Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

British Machine Vision Conference 2016, BMVC 2016

Volumn 2016-September, Issue , 2016, Pages 90.1-90,13

  Gidaris, Spyros ^a   Komodakis, Nikos ^a  

^a UNIVERSITÉ PARIS EST   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BOUNDING BOX; CORE COMPONENTS; NEW APPROACHES; OBJECT CATEGORIES; OBJECT LOCATION; STATE OF THE ART; TEST SETS;

COMPUTER VISION;

EID: 85044471801     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.30.90     Document Type: Conference Paper

References (43)

1. Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari. Measuring the objectness of image windows. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2012.
2. P. Arbel ez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik. Multiscale combinatorial grouping. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014.
3. Sean Bell, C Lawrence Zitnick, Kavita Bala, and Ross Girshick. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
4. Juan C Caicedo and Svetlana Lazebnik. Active object localization with deep reinforcement learning. In Proceedings of the IEEE International Conference on Computer Vision, 2015.
5. Xiaozhi Chen, Huimin Ma, Xiang Wang, and Zhichen Zhao. Improving object proposals with multi-thresholding straddling expansion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
6. Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, and Philip Torr. Bing: Binarized normed gradients for objectness estimation at 300fps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014.
7. Ramazan Gokberk Cinbis, Jakob Verbeek, and Cordelia Schmid. Weakly supervised object localization with multi-fold multiple instance learning. arXiv preprint arXiv:1503.00949, 2015.
8. Jifeng Dai, Kaiming He, Yi Li, Shaoqing Ren, and Jian Sun. Instance-sensitive fully convolutional networks. CoRR, abs/1603.08678.
9. Jifeng Dai, Kaiming He, and Jian Sun. Instance-aware semantic segmentation via multi-task network cascades. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
10. Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 2010.
11. Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan. Object detection with discriminatively trained part-based models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2010.
12. Amir Ghodrati, Ali Diba, Marco Pedersoli, Tinne Tuytelaars, and Luc Van Gool. Deepproposal: Hunting objects by cascading deep convolutional layers. In Proceedings of the IEEE International Conference on Computer Vision, 2015.
13. Spyros Gidaris and Nikos Komodakis. Object detection via a multi-region & semantic segmentation-aware cnn model. In Proceedings of the IEEE International Conference on Computer Vision, 2015.
14. Spyros Gidaris and Nikos Komodakis. Locnet: Improving localization accuracy for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
15. Ross Girshick. Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, 2015.
16. Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE International Conference on Computer Vision, 2014.
17. Abel Gonzalez-Garcia, Alexander Vezhnevets, and Vittorio Ferrari. An active search strategy for efficient object class detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
18. Saurabh Gupta and Jitendra Malik. Visual semantic role labeling. arXiv preprint arXiv:1505.04474, 2015.
19. Zeeshan Hayder, Xuming He, and Mathieu Salzmann. Learning to co-generate object proposals with a deep structured network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
20. K He, X Zhang, S Ren, and J Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 2015.
21. Jan Hosang, Rodrigo Benenson, Piotr Doll r, and Bernt Schiele. What makes for effective detection proposals? PAMI, 2015.
22. Justin Johnson, Andrej Karpathy, and Li Fei-Fei. Densecap: Fully convolutional localization networks for dense captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
23. Andrej Karpathy and Li Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
24. Philipp Kr henb hl and Vladlen Koltun. Geodesic object proposals. In European Conference on Computer Vision. Springer, 2014.
25. Philipp Kr henb hl and Vladlen Koltun. Learning to propose objects. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
26. Weicheng Kuo, Bharath Hariharan, and Jitendra Malik. Deepbox: Learning objectness with convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision, 2015.
27. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll r, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference on Computer Vision, 2014.
28. Yongxi Lu, Tara Javidi, and Svetlana Lazebnik. Adaptive object detection using adjacency and zoom prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
29. Francisco Massa, Bryan Russell, and Mathieu Aubry. Deep exemplar 2d-3d detection by adapting from real to rendered views. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
30. Mahyar Najibi, Mohammad Rastegari, and Larry S Davis. G-cnn: an iterative grid based object detector. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
31. Pedro H. O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, and Piotr Doll r. Learning to refine object segments. 2016. URL http://arxiv.org/abs/1603.08695.
32. Pedro O Pinheiro, Ronan Collobert, and Piotr Dollar. Learning to segment object candidates. In Advances in Neural Information Processing Systems, 2015.
33. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems, 2015.
34. Pierre Sermanet, David Eigen, Xiang Zhang, Micha l Mathieu, Rob Fergus, and Yann LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229, 2013.
35. Kevin J Shih, Saurabh Singh, and Derek Hoiem. Where to look: Focus regions for visual question answering. 2016.
36. Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick. Training region-based object detectors with online hard example mining. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
37. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
38. Koen EA Van de Sande, Jasper RR Uijlings, Theo Gevers, and Arnold WM Smeulders. Segmentation as selective search for object recognition. In Proceedings of the IEEE International Conference on Computer Vision, 2011.
39. Donggeun Yoo, Sunggyun Park, Joon-Young Lee, Anthony S Paek, and In So Kweon. Atten-tionnet: Aggregating weak directions for accurate object detection. In Proceedings of the IEEE International Conference on Computer Vision, 2015.
40. Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O Pinheiro, Sam Gross, Soumith Chintala, and Piotr Doll r. A multipath network for object detection. arXiv preprint arXiv:1604.02135, 2016.
41. Qiyang Zhao, Zhibin Liu, and Baolin Yin. Cracking bing and beyond. In Proceedings of the British Machine Vision Conference. BMVA Press, 2014.
42. Yukun Zhu, Raquel Urtasun, Ruslan Salakhutdinov, and Sanja Fidler. segdeepm: Exploiting segmentation and context in deep neural networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
43. C Lawrence Zitnick and Piotr Doll r. Edge boxes: Locating object proposals from edges. In European Conference on Computer Vision, 2014.