Higher order conditional random fields in deep neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9906 LNCS, Issue , 2016, Pages 524-540

  Arnab, Anurag ^a   Jayasumana, Sadeep ^a   Zheng, Shuai ^a   Torr, Philip H S ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Conditional random fields; Convolutional neural networks; Deep learning; Semantic segmentation

Indexed keywords

BENCHMARKING; COMPUTER VISION; CONVOLUTION; INFERENCE ENGINES; NEURAL NETWORKS; OBJECT DETECTION; RANDOM PROCESSES; SEMANTICS;

CONDITIONAL RANDOM FIELD; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; DEEP NEURAL NETWORKS; HIGHER ORDER POTENTIALS; SEGMENTATION PERFORMANCE; SEGMENTATION SYSTEM; SEMANTIC SEGMENTATION;

IMAGE SEGMENTATION;

EID: 84990864139     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46475-6_33     Document Type: Conference Paper

References (49)

1. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097-1105 (2012)
2. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
3. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
4. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) challenge. IJCV 88, 303-338 (2010)
5. Ladicky, L., Russell, C., Kohli, P., Torr, P.H.: Associative hierarchical CRFs for object class image segmentation. In: ICCV, pp. 739-746 (2009)
6. Ladický, Ľ., Sturgess, P., Alahari, K., Russell, C., Torr, P.H.S.: What, where and how many? Combining object detectors and CRFs. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 424-437. Springer, Heidelberg (2010)
7. Vineet, V., Warrell, J., Torr, P.H.: Filter-based mean-field inference for random fields with higher-order terms and product label-spaces. IJCV 110, 290-307 (2014)
8. Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 297-312. Springer, Heidelberg (2014)
9. Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: Semantic image segmentation via deep parsing network. In: ICCV (2015)
10. Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.: Conditional random fields as recurrent neural networks. In: ICCV (2015)
11. Lin, G., Shen, C., Reid, I.: Efficient piecewise training of deep structured models for semantic segmentation. In: CVPR (2016)
12. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In:ICLR (2015)
13. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
14. Shotton, J., Winn, J., Rother, C., Criminisi, A.: TextonBoost for image understanding:multi-class object recognition and segmentation by jointly modeling texture, layout, and context. IJCV 81, 2-23 (2009)
15. Kohli, P., Ladicky, L., Torr, P.: Robust higher order potentials for enforcing label consistency. IJCV 82(3), 302-324 (2009)
16. Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: NIPS (2011)
17. Ladicky, L., Russell, C., Kohli, P., Torr, P.H.S.: Graph cut based inference with co-occurrence statistics. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 239-253. Springer, Heidelberg (2010)
18. Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., Belongie, S.: Objects in context. In: ICCV, pp. 1-8 (2007)
19. Gonfaus, J.M., Boix, X., Van de Weijer, J., Bagdanov, A.D., Serrat, J., Gonzalez, J.: Harmony potentials for joint classification and segmentation. In: IEEE on CVPR, pp. 3280-3287 (2010)
20. Yao, J., Fidler, S., Urtasun, R.: Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation. In: CVPR, pp. 702-709 (2012)
21. Wojek, C., Schiele, B.: A dynamic conditional random field model for joint labeling of object and scene classes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 733-747. Springer, Heidelberg (2008)
22. Lin, G., Shen, C., Reid, I., van den Hengel, A.: Deeply learning the messages in message passing inference. In: NIPS, pp. 361-369 (2015)
23. Yang, Y., Hallman, S., Ramanan, D., Fowlkes, C.C.: Layered object models for image segmentation. PAMI 34, 1731-1743 (2012)
24. Sun, M., Kim, B.S., Kohli, P., Savarese, S.: Relating things and stuff via object property interactions. PAMI 36(7), 1370-1383 (2014)
25. Carreira, J., Caseiro, R., Batista, J., Sminchisescu, C.: Semantic segmentation with second-order pooling. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 430-443. Springer, Heidelberg (2012)
26. Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. PAMI 35, 1915-1929 (2013)
27. Dai, J., He, K., Sun, J.: Convolutional feature masking for joint object and stuff segmentation. In: CVPR (2015)
28. Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: NIPS, pp. 1799-1807 (2014)
29. Deng, Z., Zhai, M., Chen, L., Liu, Y., Muralidharan, S., Roshtkhari, M.J., Mori, G.: Deep structured models for group activity recognition. In: BMVC (2015)
30. Ionescu, C., Vantzos, O., Sminchisescu, C.: Matrix backpropagation for deep networks with structured layers. In: ICCV, pp. 2965-2973 (2015)
31. Domke, J.: Learning graphical model parameters with approximate marginal inference. PAMI 35, 2454-2467 (2013)
32. Krähenbühl, P., Koltun, V.: Parameter learning and convergent inference for dense random fields. In: ICML (2013)
33. Ross, S., Munoz, D., Hebert, M., Bagnell, J.A.: Learning message-passing inference machines for structured prediction. In: CVPR (2011)
34. Dai, J., He, K., Sun, J.: BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: ICCV (2015)
35. Girshick, R.: Fast R-CNN. In: ICCV (2015)
36. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS (2015)
37. Rother, C., Kolmogorov, V., Blake, A.: GrabCut: Interactive foreground extraction using iterated graph cuts. ACM TOG 23, 309-314 (2004)
38. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59, 167-181 (2004)
39. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. PAMI 34(11), 2274-2282 (2012)
40. Kohli, P., Kumar, M.P., Torr, P.H.: P3 & beyond: Solving energies with higher order cliques. In: CVPR (2007)
41. Baqu, P., Bagautdinov, T., Fleuret, F., Fua, P.: Principled parallel mean-field inference for discrete random fields. In: CVPR (2016)
42. Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: IEEE on ICCV, pp. 991-998 (2011)
43. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: Common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740-755. Springer, Heidelberg (2014)
44. Kokkinos, I.: Pushing the boundaries of boundary detection using deep learning. In: ICLR (2016)
45. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In:ICLR (2016)
46. Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: Scaleaware semantic image segmentation. In: CVPR (2016)
47. Papandreou, G., Chen, L., Murphy, K., Yuille, A.L.: Weakly-and semi-supervised learning of a DCNN for semantic image segmentation. In: ICCV (2015)
48. Liu, W., Rabinovich, A., Berg, A.C.: Parsenet: Looking wider to see better (2015). arXiv preprint: ArXiv:1506.04579
49. Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., et al.: The role of context for object detection and semantic segmentation in the wild. In: IEEE on CVPR, pp. 891-898 (2014)