This book addresses the fundamentals of randomized control clinical trials, devoting a chapter to each of the critical areas of a protocol. The new edition is revised and expanded, with the number of ...examples illustrating the fundamentals considerably increased.
New interactive applications, artifacts, and systems are constantly being added to our environments, and there are some concerns in the human-computer interaction research community that increasing ...interactivity might not be just to the good. But what is it that is supposed to be increasing, and how could we determine whether it is? To approach these issues in a systematic and analytical fashion, relying less on common intuitions and more on clearly defined concepts and when possible quantifiable properties, we take a renewed look at the notion of interactivity and related concepts. The main contribution of this article is a number of definitions and terms, and the beginning of an attempt to frame the conditions of interaction and interactivity. Based on this framing, we also propose some possible approaches for how interactivity can be measured.
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet 1 and Fast R-CNN 2 have reduced the running time of these ...detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network(RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model 3, our detection system has a frame rate of 5 fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.
What Makes for Effective Detection Proposals? Hosang, Jan; Benenson, Rodrigo; Dollar, Piotr ...
IEEE transactions on pattern analysis and machine intelligence,
2016-April-1, 2016-Apr, 2016-4-1, 20160401, Volume:
38, Issue:
4
Journal Article
Peer reviewed
Open access
Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and ...widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines regarding proposal repeatability, ground truth annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM, R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object detection improving proposal localisation accuracy is as important as improving recall. We introduce a novel metric, the average recall (AR), which rewards both high recall and good localisation and correlates surprisingly well with detection performance. Our findings show common strengths and weaknesses of existing methods, and provide insights and metrics for selecting and tuning proposal methods.
This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images. We present the Rotation Region Proposal Networks , which are designed to generate ...inclined proposals with text orientation angle information. The angle information is then adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation. The Rotation Region-of-Interest pooling layer is proposed to project arbitrary-oriented proposals to a feature map for a text region classifier. The whole framework is built upon a region-proposal-based architecture, which ensures the computational efficiency of the arbitrary-oriented text detection compared with previous text detection systems. We conduct experiments using the rotation-based framework on three real-world scene text detection datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.
Achieving a distribution of benefits derived from the use of genetic resources (GR) and traditional knowledge (TK) has proven to be a target difficult to achieve. For this reason, the objective of ...this thesis is to find the key elements useful for a feasible implementation of ABS. Such elements respond to the problems evidenced throughout this work regarding the difficulties experienced so far in the operationalisation of ABS. Those problems are, (i) that developing proposals for the application of legal frameworks on this very specialised, complex, fragmented, and highly political issue, requires more than one approach, (ii) that the accessible proposals on how to address ABS are predominantly theoretical, and (iii) that there seems to be resistance to the inclusion of new aspects in the discussion on ABS. Therefore, the hypothesis of this thesis is that the experience gained by countries in the implementation of ABS laws provides practical ways to solve some of the issues related to the achievement of benefit-sharing that should be explored to complement the existing theoretical proposals. For that reason, the adoption of a practical rather than a theoretical approach has been preferred. However, solving those problems requires theoretical support. Thus, the analysis found in López, de Sousa Santos, and McCann and March have been acknowledged. From different perspectives, these authors support the creation of legal systems according to the way people behave in their daily life. Fundamental aspects taken into consideration in the current study include the variety of conceptual recommendations aimed to achieve ABS. Another aspect is the legal frameworks and mutually agreed terms (MATs) available in the ABS Clearing House (ABSCH) of the CBD. This work concludes that the most significant obstacles to effective implementation of ABS are: (i) the national/bilateral approach to the CBD; (ii) the lack of specific regulation for access to GR ex-situ in the CBD; and, (iii) the application of the concept of public domain in the ABS context. Due to the lack of agreement between the Parties concerned, these obstacles are not about to be amended soon, and, for now, possible solutions can only be sought through national laws. This thesis considers that benefit-sharing could be better addressed if provider countries were to abandon the current schema of entering into single negotiations every time a GR or a TK is accessed. This task, together with controlling and monitoring all the different ways these resources could be used once access is granted, seems so vast that it would be very difficult to accomplish. Instead, it is suggested that a mandatory sharing of non-monetary benefits with a voluntary sharing of monetary benefits is the best solution. The sharing of benefits could be encouraged by: (i) introducing a certificate of compliance upon actual sharing of non-monetary benefits; and (ii) providing tax benefits for the sharing of monetary benefits. The use of mutually agreed terms (MATs) is recommended as a tool to facilitate dispute resolution at an international level. Given the potential that the global multilateral benefit-sharing mechanism (GMBSM), proposed in Article 10 of the NP, has in achieving benefit-sharing, the implementation of a basic GMBSM is suggested. Modifications of this mechanism could be introduced by the Parties as they reach new agreements.
The goal of this paper is to perform 3D object detection in the context of autonomous driving. Our method aims at generating a set of high-quality 3D object proposals by exploiting stereo imagery. We ...formulate the problem as minimizing an energy function that encodes object size priors, placement of objects on the ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground. We then exploit a CNN on top of these proposals to perform object detection. In particular, we employ a convolutional neural net (CNN) that exploits context and depth information to jointly regress to 3D bounding box coordinates and object pose. Our experiments show significant performance gains over existing RGB and RGB-D object proposal methods on the challenging KITTI benchmark. When combined with the CNN, our approach outperforms all existing results in object detection and orientation estimation tasks for all three KITTI object classes. Furthermore, we experiment also with the setting where LIDAR information is available, and show that using both LIDAR and stereo leads to the best result.
In this paper, we propose an adaptive region proposal scheme with feature channel regularization to facilitate robust object tracking. We consider tracking as a linear regression problem and an ...ensemble of correlation filters is trained on-line to distinguish the foreground target from the background. Further, we integrate adaptively learned region proposals into an enhanced two-stream tracking framework based on correlation filters. For the tracking stream, we learn two-stage cascade correlation filters on deep convolutional features to ensure competitive tracking performance. For the detection stream, we employ adaptive region proposals, which are effective in recovering target objects from tracking failures caused by heavy occlusion or out-of-view movement. In contrast to traditional tracking-by-detection methods using random samples or sliding windows, we perform target re-detection over adaptively learned region proposals. Since region proposals naturally take the objectness information into account, we show that the proposed adaptive region proposals can handle the challenging scale estimation problem as well. In addition, we observe the channel redundancy and noisy of feature representation, especially for the convolutional features. Thus, we apply a channel regularization to the correlation filter learning. Extensive experimental validations on OTB, VOT and UAV-123 datasets demonstrate that the proposed method performs favorably against state-of-the-art tracking algorithms.
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