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

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Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses

Roof-GAN: Learning to Generate Roof Geometry and Relations f...

引用

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

作者： Qian, Yiming Zhang, Hao Furukawa, Yasutaka Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

This paper presents Roof-GAN, a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships. Given the number of primitives, the generator produces a structured roof model as a graph, which consists of 1) primitive geometry as raster images at each node, encoding facet segmentation and angles;2) inter-primitive colinear/coplanar relationships at each edge;and 3) primitive geometry in a vector format at each node, generated by a novel differentiable vectorizer while enforcing the relationships. The discriminator is trained to assess the primitive raster geometry, the primitive relationships, and the primitive vector geometry in a fully end-to-end architecture. Qualitative and quantitative evaluations demonstrate the effectiveness of our approach in generating diverse and realistic roof models over the competing methods with a novel metric proposed in this paper for the task of structured geometry generation.

关键词： Geometry Measurement Image segmentation computer vision Image coding Image edge detection Generative adversarial networks

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Multi-task Learning with Attention for End-to-end Autonomous Driving

Multi-task Learning with Attention for End-to-end Autonomous...

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

作者： Ishihara, Keishi Kanervisto, Anssi Miura, Jun Hautamaki, Ville Toyohashi Univ Technol Toyohashi Aichi Japan Univ Eastern Finland Kuopio Finland

ISBN: (纸本)9781665448994

Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approaches based on end-to-end behavioral cloning have demonstrated remarkable performance in point-to-point navigational scenarios, using a realistic simulator and standard benchmarks. Offline imitation learning is readily available, as it does not require expensive hand annotation or interaction with the target environment, but it is difficult to obtain a reliable system. In addition, existing methods have not specifically addressed the learning of reaction for traffic lights, which are a rare occurrence in the training datasets. Inspired by the previous work on multi-task learning and attention modeling, we propose a novel multi-task attention-aware network in the conditional imitation learning (CIL) framework. This does not only improve the success rate of standard benchmarks, but also the ability to react to traffic lights, which we show with standard benchmarks.

关键词： Training Visualization Benchmark testing pattern recognition Automobiles Reliability Task analysis

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Indian Sign Language recognition using Skin Segmentation and vision Transformer 20

Indian Sign Language Recognition using Skin Segmentation and...

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20th ieee India Council International conference, INDICON 2023

作者： Agarwal, Agrima Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Kumar, Vishal Pune Institute of Computer Technology Department of Electronics & Telecommunication Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350305593

Sign Language is the common mode of communication among the speech and hearing-impaired people, but interpreting this language becomes a challenge for others who don't practise it. To bridge this communication gap, many Artificial Intelligence based models have been designed worldwide. In the Indian context, the field is still relatively new. A 72-word self-created Indian Sign Language dataset has been used in this study. A vision transformer model, consisting of just 2 transformer layers has been proposed. The pre-processing used on the images are YCbCr conversion and morphological operation based skin segmentation. The model achieves a test accuracy of 99.56% and experimentation with different publicly-available datasets confirms its superiority over previous methods. © 2023 ieee.

关键词： Indian Sign Language Sign Language recognition Skin segmentation vision Transformer YCbCr Mapping

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An Alternative Probabilistic Interpretation of the Huber Loss

An Alternative Probabilistic Interpretation of the Huber Los...

引用

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

作者： Meyer, Gregory P. Uber Adv Technol Grp Pittsburgh PA 15201 USA

ISBN: (纸本)9781665445092

The Huber loss is a robust loss function used for a wide range of regression tasks. To utilize the Huber loss, a parameter that controls the transitions from a quadratic function to an absolute value function needs to be selected. We believe the standard probabilistic interpretation that relates the Huber loss to the Huber density fails to provide adequate intuition for identifying the transition point. As a result, a hyper-parameter search is often necessary to determine an appropriate value. In this work, we propose an alternative probabilistic interpretation of the Huber loss, which relates minimizing the loss to minimizing an upper-bound on the Kullback-Leibler divergence between Laplace distributions, where one distribution represents the noise in the ground-truth and the other represents the noise in the prediction. In addition, we show that the parameters of the Laplace distributions are directly related to the transition point of the Huber loss. We demonstrate, through a toy problem, that the optimal transition point of the Huber loss is closely related to the distribution of the noise in the ground-truth data. As a result, our interpretation provides an intuitive way to identify well-suited hyper-parameters by approximating the amount of noise in the data, which we demonstrate through a case study and experimentation on the Faster R-CNN and RetinaNet object detectors.

关键词： computer vision Toy manufacturing industry Detectors Probabilistic logic Search problems pattern recognition Object recognition

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Progressive Unsupervised Learning for Visual Object Tracking

Progressive Unsupervised Learning for Visual Object Tracking

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

作者： Wu, Qiangqiang Wan, Jia Chan, Antoni B. City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking. Specifically, we first learn a background discrimination (BD) model that effectively distinguishes an object from background in a contrastive learning way. We then employ the BD model to progressively mine temporal corresponding patches (i.e., patches connected by a track) in sequential frames. As the BD model is imperfect and thus the mined patch pairs are noisy, we propose a noise-robust loss function to more effectively learn temporal correspondences from this noisy data. We use the proposed noise robust loss to train backbone networks of Siamese trackers. Without online fine-tuning or adaptation, our unsupervised real-time Siamese trackers can outperform state-of-the-art unsupervised deep trackers and achieve competitive results to the supervised baselines.

关键词： Training Visualization Real-time systems Data models Noise robustness pattern recognition Noise measurement

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Distill on the Go: Online knowledge distillation in self-supervised learning

Distill on the Go: Online knowledge distillation in self-sup...

