Interactive Boundary Prediction for Object Selection

By Hoang Le, Long Mai, Brian L. Price, Scott D. Cohen, Hailin Jin, Feng Liu. 2018

38 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Getting to 99% Accuracy in Interactive Segmentation (2020, methodology): “All convolutional layers are followed by LeakyRelu activations [38]. dled together, we propose to introduce the clicks sequentially, starting from a single click, and adopting the same sequential scheme used to evaluate interactive segmentation algorithms [6, 9–13, 35, 39].”
Conditional Diffusion for Interactive Segmentation (2021, unknown): “In a standard pipeline, the first step is encoding clicks into distance maps [32, 27, 11, 31], Gaussian maps [20, 23, 21, 14], or super-pixels [22]; next, encoded maps is concatenated with the original image and fed into a segmentation network to make predictions.”
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input (2019, methodology): “Existing deep learning-based interactive image segmentation methods [16, 18, 23, 26, 27, 36] have demonstrated significant improvement over the previous techniques using”
FocusCut: Diving into a Focus View in Interactive Segmentation (2022, background): “The boundary clicks [19,24] are also adopted as an effective interaction.”
Large-Scale Interactive Object Segmentation With Human Annotators (2019, background): “It has been revisited in the recent years under the deep learning umbrella [36, 20, 10, 34, 22, 16, 15, 1], focusing on how to best use clicks and scribbles corrections.”
First-Person View Hand Segmentation of Multi-Modal Hand Activity Video Dataset (2020, methodology): “A successful method is to have a neural network produce polygon annotations of objects interactively using humans-in-the-loop to improve the accuracy [1, 31, 39].”
PhraseClick: Toward Achieving Flexible Interactive Segmentation by Phrase and Click (2020, background): “These inputs usually come in the form of user clicks/strokes [5,28,34,41,51,58] or bounding boxes [16, 29, 49, 57].”
Deepstrip: High-Resolution Boundary Refinement (2020, background): “Recent decades have witnessed considerable effort to improve the boundary quality of an object that has been detected [41, 35, 32, 18, 37, 47, 16, 22] or segmented [11, 34, 24, 7].”
Deep Interactive Thin Object Selection (2021, methodology): “…in which user clicks ﬁrst go through Euclidean distance transformation before concatenated with the image for training an FCN. Recent methods focus on better exploiting useful context from user-provided inputs [23, 15, 27] or deriving better representation for user clicks [32, 45, 19, 31].”