, and Bernt Schiele, How good are detection proposals, really?, BMVC, 2014.
  • Jan Hosang, Rodrigo Benenson, Piotr Dollár, and Bernt Schiele, What makes for effective detection proposals?, TPAMI, 2016.
• Jordi Pont-Tuset and Luc Van Gool, [Boosting Object Proposals: From Pascal to COCO] (http://www.vision.ee.ethz.ch/~biwiproposals/boosting-coco/data/PontTusetVanGool-Boosting-ICCV2015.pdf), ICCV, 2015. [Project]
• Neelima Chavali, Harsh Agrawal, Aroma Mahendru, and Dhruv Batra, [Object-Proposal Evaluation Protocol is 'Gameable'] (https://arxiv.org/pdf/1505.05836.pdf), CVPR, 2016. [Project]

Low-Level Processing

• Felzenszwalb's segmentation [Project]
  • Pedro F. Felzenszwalb and Daniel P. Huttenlocher, Efficient Graph-Based Image Segmentation, IJCV, 2004.
• Structured Edge Detection [Code]
  • Piotr Dollár and C. Lawrence Zitnick, Structured Forests for Fast Edge Detection , ICCV, 2013.

Datasets

• PASCAL [Project]
  • Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman, The PASCAL Visual Object Classes (VOC) Challenge, IJCV, 2010.
• MS COCO [Project]
  • Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár, Microsoft COCO: Common Objects in Context, ECCV, 2014.
• ImageNet [Project]
  • Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database, CVPR, 2009.
• NYU Depth Dataset [Project]
  • Nathan Silberman, Pushmeet Kohli, Derek Hoiem, and Rob Fergus, Indoor Segmentation and Support Inference from RGBD Images, ECCV, 2012.
• KITTI [Project]
  • Andreas Geiger and Philip Lenz and Raquel Urtasun, Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite, CVPR, 2012.