Situational object boundary detection
Jasper Uijlings,Vittorio Ferrari +1 more
- pp 4712-4721
TLDR
In this article, the authors propose to train a specialized object boundary detector for each of the situations and then classify a test image into these situations using its context, which they model by global image appearance.Abstract:
Intuitively, the appearance of true object boundaries varies from image to image. Hence the usual monolithic approach of training a single boundary predictor and applying it to all images regardless of their content is bound to be suboptimal. In this paper we therefore propose situational object boundary detection: We first define a variety of situations and train a specialized object boundary detector for each of them using [10]. Then given a test image, we classify it into these situations using its context, which we model by global image appearance. We apply the corresponding situational object boundary detectors, and fuse them based on the classification probabilities. In experiments on ImageNet [35], Microsoft COCO [24], and Pascal VOC 2012 segmentation [13] we show that our situational object boundary detection gives significant improvements over a monolithic approach. Additionally, our method substantially outperforms [17] on semantic contour detection on their SBD dataset.read more
Citations
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Proceedings ArticleDOI
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
TL;DR: Wu et al. as mentioned in this paper proposed a boundary-aware face alignment algorithm by utilizing boundary lines as the geometric structure of a human face to help facial landmark localisation, which achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin.
Journal ArticleDOI
Video Object Segmentation without Temporal Information
Kevis-Kokitsi Maninis,Sergi Caelles,Yuhua Chen,Jordi Pont-Tuset,Laura Leal-Taixé,Daniel Cremers,L. Van Gool +6 more
TL;DR: Semantic One-Shot Video Object Segmentation is presented, based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one shot).
Proceedings ArticleDOI
Object Contour Detection with a Fully Convolutional Encoder-Decoder Network
TL;DR: A deep learning algorithm for contour detection with a fully convolutional encoder-decoder network that generalizes well to unseen object classes from the same supercategories on MS COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning.
Posted Content
Object Contour Detection with a Fully Convolutional Encoder-Decoder Network
TL;DR: Li et al. as mentioned in this paper proposed a fully convolutional encoder-decoder network to detect higher-level object contours, which can match state-of-the-art edge detection on BSDS500 with fine-tuning.
Journal ArticleDOI
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
TL;DR: Convolutional Oriented Boundaries gives a significant leap in performance over the state-of-the-art, and generalizes very well to unseen categories and datasets, and learning to estimate not only contour strength but also orientation provides more accurate results.
References
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