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Context-Reinforced Semantic Segmentation
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Recent efforts have shown the importance of context on deep convolutional neural network based semantic segmentation. Among others, the predicted segmentation map (p-map) itself which encodes rich high-level semantic cues (e.g. objects and layout) can be regarded as a promising source of context.
Yizhou Zhou +3 more
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Averse Deep Semantic Segmentation
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019Semantic segmentation consists in predicting whether any given pixel is part of the object of interest or not. Two types of errors are therefore possible: false positives and false negatives. For visualization and emphasis purposes, we might want to put special effort into reducing one type of error in detriment of the other.
Ricardo P. M. Cruz +2 more
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Deep Learning for Semantic Segmentation
2021Segmentation is a fundamental problem but not the ultimate goal, it is a stepping stone to higher level application problems. It consists in associating each of the low-level image pixels to the class they locally represent. This task completes image analysis tasks such as visual scene classification and instance level object detection. It enables high
Benoit, Alexandre +4 more
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Transferable Attacks for Semantic Segmentation
We analyze the performance of semantic segmentation models w.r.t. adversarial attacks. We observe that the adversarial examples generated from a source model fail to attack the target models, i.e. the conventional attack methods [1, 2] do not transfer well to the target models, making it necessary to study the transferable attacks, in particular ...Mengqi He, Jing Zhang 0052, Xin Yu 0002
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Spatiotemporal semantic video segmentation
2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008In this paper, we propose a framework to extend semantic labeling of images to video shot sequences and achieve efficient and semantic-aware spatiotemporal video segmentation. This task faces two major challenges, namely the temporal variations within a video sequence which affect image segmentation and labeling, and the computational cost of region ...
Eric Galmar +3 more
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Attention Forest for Semantic Segmentation
2018Semantic segmentation is a classical task in computer vision. In this paper, we target to address the low confidence regions which traditional CNN can not solve very well in semantic segmentation task. Depending on different characteristics of low confidence regions, an adaptive and robust attention mechanism is important to focus on the informative ...
Jingbo Wang 0003, Yajie Xing, Gang Zeng
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Semantic Segmentation with Peripheral Vision
2020Deep convolutional neural networks exhibit exceptional performance on many computer vision tasks, including image semantic segmentation. Pre-trained networks trained on a relevant and large benchmark have a notable impact on these successful achievements.
Mohammad Hamed Mozaffari, Won-Sook Lee
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Semantic Segmentation of Fisheye Images
2019Semantic segmentation of fisheye images (e.g., from action-cameras or smartphones) requires different training approaches and data than those of rectilinear images obtained using central projection. The shape of objects is distorted depending on the distance between the principal point and the object position in the image. Therefore, classical semantic
Gregor Blott +2 more
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Semantic Co-segmentation in Videos
2016Discovering and segmenting objects in videos is a challenging task due to large variations of objects in appearances, deformed shapes and cluttered backgrounds. In this paper, we propose to segment objects and understand their visual semantics from a collection of videos that link to each other, which we refer to as semantic co-segmentation.
Yi-Hsuan Tsai +2 more
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