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Negative results in computer vision: A perspective 원문보기

Image and vision computing, v.69, 2018년, pp.1 - 8  

Borji, Ali

Abstract AI-Helper 아이콘AI-Helper

Abstract A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in ot...

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참고문헌 (75)

  1. PLoS Med. Ioannidis 2 8 e124 2005 10.1371/journal.pmed.0020124 Why most published research findings are false 

  2. Am. J. Phys. Shankland 32 1 16 1964 10.1119/1.1970063 Michelson-Morley experiment 

  3. Artif. Intell. Horn 17 1-3 185 1981 10.1016/0004-3702(81)90024-2 Determining optical flow 

  4. Szeliski 2010 Computer Vision: Algorithms and Applications 

  5. Proc. IEEE LeCun 86 11 2278 1998 10.1109/5.726791 Gradient-based learning applied to document recognition 

  6. http://discovermagazine.com/2009/oct/06-brain-like-chip-may-solve-computers-big-problem-energy/. 

  7. https://www.caseyresearch.com/articles/brain-vs-computer. 

  8. IEEE Trans. Pattern Anal. Mach. Intell. Borji 35 1 185 2013 10.1109/TPAMI.2012.89 State-of-the-art in visual attention modeling 

  9. Nguyen 427 2015 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Deep neural networks are easily fooled: high confidence predictions for unrecognizable images 

  10. IEEE Trans. Pattern Anal. Mach. Intell. Kruger 35 8 1847 2013 10.1109/TPAMI.2012.272 Deep hierarchies in the primate visual cortex: what can we learn for computer vision? 

  11. IEEE Trans. Pattern Anal. Mach. Intell. Scheirer 36 8 1679 2014 10.1109/TPAMI.2013.2297711 Perceptual annotation: measuring human vision to improve computer vision 

  12. PLoS Comput. Biol. Pinto 4 1 e27 2008 10.1371/journal.pcbi.0040027 Why is real-world visual object recognition hard? 

  13. Trends Cogn. Sci. DiCarlo 11 8 333 2007 10.1016/j.tics.2007.06.010 Untangling invariant object recognition 

  14. Comput. Vis. Image Underst. Medathati 150 1 2016 10.1016/j.cviu.2016.04.009 Bio-inspired computer vision: towards a synergistic approach of artificial and biological vision 

  15. Front. Neurosci. Tan 9 374 2015 10.3389/fnins.2015.00374 Benchmarking neuromorphic vision: lessons learnt from computer vision 

  16. Fukushima 267 1982 Competition and Cooperation in Neural Nets Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition 

  17. Borji 113 2014 Proceedings of the IEEE conference on computer vision and pattern recognition Human Vs. Computer in scene and object recognition 

  18. 10.3389/fpsyg.2017.00142 R. VanRullen, Perception science in the age of deep neural networks, Front. Psychol. 8. 

  19. Ann. Rev. Vis. Sci. Kriegeskorte 1 417 2015 10.1146/annurev-vision-082114-035447 Deep neural networks: a new framework for modeling biological vision and brain information processing 

  20. Curr. Biol. Cox 24 18 R921 2014 10.1016/j.cub.2014.08.026 Neural networks and neuroscience-inspired computer vision 

  21. IEEE Trans. Pattern Anal. Mach. Intell. Serre 29 3 2007 10.1109/TPAMI.2007.56 Robust object recognition with cortex-like mechanisms 

  22. Parikh 1425 2011 Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference On Finding the weakest link in person detectors 

  23. Vogel 33 2006 Proceedings of the 3Rd Symposium on Applied Perception in Graphics and Visualization Categorization of natural scenes: local vs. global information 

  24. Deng 580 2013 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Fine-grained crowdsourcing for fine-grained recognition 

  25. Vis. Res. Gosselin 41 17 2261 2001 10.1016/S0042-6989(01)00097-9 Bubbles: a technique to reveal the use of information in recognition tasks 

  26. Science Potter 187 4180 965 1975 10.1126/science.1145183 Meaning in visual search 

  27. Nature Thorpe 381 6582 520 1996 10.1038/381520a0 Speed of processing in the human visual system 

  28. Denton 1486 2015 Advances in Neural Information Processing Systems Deep generative image models using a Laplacian pyramid of adversarial networks 

  29. Vondrick 289 2015 Advances in Neural Information Processing Systems Learning visual biases from human imagination 

  30. R. Fong, W. Scheirer, D. Cox, Using human brain activity to guide machine learning arXiv:1703.05463 

  31. Proc. Natl. Acad. Sci. Yamins 111 23 8619 2014 10.1073/pnas.1403112111 Performance-optimized hierarchical models predict neural responses in higher visual cortex 

  32. J. Vis. Borji 14 3 29-29 2014 10.1167/14.3.29 Defending yarbus: Eye movements reveal observers' task 

  33. Nature LeCun 521 7553 436 2015 10.1038/nature14539 Deep learning 

  34. He 1026 2015 Proceedings of the IEEE International Conference on Computer Vision Delving Deep into Rectifiers: Surpassing human-level performance on Imagenet classification 

  35. Jaderberg 2017 2015 Advances in Neural Information Processing Systems Spatial Transformer Networks 

  36. J. Physiol. Hubel 160 1 106 1962 10.1113/jphysiol.1962.sp006837 Receptive fields, binocular interaction and functional architecture in the cat's visual cortex 

