Semantic Object Selection

Ahmed, Ejaz; Cohen, Scott; Price, Brian

doi:10.1109/cvpr.2014.403

Cited by 17 publications

(10 citation statements)

References 24 publications

(48 reference statements)

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“…Weakly Supervised Signals for Segmentation: Numerous alternatives to expensive pixel-level segmentation have been proposed and used in the literature. Image-level labels [27], noisy web labels [1,16] and scribble-level labels [20] are some of the supervisory signal that have been used to guide segmentation methods. Closer to our approach, [3] employs point-level supervision in the form of a single click to train a CNN for semantic segmentation and [26] uses central points of an imaginary bounding box to weakly supervise object detection.…”

Section: Related Workmentioning

confidence: 99%

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Deep Extreme Cut: From Extreme Points to Object Segmentation

Maninis

Caelles

Pont-Tuset

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

401

426

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Figure 1. Example results of DEXTR: The user provides the extreme clicks for an object, and the CNN produces the segmented masks. AbstractThis paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos. We do so by adding an extra channel to the image in the input of a convolutional neural network (CNN), which contains a Gaussian centered in each of the extreme points. The CNN learns to transform this information into a segmentation of an object that matches those extreme points.We demonstrate the usefulness of this approach for guided segmentation (grabcut-style), interactive segmentation, video object segmentation, and dense segmentation annotation. We show that we obtain the most precise results to date, also with less user input, in an extensive and varied selection of benchmarks and datasets. All our models and code are publicly available on

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Section: Related Workmentioning

confidence: 99%

“…DEXTR's annotations reach practically the same performance than ground truth when given the same number of annotated images. 1 10 100 1000 20…”

Section: Annotationmentioning

confidence: 99%

Deep Extreme Cut: From Extreme Points to Object Segmentation

Maninis

Caelles

Pont-Tuset

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

401

426

View full text Add to dashboard Cite

show abstract

“…2) Weakly supervised object segmentation: Conventional object segmentation approaches use scribbles or rectangular boxes to indicate the object of interest, while in our approach, only the semantic label of the object of interest is input to the system, similar to the semantic object selection [77]. We threshold our TD saliency map to identify definite foreground and background regions in an image, followed by Grabcut [73] to accurately segment out the object of interest.…”

Section: Applicationsmentioning

confidence: 99%

“…In all the three categories, we achieve state-of-theart performance compared to related co-segmentation [46], [62] and co-saliency [47] approaches. The semantic object selection [77] uses additional supervision by collecting positive training images with white background using an internet search. Inspite of this modification, they could only achieve an average accuracy of 63.73%, which is lower than our mean accuracy of 68.0% across 3 categories.…”

Section: Applicationsmentioning

confidence: 99%

Backtracking Spatial Pyramid Pooling-Based Image Classifier for Weakly Supervised Top–Down Salient Object Detection

Cholakkal

Johnson

Rajan

2018

IEEE Trans. on Image Process.

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Top-down saliency models produce a probability map that peaks at target locations specified by a task/goal such as object detection. They are usually trained in a fully supervised setting involving pixel-level annotations of objects. We propose a weakly supervised top-down saliency framework using only binary labels that indicate the presence/absence of an object in an image. First, the probabilistic contribution of each image region to the confidence of a CNN-based image classifier is computed through a backtracking strategy to produce top-down saliency. From a set of saliency maps of an image produced by fast bottom-up saliency approaches, we select the best saliency map suitable for the top-down task. The selected bottom-up saliency map is combined with the top-down saliency map. Features having high combined saliency are used to train a linear SVM classifier to estimate feature saliency. This is integrated with combined saliency and further refined through a multi-scale superpixel-averaging of saliency map. We evaluate the performance of the proposed weakly supervised topdown saliency and achieve comparable performance with fully supervised approaches. Experiments are carried out on seven challenging datasets and quantitative results are compared with 40 closely related approaches across 4 different applications. Code will be made publicly available.

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“…Webly Supervised Computer Vision The idea of utilizing web images for supervising computer vision algorithms has been explored in several tasks, such as object classification [28], object detection [29], object parts localization [30] and object segmentation [17,31,32,33]. Recently, Wei et al [17] also propose to use web images to train CNNs for semantic segmentation.…”

Section: Related Workmentioning

confidence: 99%

Webly Supervised Semantic Segmentation

Jin

Segovia

Süsstrunk

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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We propose a weakly supervised semantic segmentation algorithm that uses image tags for supervision. We apply the tags in queries to collect three sets of web images, which encode the clean foregrounds, the common backgrounds, and realistic scenes of the classes. We introduce a novel three-stage training pipeline to progressively learn semantic segmentation models. We first train and refine a class-specific shallow neural network to obtain segmentation masks for each class. The shallow neural networks of all classes are then assembled into one deep convolutional neural network for end-to-end training and testing. Experiments show that our method notably outperforms previous state-of-the-art weakly supervised semantic segmentation approaches on the PASCAL VOC 2012 segmentation benchmark. We further apply the class-specific shallow neural networks to object segmentation and obtain excellent results.

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Semantic Object Selection

Cited by 17 publications

References 24 publications

Deep Extreme Cut: From Extreme Points to Object Segmentation

Deep Extreme Cut: From Extreme Points to Object Segmentation

Backtracking Spatial Pyramid Pooling-Based Image Classifier for Weakly Supervised Top–Down Salient Object Detection

Webly Supervised Semantic Segmentation

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