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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是1771-1780 订阅

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Leveraging combinatorial testing for safety-critical computer vision datasets

Leveraging combinatorial testing for safety-critical compute...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gladisch, Christoph Heinzemann, Christian Herrmann, Martin Woehrle, Matthias Robert Bosch GmbH Corp Res Gerlingen Germany

ISBN: (纸本)9781728193601

Deep learning-based approaches have gained popularity for environment perception tasks such as semantic segmentation and object detection from images. However, the different nature of a data-driven deep neural nets (DNN) to conventional software is a challenge for practical software verification. In this work, we show how existing methods from software engineering provide benefits for the development of a DNN and in particular for dataset design and analysis. We show how combinatorial testing based on a domain model can be leveraged for generating test sets providing coverage guarantees with respect to important environmental features and their interaction. Additionally, we show how our approach can be used for growing a dataset, i.e. to identify where data is missing and should be collected next. We evaluate our approach on an internal use case and two public datasets.

关键词： Object detection

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Hierarchical Image Classification using Entailment Cone Embeddings

Hierarchical Image Classification using Entailment Cone Embe...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dhall, Ankit Makarova, Anastasia Ganea, Octavian Pavllo, Dario Greeff, Michael Krause, Andreas Swiss Fed Inst Technol Zurich Switzerland MIT Cambridge MA 02139 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Image classification has been studied extensively, but there has been limited work in using unconventional, external guidance other than traditional image-label pairs for training. We present a set of methods for leveraging information about the semantic hierarchy embedded in class labels. We first inject label-hierarchy knowledge into an arbitrary CNN-based classifier and empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance. Taking a step further in this direction, we model more explicitly the label-label and label-image interactions using order-preserving embeddings governed by both Euclidean and hyperbolic geometries, prevalent in natural language, and tailor them to hierarchical image classification and representation learning. We empirically validate all the models on the hierarchical ETHEC dataset.

关键词： Visualization Semantics Task analysis Geometry Computational modeling Apertures computer vision

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SAM: The Sensitivity of Attribution Methods to Hyperparameters

SAM: The Sensitivity of Attribution Methods to Hyperparamete...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bansal, Naman Agarwal, Chirag Anh Nguyen Auburn Univ Auburn AL 36849 USA Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9781728193601

Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! Interestingly, such sensitivity is not reflected in the average explanation accuracy scores over the dataset as commonly reported in the literature. In addition, explanations generated for robust classifiers (i.e. which are trained to be invariant to pixel-wise perturbations) are surprisingly more robust than those generated for regular classifiers.

关键词： computer vision

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Anomaly Detection in Autonomous Driving: A Survey

Anomaly Detection in Autonomous Driving: A Survey

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Daniel Bogdoll Maximilian Nitsche J. Marius Zö llner FZI Research Center for Information Technology Karlsruhe Germany KIT Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.

关键词： computer vision Laser radar Roads conferences Radar detection Benchmark testing Cameras

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Machine Learning Approaches for Quality Control in Pulp Packaging Manufacturers 20

Machine Learning Approaches for Quality Control in Pulp Pack...

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20th International Joint conference on computer Science and Software Engineering, JCSSE 2023

作者： Ceesay, Rattana Boonchoo, Thapana Rattanatamrong, Prapaporn Thammasat University Faculty of Science and Technology Department of Computer Science Pathumthani Thailand

ISBN: (纸本)9798350300505

In molded pulp packaging manufacturing, defect detection and classification processes are critical to ensuring the products meet quality criteria. Yet most manufacturers still rely on human-based manual visual defect classification which can be inconsistent and labor intensive. In this research, we introduce the conjunction of machine vision hardware and machine learning to build a framework for an automated molded pulp packaging defect detection system. The framework consists of two modules. First, the image acquisition module setups appropriate hardware and configuration such t hat high-quality images can be acquired. The second is a machine learning module that constructs a deep learning model with hyper-parameter tuning to automatically detect the defects on the surface of molded pulp products. Our proposed model is based on deep learning model - the Xception architecture, which is recently developed and expected to be more robust on defect detection. In comparison with Traditional machine learning algorithms - SVM and Naive bayes have been widely used in the field of industrial detection. The oriented FAST and rotated BRIEF (ORB) and Bag-of-Visual-Word (BoVW) are implemented for pre-feature extraction. Since molded pulp packaging has obstacles on surface fluctuation by color, grain pulp fiber and non-repeating defect pattern, the Negative Monochrome (NGMC) image preprocessing is proposed to enhance the visibility of defects on the surface and reduce undesired features. The extracted features must be able to describe and distinguish images categories, which could be a limitation for traditional algorithms that required pre-feature extraction. The results demonstrate that the Xception model trained with NGMC images resolution 192x192 and learning rate 0.001 achieved more than 92.98% accuracy and best generalize across datasets from different production lots, which suggests that the robustness of our framework has the potential to be utilized in industrial applica

关键词： Feature extraction

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Effects of Facial Expressions and Parts on Changes in Facial Attractiveness by Brief Presentation 27

Effects of Facial Expressions and Parts on Changes in Facial...

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27th International computer Science and Engineering conference, ICSEC 2023

作者： Kobayashi, Maiko Saegusa, Chihiro Sugiyama, Haruko Tsuchiya, Yoko Kawashima, Misaki Watanabe, Katsumi Waseda University Faculty of Science and Engineering Tokyo Japan Kao Corporation Tokyo Japan

ISBN: (纸本)9798350342109

In the recent development of computer graphics and avatar generation, the synthesis of attractive faces and bodies has gained interest. In cognitive psychology, it is known that the attractiveness of the whole face and facial parts changes with the duration of exposure. Also, facial expressions influence the attractiveness of the whole face and facial parts. In the present study, we investigated how attractiveness judgments for whole faces and parts with facial expressions would change with exposure time. Participants rated the attractiveness of whole faces and facial parts. The faces and face parts expressed either smiling, angry, or neutral expressions. The face stimuli were presented for either 20 ms or 1000 ms. The results showed that the attractiveness ratings of the eyes, mouth, and whole face were higher with the shorter exposure duration. However, the nose became more attractive with the longer exposure. Smiling faces tended to be rated as more attractive and angry faces as less attractive. However, this pattern did not always hold for facial parts. The present results have implications not only for understanding attractiveness judgments in humans, but also for computer graphics and avatar generation in the fields of artificial intelligence and computer vision. © 2023 ieee.

