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Visual Learning and Recognition: Difference between revisions
Visual Learning and Recognition (view source)
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===ION: Inside Out Network=== | ===ION: Inside Out Network=== | ||
Bell ''et al.'' <ref name="bell2016ion"></ref> | |||
The key idea is that we want a feature vector which uses features from multiple scales. | The key idea is that we want a feature vector which uses features from multiple scales. | ||
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<ref name="shotton2009texton">Jamie Shotton John Winn Carsten Rother Antonio Criminisi (2009) TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context. [https://www.microsoft.com/en-us/research/publication/textonboost-for-image-understanding-multi-class-object-recognition-and-segmentation-by-jointly-modeling-texture-layout-and-context/ Link]</ref> | <ref name="shotton2009texton">Jamie Shotton John Winn Carsten Rother Antonio Criminisi (2009) TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context. [https://www.microsoft.com/en-us/research/publication/textonboost-for-image-understanding-multi-class-object-recognition-and-segmentation-by-jointly-modeling-texture-layout-and-context/ Link]</ref> | ||
<ref name="shrivastava2016ohem">Abhinav Shrivastava, Abhinav Gupta, Ross Girshick (2016) Training Region-Based Object Detectors With Online Hard Example Mining. (CVPR 2016)[https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.html Link]</ref> | <ref name="shrivastava2016ohem">Abhinav Shrivastava, Abhinav Gupta, Ross Girshick (2016) Training Region-Based Object Detectors With Online Hard Example Mining. (CVPR 2016)[https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.html Link]</ref> | ||
<ref name="bell2016ion">Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick (2016). Inside-Outside Net: Detecting Objects in Context With Skip Pooling and Recurrent Neural Networks (CVPR 2016)</ref> | |||
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