Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images

53 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Object-Proposal Evaluation Protocol is ‘Gameable’ (2015, methodology): “Object proposals are evaluated using this metric in two ways: – plotting Recall-vs-#proposals by ﬁxing t – plotting Recall-vs-t by ﬁxing the #proposals in L .”
RGBD Salient Object Detection via Deep Fusion (2016, background): “essential for many applications, such as 3D content surveillance, retrieval, and image recognition [7], [8], [9].”
End-to-End Instance Segmentation with Recurrent Attention (2016, background): “The sequential decomposition idea for structured prediction is also explored in [20, 3].”
Reinforcement Learning for Visual Object Detection (2016, methodology): “More recently, reinforcement learning[36] has been applied to visual analysis problems like image classification[24, 19, 29], face detection[14], tracking and recognizing objects in video[2], learning a sequential policy for RGB-D semantic segmentation[1], or scanpath prediction[27].”
Weakly Supervised Object Boundaries (2015, background): “It is an enabling ingredient for many vision tasks such as image/video segmentation [1, 12], object proposals [17], object detection [37], and semantic labelling [2].”