In this paper, we provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter. This is an unrealistic assumption. The design bias has led to a saturated high performance for state of the art SOD models when evaluated on existing datasets. The models, however, still perform far from being satisfactory when applied to real-world daily scenes. Based on our analyses, we first identify 7 crucial aspects that a comprehensive and balanced dataset should fulfill. Then, we propose a new high quality dataset and update the previous saliency benchmark. Specifically, our dataset called , Salient Objects in Clutter, includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes () that reflect common challenges in real-world scenes, and can help 1) gain a deeper insight into the SOD problem, 2) investigate the pros and cons of the SOD models, and 3) objectively assess models from different perspectives. Finally, we report attribute-based performance assessment on our SOC dataset. We believe that our dataset and results will open new directions for future research on salient object detection.
&1& @cite_2 & 2015&CVPR & 3,340 & MB + P & --- & & & & &2& @cite_1 & 2015&CVPR& 8,000 & MK & GoogLeNet& & & & &3& @cite_33 & 2015& CVPR& 2,500 & MB & --- & & & & &4& @cite_5 & 2016&CVPR & 2,500 & MB & VGGNet & & & & &5& @cite_30 & 2016& CVPR& 9,000& MK &VGGNet& & & & &6& @cite_42 & 2016&CVPR & 9,500 & MK+D & VGGNet& & & & &7& @cite_8 & 2016 & ECCV & 10,103 & P2010 & --- & & & & &8& @cite_14 & 2016 & TNNLS & 9,000 & MK&---& & & & &9& @cite_16 & 2017&WACV & 6,000 & MK &ResNet-101& & & & &10& @cite_9 & 2017&CVPR & 2,500 & MB & VGGNet& & & & &11& @cite_45 & 2017&CVPR & 2,500 & MB &VGGNet& & & & &12& @cite_48 & 2017&ICCV & 10,000 & MK &VGGNet& & & & &13& @cite_17 & 2017&ICCV & 10,000 & MK &---& & & &
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These Ecole polytechnique federale de Lausanne EPFL, n° 2366 (2001)Faculte informatique et communicationsJury: Paolo Frasconi, Roger Hersch, Martin Rajman, Jurgen Schmidhuber Public defense: 2001-4-6 Reference doi:10.5075 epfl-thesis-2366Print copy in library catalog Record created on 2005-03-16, modified on 2016-08-08 In this paper we settle several longstanding open problems in theory of indexability and external orthogonal range searching. In the rst part of the paper, we apply the theory of indexability to the problem of two-dimensional range searching. We show that the special case of 3-sided querying can be solved with constant redundancy and access overhead. From this, we derive indexing schemes for general 4-sided range queries that exhibit an optimal tradeo between redundancy and access overhead. In the second part of the paper, we develop dynamic external memory data structures for the two query types. Our structure for 3-sided queries occupies O(N=B) disk blocks, and it supports insertions and deletions in O(log B N) I Os and queries in O(log B N + T=B) I Os, where B is the disk block size, N is the number of points, and T is the query output size. These bounds are optimal. Our structure for general (4-sided) range searching occupies O (N=B)(log(N=B))= log log B N disk blocks and answers queries in O(log B N + T=B) I Os, which are optimal. It also supports updates in O (log B N)(log(N=B))= log log B N I Os. Center for Geometric Computing, Department of Computer Science, Duke University, Box 90129, Durham, NC 27708 0129. Supported in part by the U.S. Army Research O ce through MURI grant DAAH04 96 1 0013 and by the National Science Foundation through ESS grant EIA 9870734. Part of this work was done while visiting BRICS, Department of Computer Science, University of Aarhus, Denmark. Email: large@cs.duke.edu. yDepartment of Computer Sciences, University of Texas at Austin, Austin, TX 78712-1188. Email vsam@cs.utexas.edu zCenter for Geometric Computing, Department of Computer Science, Duke University, Box 90129, Durham, NC 27708 0129. Supported in part by the U.S. Army Research O ce through MURI grant DAAH04 96 1 0013 and by the National Science Foundation through grants CCR 9522047 and EIA 9870734. Part of this work was done while visiting BRICS, Department of Computer Science, University of Aarhus, Denmark and I.N.R.I.A., Sophia Antipolis, France. Email: jsv@cs.duke.edu. This paper describes the SemEval-2014, Task 7 on the Analysis of Clinical Text and presents the evaluation results. It focused on two subtasks: (i) identification (Task A) and (ii) normalization (Task B) of diseases and disorders in clinical reports as annotated in the Shared Annotated Resources (ShARe) 1 corpus. This task was a follow-up to the ShARe CLEF eHealth 2013 shared task, subtasks 1a and 1b, 2 but using a larger test set. A total of 21 teams competed in Task A, and 18 of those also participated in Task B. For Task A, the best system had a strict F1-score of 81.3, with a precision of 84.3 and recall of 78.6. For Task B, the same group had the best strict accuracy of 74.1. The organizers have made the text corpora, annotations, and evaluation tools available for future research and development at the shared task website. 3 In this paper, we investigate the application of text classification methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the influence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble system combining the output of multiple SVM classifiers. We report results of 98 average F1 score in predicting a case ruling, 96 F1 score for predicting the law area of a case, and 87.07 F1 score on estimating the date of a ruling.