  37. Nat. Neurosci. Riesenhuber 2 11 1019 1999 10.1038/14819 Hierarchical models of object recognition in cortex 

  38. Anselmi 2014 Tech. rep. Representation learning in sensory cortex: a theory 

  39. Vis. Res. Greene 62 1 2012 10.1016/j.visres.2012.03.019 Reconsidering Yarbus: a failure to predict observers task from eye movement patterns 

  40. Yarbus 1967 Eye Movements during Perception of Complex Objects 

  41. J. Am. Stat. Assoc. Sterling 54 285 30 1959 Publication decisions and their possible effects on inferences drawn from tests of significance or vice versa 

  42. Int. J. Environ. Res. Public Health Lian 12 8 9068 2015 10.3390/ijerph120809068 Short-term effect of ambient temperature and the risk of stroke: a systematic review and meta-analysis 

  43. Psychol. Bull. Rosenthal 86 3 638 1979 10.1037/0033-2909.86.3.638 The file drawer problem and tolerance for null results. 

  44. Plous 1993 The Psychology of Judgment and Decision Making. 

  45. Psychol. Inq. Nelson 23 3 291 2012 10.1080/1047840X.2012.705245 Let's publish fewer papers 

  46. J. Devlin, S. Gupta, R. Girshick, M. Mitchell, C. L. Zitnick, Exploring nearest neighbor approaches for image captioning, arXiv:1505.04467 

  47. J. Vis. Tatler 7 14 4-4 2007 10.1167/7.14.4 The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions 

  48. Dalal 1 886 2005 Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference On Histograms of oriented gradients for human detection 

  49. Vondrick 1 2013 Proceedings of the IEEE International Conference on Computer Vision Hoggles: visualizing object detection features 

  50. IEEE Trans. Pattern Anal. Mach. Intell. Hartley 19 6 580 1997 10.1109/34.601246 In defense of the eight-point algorithm 

  51. IEEE Trans. Pattern Anal. Mach. Intell. Achanta 34 11 2274 2012 10.1109/TPAMI.2012.120 SLIC Superpixels compared to state-of-the-art superpixel methods 

  52. Zeiler 818 2014 European Conference on Computer Vision Visualizing and understanding convolutional networks 

  53. J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, H. Lipson, Understanding neural networks through deep visualization, arXiv:1506.06579. 

  54. Eur. J. Personal. Asendorpf 27 2 108 2013 10.1002/per.1919 Recommendations for increasing replicability in psychology 

  55. https://rationalwiki.org/wiki/Extraordinary_claims_require_extraordinary_evidence. 

  56. Comput. Vis. Graphics Image Process. Haralick 36 2-3 372 1986 10.1016/0734-189X(86)90082-4 Computer vision theory: the lack thereof 

  57. http://www.nowozin.net/sebastian/blog/how-to-report-uncertainty.html. 

  58. Psychol. Methods Belia 10 4 389 2005 10.1037/1082-989X.10.4.389 Researchers misunderstand confidence intervals and standard error bars. 

  59. http://scienceblogs.com/cognitivedaily/2008/07/31/most-researchers-dont-understa-1/. 

  60. J. Am. Stat. Assoc. Dunnett 50 272 1096 1955 10.1080/01621459.1955.10501294 A multiple comparison procedure for comparing several treatments with a control 

  61. Sci. Adv. Cox 3 6 e1700768 2017 10.1126/sciadv.1700768 Statistical thinking for 21st century scientists 

  62. Efron 2016 Computer age statistical inference 

  63. J. R. Stat. Soc. Ser. B Methodol. Benjamini 289 1995 10.1111/j.2517-6161.1995.tb02031.x Controlling the false discovery rate: a practical and powerful approach to multiple testing 

  64. Teach. Stat. Matthews 22 2 36 2000 10.1111/1467-9639.00013 Storks deliver babies (p= 0.008) 

  65. https://priceonomics.com/do-storks-deliver-babies/. 

  66. http://www.mturk.com. 

  67. Found. Trends®, Comput. Graph. Vis. Kovashka 10 3 177 2016 10.1561/0600000071 Crowdsourcing in computer vision 

  68. www.computervisionblog.com. 

  69. W. Brendel, M. Bethge, Comment on biologically inspired protection of deep networks from adversarial attacks, arXiv:1704.01547. 

  70. A. Nayebi, S. Ganguli, Biologically inspired protection of deep networks from adversarial attacks, arXiv:1703.09202. 

  71. J. Vis. Borji 13 10 18-18 2013 10.1167/13.10.18 Objects do not predict fixations better than early saliency: a re-analysis of Einhäuser et al.’s data 

  72. Torralba 1521 2011 Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference On Unbiased look at dataset bias 

  73. Image Vis. Comput. Chellappa 30 8 467 2012 10.1016/j.imavis.2012.03.008 Mathematical statistics and computer vision 

  74. Proc. Natl. Acad. Sci. Geman 113 34 9384 2016 10.1073/pnas.1609793113 Opinion: science in the age of selfies 

  75. Image Vis. Comput. Yuille 30 8 469 2012 10.1016/j.imavis.2011.12.013 Computer vision needs a core and foundations 

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