The
Pascal
Visual Object Classes (VOC) challenge consists of two components: (i) a publicly available
dataset
of images together with ground truth annotation and standardised evaluation software; and ...(ii) an annual
competition
and workshop. There are five challenges: classification, detection, segmentation, action classification, and person layout. In this paper we provide a review of the challenge from 2008–2012. The paper is intended for two audiences:
algorithm designers
, researchers who want to see what the state of the art is, as measured by performance on the VOC datasets, along with the limitations and weak points of the current generation of algorithms; and,
challenge designers
, who want to see what we as organisers have learnt from the process and our recommendations for the organisation of future challenges. To analyse the performance of submitted algorithms on the VOC datasets we introduce a number of novel evaluation methods: a bootstrapping method for determining whether differences in the performance of two algorithms are significant or not; a normalised average precision so that performance can be compared across classes with different proportions of positive instances; a clustering method for visualising the performance across multiple algorithms so that the hard and easy images can be identified; and the use of a joint classifier over the submitted algorithms in order to measure their complementarity and combined performance. We also analyse the community’s progress through time using the methods of Hoiem et al. (Proceedings of European Conference on Computer Vision,
2012
) to identify the types of occurring errors. We conclude the paper with an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
The Pascal Visual Object Classes (VOC) Challenge Everingham, Mark; Van Gool, Luc; Williams, Christopher K. I. ...
International journal of computer vision,
06/2010, Letnik:
88, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The
Pascal
Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of ...images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as
the
benchmark for object detection.
This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
A significant proportion of the worldwide population is at risk of social isolation and loneliness as a result of the COVID-19 pandemic. We aimed to identify effective interventions to reduce social ...isolation and loneliness that are compatible with COVID-19 shielding and social distancing measures.
In this rapid systematic review, we searched six electronic databases (Medline, Embase, Web of Science, PsycINFO, Cochrane Database of Systematic Reviews and SCOPUS) from inception to April 2020 for systematic reviews appraising interventions for loneliness and/or social isolation. Primary studies from those reviews were eligible if they included: 1) participants in a non-hospital setting; 2) interventions to reduce social isolation and/or loneliness that would be feasible during COVID-19 shielding measures; 3) a relevant control group; and 4) quantitative measures of social isolation, social support or loneliness. At least two authors independently screened studies, extracted data, and assessed risk of bias using the Downs and Black checklist. Study registration: PROSPERO CRD42020178654. We identified 45 RCTs and 13 non-randomised controlled trials; none were conducted during the COVID-19 pandemic. The nature, type, and potential effectiveness of interventions varied greatly. Effective interventions for loneliness include psychological therapies such as mindfulness, lessons on friendship, robotic pets, and social facilitation software. Few interventions improved social isolation. Overall, 37 of 58 studies were of "Fair" quality, as measured by the Downs & Black checklist. The main study limitations identified were the inclusion of studies of variable quality; the applicability of our findings to the entire population; and the current poor understanding of the types of loneliness and isolation experienced by different groups affected by the COVID-19 pandemic.
Many effective interventions involved cognitive or educational components, or facilitated communication between peers. These interventions may require minor modifications to align with COVID-19 shielding/social distancing measures. Future high-quality randomised controlled trials conducted under shielding/social distancing constraints are urgently needed.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past ...decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
In this note, I study how the precision of a binary classifier depends on the ratio
of positive to negative cases in the test set, as well as the classifier's true and false-positive rates. This ...relationship allows prediction of how the precision-recall curve will change with
, which seems not to be well known. It also allows prediction of how
and the precision gain and recall gain measures of Flach and Kull (2015) vary with
.
This study empirically examines the role of shocks to macro-uncertainty in shaping the responses of stock market participants to firm-specific earnings news. Specifically, I find that investors place ...greater weight on bad news following an increase in macro-uncertainty. By contrast, I find that investors place equal weight on both good and bad news following a decrease in macro-uncertainty. Furthermore, my findings show that these effects are more pronounced (1) for firms whose prior returns are more correlated with macro-uncertainty, (2) for firms that experience abnormally low trading volume during the earnings announcement, (3) for firms with relatively lower levels of institutional ownership, and (4) for firms with relatively higher information uncertainty. In sum, these findings provide novel empirical evidence that investors behave in a manner consistent with ambiguity aversion, with the effects strongest among unsophisticated investors.
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Powder bed fusion (PBF) is one of seven different classes of additive manufacturing (AM) technologies identified by ASTM and ISO. In polymer PBF, an infra-red energy source ...selectively fuses powder particles layer-by-layer into a three-dimensional structure. This enables the production of parts without the use of a mold, which is useful for prototyping and low-volume production. The early research in polymer PBF has focused largely on exploring the expanded design space afforded by the technology and modeling the heat transfer during fabrication. These are aspects that emphasize the manufacturing process and resultant quality in a material agnostic manner. Early investigations into structure-process-property relationships focused on the industrially dominant polyamide family. Only recently has research been conducted towards expanding the PBF material portfolio beyond nylon-12 and its composites. Guiding this research is the knowledge gained from studying the behavior of polyamides in PBF, which resulted in pervasive guidelines for material screening and process parameter development in polymer PBF, including the concepts of a “stable sintering region” and utilizing the “energy melt ratio” to set machine parameters. However, these guidelines are largely empirical and disproportionately focus on process parameter effects on the mechanical properties of the printed parts, instead of the intrinsic polymer properties and first principles of polymer science and engineering.
This review categorically compiles the PBF AM literature by the three process sub-functions: powder recoating, energy input, and coalescence and cooling. The literature outlining the governing physics, structure-property-processing relationships enabling printing, and the process-structure-property relationships enabling targeted final part properties are discussed within each sub-function. Establishing these polymer-manufacturing relationships, both for printability and for final part property prediction, is important to aid in the identification and adaptation of existing polymers, and development of novel polymers, for PBF AM.
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