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
• Object Detection

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
  • 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
  • Jing Liu, Tongwei Ren, Yuantian Wang, Sheng-Hua Zhong, Jia Bei, 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.
• DeepStereoOP
  • Cuong C. Pham and Jae Wook Jeon, Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks, SPIC, 2017.

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.
• SLIC Superpixels [Project]
  • Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, TPAMI, 2012.
• 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.

Object Detection

• R-FCN [Code][PyCode]
  • Jifeng Dai, Yi Li, Kaiming He, Jian Sun, R-FCN: Object Detection via Region-based Fully Convolutional Networks, NIPS, 2016.
• SSD [Code]
  • Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg, SSD: Single Shot MultiBox Detector, ECCV, 2016.
• YOLO [Code]
  • Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You Only Look Once: Unified, Real-Time Object Detection, ECCV, 2016.
• Faster R-CNN [Code] [[PyCode]] (https://github.com/rbgirshick/py-faster-rcnn)
  • Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NIPS, 2015.
• Fast R-CNN [Code]
  • Ross Girshick, Fast R-CNN, ICCV, 2015.
• SPP [Code]
  • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, ECCV, 2014.
• R-CNN [Code]
  • Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR, 2014.

Notes

I hope this repository is helpful to everyone who're interested in this awesome topic. As Piotr Dollár said, it’s an amazing time to be doing research in object detection (and deep learning).

Thank you very much for your star and appreciation. Feel free to send a pull request if you have anything in mind that would fit in this list!