关键词： computer graphics

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It's All Around You: Range-Guided Cylindrical Network for 3D Object Detection 18

It's All Around You: Range-Guided Cylindrical Network for 3D...

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18th ieee/CVF International conference on computer vision (ICCV)

作者： Rapoport-Lavie, Meytal Raviv, Dan Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781665401913

Modern perception systems in the field of autonomous driving rely on 3D data analysis. LiDAR sensors are frequently used to acquire such data due to their increased resilience to different lighting conditions. Although rotating LiDAR scanners produce ring-shaped patterns in space, most networks analyze their data using an orthogonal voxel sampling strategy. This work presents a novel approach for analyzing 3D data produced by 360-degree depth scanners, utilizing a more suitable coordinate system, which is aligned with the scanning pattern. Furthermore, we introduce a novel notion of range-guided convolutions, adapting the receptive field by distance from the ego vehicle and the object's scale. Our network demonstrates powerful results on the competitive nuScenes 3D object detection challenge, comparable to current state-of-the-art architectures.

关键词： computer vision Three-dimensional displays Laser radar Data analysis conferences Lighting Object detection

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Any-Width Networks

Any-Width Networks

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Vu, Thanh Eder, Marc Price, True Frahm, Jan-Michael Univ North Carolina Chapel Hill NC 27515 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Despite remarkable improvements in speed and accuracy, convolutional neural networks (CNNs) still typically operate as monolithic entities at inference time. This poses a challenge for resource-constrained practical applications, where both computational budgets and performance needs can vary with the situation. To address these constraints, we propose the Any-Width Network (AWN), an adjustable-width CNN architecture and associated training routine that allow for fine-grained control over speed and accuracy during inference. Our key innovation is the use of lower-triangular weight matrices which explicitly address width-varying batch statistics while being naturally suited for multi-width operations. We also show that this design facilitates an efficient training routine based on random width sampling. We empirically demonstrate that our proposed AWNs compare favorably to existing methods while providing maximally granular control during inference.

关键词： Training Switches computer architecture Standards Convolution Inference algorithms computer vision

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MedDietAgent: An AI-based Mobile App for Harmonizing Individuals' Dietary Choices with the Mediterranean Diet pattern 46

MedDietAgent: An AI-based Mobile App for Harmonizing Individ...

引用

46th Annual International conference of the ieee Engineering in Medicine and Biology society, EMBC 2024

作者： Konstantakopoulos, Fotios S. Sfakianos, Michail Georga, Eleni I. Mavrokotas, Konstantinos I. Katsarou, Daphne N. Chalatsis, Konstantinos Zapadiotis, Charalambos Panousi, Anastasia Plimakis, Sifis Eleftheriou, Sofia Kanellou, Anastasia Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece Biomedical Research Institute Forth IoanninaGR 45110 Greece AppArt a Software Solutions Company Based in Athens GR 15343 Greece Dbc Diadikasia a Leading Business Consulting Firm in Athens 296 Kifissias Avenue & 40 Navarinou str HalandriGR 15232 Greece University of West Attica Department of Food Science and Technology Egaleo AthensGR 12243 Greece

ISBN: (纸本)9798350371499

Recently, there has been an increasing interest in applying technological advances to offer specific dietary recommendations in the field of nutrition and health. Dietary recommendation systems are advanced tools designed to assist individuals in making well-informed and health-conscious decisions on their food choices, taking into account their personal needs, preferences, and health targets or habits. In this study, we present an AI-based mobile app for harmonizing individuals' dietary choices with the pattern of the Mediterranean diet. A combination of computer vision, natural language processing, machine learning, and reinforcement techniques are used to record the nutritional information via images or speech and to generate dynamic recommendations tailored to the user's performance across key nutritional areas, encompassing calories, combined fats, proteins, carbohydrates, sugars, dietary fibers, sodium intake, fruits, vegetables, and dairy products. The image-based dietary assessment subsystem achieves a mean absolute percentage error of 3.73%, while the reinforcement learning subsystem achieves a 96% average reward. Then, a well-designed approach was taken to develop the MedDietAgent mobile app, using cutting-edge technologies and applying a simplistic approach. One of the key aspects of MedDietAgent is its ability to offer dynamic recommendations by monitoring the user's environment. © 2024 ieee.

关键词： Electronic health record

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RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images

RAPiD: Rotation-Aware People Detection in Overhead Fisheye I...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Duan, Zhihao Tezcan, M. Ozan Nakamura, Hayato Ishwar, Prakash Konrad, Janusz Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781728193601

Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing which radically increases computational complexity. In this work, we develop an end-to-end rotation-aware people detection method, named RAPiD, that detects people using arbitrarily-oriented bounding boxes. Our fully-convolutional neural network directly regresses the angle of each bounding box using a periodic loss function, which accounts for angle periodicities. We have also created a new dataset(1) with spatio-temporal annotations of rotated bounding boxes, for people detection as well as other vision tasks in overhead fisheye videos. We show that our simple, yet effective method outperforms state-of-the-art results on three fisheye-image datasets. The source code for RAPiD is publicly available(2).

关键词： computer vision

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