1,720,959 research outputs found
ContextMix: A context-aware data augmentation method for industrial visual inspection systems
Full text linkWhile deep neural networks have achieved remarkable performance, data
augmentation has emerged as a crucial strategy to mitigate overfitting and
enhance network performance. These techniques hold particular significance in
industrial manufacturing contexts. Recently, image mixing-based methods have
been introduced, exhibiting improved performance on public benchmark datasets.
However, their application to industrial tasks remains challenging. The
manufacturing environment generates massive amounts of unlabeled data on a
daily basis, with only a few instances of abnormal data occurrences. This leads
to severe data imbalance. Thus, creating well-balanced datasets is not
straightforward due to the high costs associated with labeling. Nonetheless,
this is a crucial step for enhancing productivity. For this reason, we
introduce ContextMix, a method tailored for industrial applications and
benchmark datasets. ContextMix generates novel data by resizing entire images
and integrating them into other images within the batch. This approach enables
our method to learn discriminative features based on varying sizes from resized
images and train informative secondary features for object recognition using
occluded images. With the minimal additional computation cost of image
resizing, ContextMix enhances performance compared to existing augmentation
techniques. We evaluate its effectiveness across classification, detection, and
segmentation tasks using various network architectures on public benchmark
datasets. Our proposed method demonstrates improved results across a range of
robustness tasks. Its efficacy in real industrial environments is particularly
noteworthy, as demonstrated using the passive component dataset.Comment: Accepted to EAA
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
No full textWith the development of 3D scanning technologies, 3D vision tasks have become a popular research area. Owing to the large amount of data acquired by sensors, unsupervised learning is essential for understanding and utilizing point clouds without an expensive annotation process. In this paper, we propose a novel framework and an effective auto-encoder architecture named “PSG-Net” for reconstruction-based learning of point clouds. Unlike existing studies that used fixed or random 2D points, our framework generates input-dependent point-wise features for the latent point set. PSG-Net uses the encoded input to produce point-wise features through the seed generation module and extracts richer features in multiple stages with gradually increasing resolution by applying the seed feature propagation module progressively. We prove the effectiveness of PSG-Net experimentally; PSG-Net shows state-of-the-art performances in point cloud reconstruction and unsupervised classification, and achieves comparable performance to counterpart methods in supervised completion
빠른 포인트 클라우드 분할을 위한 프로젝션 기반의 포인트 컨볼루션 기법에 관한 연구
No full text학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2022.8,[v, 44 p. :]Understanding point cloud has recently gained huge interests following the development of 3D scanning devices and the accumulation of large-scale 3D data. In this research, we study various point cloud processing algorithms and propose a novel approach that provides possibility to overcome the limitations of existing methods. Specifically, we focus on the speed of point cloud processing algorithms and show that effective algorithms run too slowly to perform real-time analysis, while fast algorithms relatively lack in accuracy on the target task. Based on the observations that point-based and voxel-based 3D convolutional methods show better performance while projection-based methods run faster, we present a novel approach, which is a hybrid method of projection- and point-based algorithms, that leverages the advantage of each method. First, we propose a convolutional module, named Projection-based Point Convolution (PPConv), that uses 2D convolutions and multi-layer perceptrons (MLPs) as its components. In PPConv, point features are processed through two branches: point branch, which consists of MLPs, and projection branch that transforms point features into a 2D feature map and then use 2D convolutions to transform these features. We do not use the time-consuming operations which are used in point-based or voxel-based convolutions while constructing PPConvthus it has advantage in fast point cloud processing. When combined with a learnable projection and effective feature fusion strategy, PPConv achieves superior efficiency compared to popular point-based and voxel-based methods, even with a simple backbone architecture based on PointNet++. Next, through deeper study on the architecture design, we construct an efficient point cloud processing framework that can operate on larger-scale point clouds. Throughout the experiments, we demonstrate the efficiency of projection-based point convolutional approach in terms of the trade-off between inference time and segmentation performance. We analyze the efficiency of our approach using point clouds of various scales, including object shapes, indoor scenes, and outdoor LiDAR scenes. Through this research, we intend to provide an efficient approach to point cloud processing based on projection, which can further be combined with various research on improving the performance of 2D convolutional architectures or the running speed of 2D CNNs.한국과학기술원 :전기및전자공학부
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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