Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks

Yang, Yi; Lu, Xiankai; Shen, Jianbing; Crandall, David J.; Shao, Ling

doi:10.1109/iccv.2019.00933

Cited by 243 publications

(131 citation statements)

References 64 publications

(136 reference statements)

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“…Now, we are working on an adaptation of the proposed segmentation method into 3D [18,27]. We are also considering assessing the method on biomedical images and more experimental comparisons with very popular, fully convolutional approaches [29,54]. Finally, we plan to tackle a more complex task of semantic segmentation.…”

Section: Discussionmentioning

confidence: 99%

GPU-accelerated image segmentation based on level sets and multiple texture features

Reska

Krȩtowski

2020

Multimed Tools Appl

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In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

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Section: Discussionmentioning

confidence: 99%

GPU-accelerated image segmentation based on level sets and multiple texture features

Reska

Krȩtowski

2020

Multimed Tools Appl

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“…With their popularity in the field of natural language processing [8,39,43,49,60], attention modeling is rapidly adopted in various computer vision tasks, such as image recognition [14,23,58,66,73], domain adaptation [67,83], human pose estimation [9,63,77], object detection [4] and image generation [76,81,86]. Further, co-attention mechanisms become an essential tool in many vision-language applications and sequential modeling tasks, such as visual question answering [41,44,75,78], visual dialog [74,84], vision-language navigation [68], and video segmentation [42,61], showing its effectiveness in capturing the underlying relations between different entities. Inspired by the general idea of attention mechanisms, this work leverages co-attention to mine semantic relations within training image pairs, which helps the classifier network learn complete object patterns and generate precise object localization maps.…”

Section: Related Workmentioning

confidence: 99%

Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation

Sun

Yang

Dai

et al. 2020

Computer Vision – ECCV 2020

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191

149

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This paper studies the problem of learning semantic segmentation from image-level supervision only. Current popular solutions leverage object localization maps from classifiers as supervision signals, and struggle to make the localization maps capture more complete object content. Rather than previous efforts that primarily focus on intra-image information, we address the value of cross-image semantic relations for comprehensive object pattern mining. To achieve this, two neural coattentions are incorporated into the classifier to complimentarily capture cross-image semantic similarities and differences. In particular, given a pair of training images, one co-attention enforces the classifier to recognize the common semantics from co-attentive objects, while the other one, called contrastive co-attention, drives the classifier to identify the unshared semantics from the rest, uncommon objects. This helps the classifier discover more object patterns and better ground semantics in image regions. In addition to boosting object pattern learning, the co-attention can leverage context from other related images to improve localization map inference, hence eventually benefiting semantic segmentation learning. More essentially, our algorithm provides a unified framework that handles well different WSSS settings, i.e., learning WSSS with (1) precise image-level supervision only, (2) extra simple single-label data, and (3) extra noisy web data. It sets new state-of-the-arts on all these settings, demonstrating well its efficacy and generalizability.

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“…Pixels that are similar in properties combine together. The selection standard is referred to as the threshold value (Kaur & Kaur, 2014); Nishanth & Karthik, 2015; Lu, Ma, Ni, & Yang, 2019; Wang, Lu, Shen, Crandall, & Shao, 2019). Thresholding is a simple method of segmentation.…”

Section: Automatic Detection Of Exudates From Retinal Imagesmentioning

confidence: 99%

Classification of retinal images based on convolutional neural network

El-Hag

Sedik

El‐Shafai

et al. 2020

Microscopy Res & Technique

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Automatic detection of maculopathy disease is a very important step to achieve high‐accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.

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Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks

Cited by 243 publications

References 64 publications

GPU-accelerated image segmentation based on level sets and multiple texture features

GPU-accelerated image segmentation based on level sets and multiple texture features

Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation

Classification of retinal images based on convolutional neural network

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