What makes for effective detection proposals?

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.Comment: TPAMI final version, duplicate proposals removed in experiment

St. Mary\u27s Parish House: Reuse and Rehabilitaion Feasibility Report

Most windows in the structure are historic and in fair condition, consisting of double-hung, six-over-six, divided-light windows throughout most of the building and 12-over-8 divided- light windows in the gymnasium. Windows on the first story of the south façade, in the school addition, are wooden and one-over-one

DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained CNN, combining the localization accuracy of the early layers with the high informative-ness (and hence recall) of the later layers. To this end, we build an inverse cascade that, going backward from the later to the earlier convolutional layers of the CNN, selects the most promising locations and refines them in a coarse-to-fine manner. The method is efficient, because i) it re-uses the same features extracted for detection, ii) it aggregates features using integral images, and iii) it avoids a dense evaluation of the proposals thanks to the use of the inverse coarse-to-fine cascade. The method is also accurate. We show that our DeepProposals outperform most of the previously proposed object proposal and action proposal approaches and, when plugged into a CNN-based object detector, produce state-of-the-art detection performance.Comment: 15 page

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

The problem of computing category agnostic bounding box proposals is utilized as a core component in many computer vision tasks and thus has lately attracted a lot of attention. In this work we propose a new approach to tackle this problem that is based on an active strategy for generating box proposals that starts from a set of seed boxes, which are uniformly distributed on the image, and then progressively moves its attention on the promising image areas where it is more likely to discover well localized bounding box proposals. We call our approach AttractioNet and a core component of it is a CNN-based category agnostic object location refinement module that is capable of yielding accurate and robust bounding box predictions regardless of the object category. We extensively evaluate our AttractioNet approach on several image datasets (i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on all of them state-of-the-art results that surpass the previous work in the field by a significant margin and also providing strong empirical evidence that our approach is capable to generalize to unseen categories. Furthermore, we evaluate our AttractioNet proposals in the context of the object detection task using a VGG16-Net based detector and the achieved detection performance on COCO manages to significantly surpass all other VGG16-Net based detectors while even being competitive with a heavily tuned ResNet-101 based detector. Code as well as box proposals computed for several datasets are available at:: https://github.com/gidariss/AttractioNet.Comment: Technical report. Code as well as box proposals computed for several datasets are available at:: https://github.com/gidariss/AttractioNe

Spartan Daily, June 7, 1935

Volume 23, Issue 149https://scholarworks.sjsu.edu/spartandaily/2326/thumbnail.jp

Sequential Optimization for Efficient High-Quality Object Proposal Generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5% and 16.7% on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.Comment: Accepted by TPAM

Sequential optimization for efficient high-quality object proposal generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING ++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster

High magnetic field pulsars and magnetars: a unified picture

We propose a unified picture of high magnetic field radio pulsars and magnetars by arguing that they are all rotating high-field neutron stars, but have different orientations of their magnetic axes with respective to their rotation axes. In strong magnetic fields where photon splitting suppresses pair creation near the surface, the high-field pulsars can have active inner accelerators while the anomalous X-ray pulsars cannot. This can account for the very different observed emission characteristics of the anomalous X-ray pulsar 1E 2259+586 and the high field radio pulsar PSR J1814-1744. A predicted consequence of this picture is that radio pulsars having surface magnetic field greater than about $2\times 10^{14}$ G should not exist.Comment: 5 pages, emulateapj style, accepted for publication in the ApJ Letter

Viscoelastically prestressed polymeric matrix composites: An overview

Elastically prestressed polymeric matrix composites exploit the principles of prestressed concrete, i.e. fibres are stretched elastically during matrix curing. On matrix solidification, compressive stresses are created within the matrix, counterbalanced by residual fibre tension. Unidirectional glass fibre elastically prestressed polymeric matrix composites have demonstrated 25–50% improvements in impact toughness, strength and stiffness compared with control (unstressed) counterparts. Although these benefits require no increase in section dimensions or weight, the need to apply fibre tension during curing can impose restrictions on processing and product geometry. Also, fibre–matrix interfacial creep may eventually cause the prestress to deteriorate. This paper gives an overview of an alternative approach: viscoelastically prestressed polymeric matrix composites. Here, polymeric fibres are subjected to tensile creep, the applied load being removed before the fibres are moulded into the matrix. Following matrix curing, viscoelastic recovery mechanisms cause the previously strained fibres to impart compressive stresses to the matrix. Since fibre stretching and moulding operations are decoupled, viscoelastically prestressed polymeric matrix composite production offers considerable flexibility. Also, the potential for deterioration through fibre–matrix creep is offset by longer term viscoelastic recovery mechanisms. To date, viscoelastically prestressed viscoelastically prestressed polymeric matrix composites have been produced from fibre reinforcements such as nylon 6,6, ultra-high molecular weight polyethylene and bamboo. Compared with control counterparts, mechanical property improvements are similar to those of elastically prestressed polymeric matrix composites. Of major importance, however, is longevity: through accelerated ageing, nylon fibre-based viscoelastically prestressed viscoelastically prestressed polymeric matrix composites show no deterioration in mechanical performance over a duration equivalent to ∼25 years at 50℃ ambient. Potential applications include crashworthy and impact-absorbing structures, dental materials, prestressed precast concrete and shape-changing (morphing) structures