Computer Science Research Notes最新文献_第3页

Self-Checkout Product Class Verification using Center Loss approach 使用中心损失方法的自助结帐产品类别验证

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.4

Bernardas Ciapas, P. Treigys

{"title":"Self-Checkout Product Class Verification using Center Loss approach","authors":"Bernardas Ciapas, P. Treigys","doi":"10.24132/csrn.3301.4","DOIUrl":"https://doi.org/10.24132/csrn.3301.4","url":null,"abstract":"The traditional image classifiers are not capable to verify if samples belong to specified classes due to several reasons: classifiers do not provide boundaries between in-class and out-of-class samples; although classifiers provide separation boundaries between known classes, classifiers\" latent features tend to have high intra-class variance; classifiers often predict high probabilities for out-of-distribution samples; training classifiers on unbalanced data results in bias towards over-represented classes. The nature of the class verification problem requires a different loss function than the ubiquitous cross entropy loss in traditional classifiers: input to a class verification function includes a suggested class in addition to an image. As opposed to outlier detection, space is transformed to be not only separable, but discriminative between in-class and out-of-class inputs. In this paper, class verification based on a euclidean distance from the class centre is proposed and implemented. Class centres are learnt by training on a centre loss function. The method\"s effectiveness is shown on a self-checkout image dataset of 194 food retail products. The results show that a two-fold loss function is not only useful to verify class, but does not degrade classification performance - thus, the same neural network is usable both for classification and verification.","PeriodicalId":322214,"journal":{"name":"Computer Science Research Notes","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121334818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Justice Expectations Related to the Use of CNNs to Identify CSAM. Technological Interview 与使用cnn识别CSAM相关的司法期望。技术面试

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.39

W. Oronowicz-Jaśkowiak, Piotr Wasilewski, Mirosław Kowaluk

引用次数: 0

Coordinate-Unet 3D for segmentation of lung parenchyma 坐标- unet 3D肺实质分割

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.6

V. Le, Olivier Saut

引用次数: 1

Using the Adaptive HistoPyramid to Enhance Performance of Surface Extraction in 3D Medical Image Visualisation 利用自适应组织金字塔增强三维医学图像可视化表面提取的性能

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.57

Antony Padinjarathala, R. Sadleir

引用次数: 0

SAIL: Semantic Analysis of Information in Light Fields: Results from Synthetic and Real-World Data 光场信息的语义分析:来自合成数据和真实世界数据的结果

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.12

Robin Kremer, Thorsten Herfet

引用次数: 0

Evolutionary-Edge Bundling with Concatenation Process of Control Points 具有控制点串联过程的进化边缘捆绑

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.33

R. Saga, Jae-Chul Baek

引用次数: 0

A New Deterministic Gasket Fractal Based on Ball Sets 一种新的基于球集的确定性垫片分形

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.34

Roberto Soto-Villalobos, F. Benavides-Bravo, F. Hueyotl-Zahuantitla, Mario A. Aguirre-López

引用次数: 1

Position Based Rigid Body Simulation: A comparison of physics simulators for games 基于位置的刚体模拟:游戏物理模拟器的比较

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.59

Miguel Seabra, Francisco Fernandes, Daniel Simões Lopes, João Madeiras Pereira

引用次数: 0

Monte Carlo Based Real-Time Shape Analysis in Volumes 基于蒙特卡罗的实时形状分析

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.15

K. Gurijala, Lei Wang, A. Kaufman

引用次数: 0

Designing a Lightweight Edge-Guided Convolutional Neural Network for Segmenting Mirrors and Reflective Surfaces 设计一种轻量级边缘引导卷积神经网络分割镜面和反射面

Computer Science Research Notes Pub Date : 2023-07-01 DOI: 10.24132/csrn.3301.14

Mark Edward M. Gonzales, Lorene C. Uy, J. Ilao

{"title":"Designing a Lightweight Edge-Guided Convolutional Neural Network for Segmenting Mirrors and Reflective Surfaces","authors":"Mark Edward M. Gonzales, Lorene C. Uy, J. Ilao","doi":"10.24132/csrn.3301.14","DOIUrl":"https://doi.org/10.24132/csrn.3301.14","url":null,"abstract":"The detection of mirrors is a challenging task due to their lack of a distinctive appearance and the visual similarity of reflections with their surroundings. While existing systems have achieved some success in mirror segmentation, the design of lightweight models remains unexplored, and datasets are mostly limited to clear mirrors in indoor scenes. In this paper, we propose a new dataset consisting of 454 images of outdoor mirrors and reflective surfaces. We also present a lightweight edge-guided convolutional neural network based on PMDNet. Our model uses EfficientNetV2-Medium as its backbone and employs parallel convolutional layers and a lightweight convolutional block attention module to capture both low-level and high-level features for edge extraction. It registered maximum F-measure scores of 0.8483, 0.8117, and 0.8388 on the Mirror Segmentation Dataset (MSD), Progressive Mirror Detection (PMD) dataset, and our proposed dataset, respectively. Applying filter pruning via geometric median resulted in maximum F-measure scores of 0.8498, 0.7902, and 0.8456, respectively, performing competitively with the state-of-the-art PMDNet but with 78.20x fewer floating-point operations per second and 238.16x fewer parameters. The code and dataset are available at https://github.com/memgonzales/mirror-segmentation.","PeriodicalId":322214,"journal":{"name":"Computer Science Research Notes","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121126822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0