Amodal Instance Segmentation

We consider the problem of amodal instance segmentation, the objective of which is to predict the region encompassing both visible and occluded parts of each object. Thus far, the lack of publicly available amodal segmentation annotations has stymied the development of amodal segmentation methods. In this paper, we sidestep this issue by relying solely on standard modal instance segmentation annotations to train our model. The result is a new method for amodal instance segmentation, which represents the first such method to the best of our knowledge. We demonstrate the proposed method’s effectiveness both qualitatively and quantitatively.

[1]  C. V. Jawahar,et al.  The truth about cats and dogs , 2011, 2011 International Conference on Computer Vision.

[2]  Jitendra Malik,et al.  Amodal Completion and Size Constancy in Natural Scenes , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[3]  Derek Hoiem,et al.  Learning to localize detected objects , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Jitendra Malik,et al.  Recognition using regions , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[6]  Jian Dong,et al.  Towards Unified Object Detection and Semantic Segmentation , 2014, ECCV.

[7]  Pushmeet Kohli,et al.  Energy minimization for linear envelope MRFs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[10]  Sanja Fidler,et al.  Bottom-Up Segmentation for Top-Down Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Jian Sun,et al.  Convolutional feature masking for joint object and stuff segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Vibhav Vineet,et al.  Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Subhransu Maji,et al.  Semantic contours from inverse detectors , 2011, 2011 International Conference on Computer Vision.

[14]  Shimon Ullman,et al.  Class-Specific, Top-Down Segmentation , 2002, ECCV.

[15]  Jitendra Malik,et al.  Simultaneous Detection and Segmentation , 2014, ECCV.

[16]  Pushmeet Kohli,et al.  Graph Cut Based Inference with Co-occurrence Statistics , 2010, ECCV.

[17]  Ronan Collobert,et al.  Recurrent Convolutional Neural Networks for Scene Labeling , 2014, ICML.

[18]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Silvio Savarese,et al.  Beyond PASCAL: A benchmark for 3D object detection in the wild , 2014, IEEE Winter Conference on Applications of Computer Vision.

[20]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Cristian Sminchisescu,et al.  Object Recognition by Sequential Figure-Ground Ranking , 2012, International Journal of Computer Vision.

[22]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[23]  Yi Yang,et al.  Layered Object Models for Image Segmentation , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Svetlana Lazebnik,et al.  Scene Parsing with Object Instances and Occlusion Ordering , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  LeCunYann,et al.  Learning Hierarchical Features for Scene Labeling , 2013 .

[26]  YangYi,et al.  Layered Object Models for Image Segmentation , 2012 .

[27]  Anat Levin,et al.  Learning to Combine Bottom-Up and Top-Down Segmentation , 2006, ECCV.

[28]  Kristen Grauman,et al.  Efficient region search for object detection , 2011, CVPR 2011.

[29]  Andrew Zisserman,et al.  OBJ CUT , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[30]  Jian Sun,et al.  Instance-Aware Semantic Segmentation via Multi-task Network Cascades , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Jitendra Malik,et al.  Hypercolumns for object segmentation and fine-grained localization , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Jianbo Shi,et al.  Object-Specific Figure-Ground Segregation , 2003, CVPR.

[33]  Joost van de Weijer,et al.  Harmony Potentials , 2011, International Journal of Computer Vision.

[34]  B. Schiele,et al.  Combined Object Categorization and Segmentation With an Implicit Shape Model , 2004 .

[35]  Jitendra Malik,et al.  Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Noah Snavely,et al.  OpenSurfaces , 2013, ACM Trans. Graph..

[37]  Yuandong Tian,et al.  Semantic Amodal Segmentation , 2015, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Neil A. Dodgson,et al.  Proceedings Ninth IEEE International Conference on Computer Vision , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[39]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  G. Kanizsa,et al.  Organization in Vision: Essays on Gestalt Perception , 1979 .

[41]  Jitendra Malik,et al.  Semantic segmentation using regions and parts , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Jitendra Malik,et al.  Iterative Instance Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).