MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation

By Jiajun Wu, Yibiao Zhao, Jun-Yan Zhu, Siwei Luo, Zhuowen Tu. 2014

151 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

SimpleClick: Interactive Image Segmentation with Simple Vision Transformers (2022, methodology): “Much research has been devoted to exploring interactive image segmentation with different interaction types, such as bounding boxes [45], polygons [1], clicks [40], scribbles [43], and their combinations [49].”
FocalClick: Towards Practical Interactive Image Segmentation (2022, background): “It enables users to annotate masks conveniently using simple interactions like scribbles [1, 11, 21], boxes [20, 34, 40], or clicks [5, 18, 27, 35, 36, 44].”
Reviving Iterative Training with Mask Guidance for Interactive Segmentation (2021, background): “For example, one of the simplest types of interactions is a bounding box, that is used in [23, 18, 24].”
Deep Extreme Cut: From Extreme Points to Object Segmentation (2017, background): “One of the most common ways to perform weakly supervised segmentation is drawing a bounding box around the object of interest [4, 38, 32, 18].”
Extreme Clicking for Efficient Object Annotation (2017, methodology): “To reduce annotation time many interactive segmentation techniques have been proposed, which require the user to input either a bounding box around the object [42, 51, 71], or scribbles [3, 16, 24, 26, 27, 42, 50, 64, 65, 72], or clicks [31, 69].”
Deep multiple instance learning for image classification and auto-annotation (2015, background): “Examples include object and face detection [43], visual categorization [42], segmentation [45] and image retrieval [24].”
FocusCut: Diving into a Focus View in Interactive Segmentation (2022, background): “For the former, the interactive modes are widely explored and mainly based on the bounding box [50], the polygon [1, 6, 32], clicks [2, 29, 36], scribbles [3, 48], and some combinations [34, 52].”
Interactive Object Segmentation With Inside-Outside Guidance (2022, background): “Thus, interactive segmentation, which allows the human annotators to quickly extract the object-ofinterest by providing some user inputs such as bounding boxes [2], [35], [36] or clicks [37], [38], [39], [40], appears to be an attractive and efficientway to reduce the annotation effort.”
Conditional Diffusion for Interactive Segmentation (2021, methodology): “Human could provide bounding boxes [26, 30, 15], scribbles [16, 6, 1], or clicks [27, 20, 32, 11] to express the segmentation intentions, which guide the algorithm for the mask extraction process.”
Interactive Image Segmentation via Backpropagating Refinement Scheme (2019, background): “[51] over-segment an image into superpixels and generate the foreground and background bags for multiple instance learning.”
Interactive Object Segmentation With Inside-Outside Guidance (2020, background): “Interactive Segmentation: Most interactive segmentation methods target at relieving human effort when labeling pixel-level annotations with the help of bounding box [66, 64, 52], clicks [67, 38, 37, 45] or contours [7, 1, 42] as guidance.”
Loosecut: Interactive image segmentation with loosely bounded boxes (2015, methodology): “Metrics: As in [18] [17] [13], we use Error Rate to evaluate an interactive image segmentation by counting the percentage of misclassified pixels inside the bounding box.”
Y Qualification Main task Y Return N N Y User qualified ? N Feedback Pass ? Pass ? Annotator training Annotating images (2017, methodology): “To reduce annotation time many interactive segmentation techniques have been proposed, which require the user to input either a bounding box around the object [42, 51, 71], or scribbles [3, 16, 24, 26, 27, 42, 50, 64, 65, 72], or clicks [31, 69].”
PseudoClick: Interactive Image Segmentation with Click Imitation (2022, methodology): “While different interaction types have been investigated, including clicks [10,11], bounding boxes [12,13,14], and scribbles [15,16], we only focus on click-based interactive segmentation because it has the simplest interaction and well-established ar X iv :2 20 7.”