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

作者： Bhat, Prashant Arani, Elahe Zonooz, Bahram NavInfo Europe Adv Res Lab Eindhoven Netherlands

ISBN: (纸本)9781665448994

Self-supervised learning solves pretext prediction tasks that do not require annotations to learn feature representations. For vision tasks, pretext tasks such as predicting rotation, solving jigsaw are solely created from the input data. Yet, predicting this known information helps in learning representations useful for downstream tasks. However, recent works have shown that wider and deeper models benefit more from self-supervised learning than smaller models. To address the issue of self-supervised pre-training of smaller models, we propose Distill-on-the-Go (DoGo), a self-supervised learning paradigm using single-stage online knowledge distillation to improve the representation quality of the smaller models. We employ deep mutual learning strategy in which two models collaboratively learn from each other to improve one another. Specifically, each model is trained using self-supervised learning along with distillation that aligns each model's softmax probabilities of similarity scores with that of the peer model. We conduct extensive experiments on multiple benchmark datasets, learning objectives, and architectures to demonstrate the potential of our proposed method. Our results show significant performance gain in the presence of noisy and limited labels, and in generalization to out-of-distribution data.

关键词： computer vision Annotations conferences computer architecture Performance gain Benchmark testing pattern recognition

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Design of Robot Obstacle recognition Location Ranging Algorithm Based on computer vision and Artificial Intelligence 5

Design of Robot Obstacle Recognition Location Ranging Algori...

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5th International conference on Applied Machine Learning, ICAML 2023

作者： Yang, Libo Guangdong University of Science & Technology Dongguan523083 China

ISBN: (纸本)9798350341416

Obstacle recognition in robot vision is closely related to the distribution and shape of obstacles in terrain environment. How to accurately identify obstacles in terrain environment in real time is the key to whether the robot can successfully pass through complex terrain. The traditional detection and maintenance methods of transmission lines require line workers to work in the field and high-pressure environment for a long time, which is labor-intensive and dangerous. In order to solve the shortcomings of the current positioning methods, this paper puts forward an obstacle positioning method based on computer vision technology, and optimizes the path according to the distance between the obstacle and the target area by using artificial potential energy field method to realize the autonomous movement of the inspection robot. In order to verify the feasibility of obstacle detection and obstacle avoidance scheme, and simulate the working environment and autonomous movement process of inspection robot, the obstacle avoidance behavior of inspection robot was tested. The experimental results show that the improved CNN robot obstacle recognition model is better than the artificial fish swarm algorithm (AFSA) in both accuracy and efficiency. Compared with traditional SVM and PSO, the obstacle recognition error of this method is obviously lower, which can satisfy the fast and accurate navigation of mobile robots. © 2023 ieee.

关键词： computer vision

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Embedded computer vision for Object recognition in Smart Devices for the Blind

Embedded Computer Vision for Object Recognition in Smart Dev...

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2023 International conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Srikanth, N Venkata Sai, Narra Prem Pandurangan, Raji Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9798350313987

This study has proposed a novel method for assisting the visually impaired people by combining computer vision with cutting-edge technological instruments. Convolutional Neural Networks (CNNs) are primarily utilized for performing realtime object recognition. To address the object identification challenge, this study collects and preprocesses a large dataset, construct an efficient CNN architecture, optimize inference speed, and connect this system to smart devices. The results indicate significant gains in precision and throughput for practical applications. This innovation may significantly enhance the quality of life for the visually impaired by increasing their mobility. The research study has far-reaching implications for assistive technology and sheds light on the possibility of making the world more accessible for those with visual impairments. Long-term, intends to enhance the consumer product by enlarging the dataset, refining the model's precision, and investigating additional features. This research is a crucial first step in utilizing technology to provide equal opportunities to individuals with visual impairments. © 2023 ieee.

关键词： computer vision Convolutional Neural Network (CNN) Object recognition Smart Devices Visual Impairment

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SelfDoc: Self-Supervised Document Representation Learning

SelfDoc: Self-Supervised Document Representation Learning

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

作者： Li, Peizhao Gu, Jiuxiang Kuen, Jason Morariu, Vlad, I Zhao, Handong Jain, Rajiv Manjunatha, Varun Liu, Hongfu Brandeis Univ Waltham MA 02254 USA Adobe Res Redmond WA USA

ISBN: (纸本)9781665445092

We propose SelfDoc, a task-agnostic pre-training framework for document image understanding. Because documents are multimodal and are intended for sequential reading, our framework exploits the positional, textual, and visual information of every semantically meaningful component in a document, and it models the contextualization between each block of content. Unlike existing document pre-training models, our model is coarse-grained instead of treating individual words as input, therefore avoiding an overly fine-grained with excessive contextualization. Beyond that, we introduce cross-modal learning in the model pre-training phase to fully leverage multimodal information from unlabeled documents. For downstream usage, we propose a novel modality-adaptive attention mechanism for multimodal feature fusion by adaptively emphasizing language and vision signals. Our framework benefits from self-supervised pre-training on documents without requiring annotations by a feature masking training strategy. It achieves superior performance on multiple downstream tasks with significantly fewer document images used in the pre-training stage compared to previous works.

关键词： Training Visualization computer vision Semantics Layout Linguistics pattern recognition

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Application of Environmental Noise Monitoring System Integrated with Visual recognition Technology 5

Application of Environmental Noise Monitoring System Integra...

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5th ieee Eurasia conference on IOT, Communication and Engineering, ECICE 2023

作者： Tsao, Yung-Chung Shih, Ming-Chang Kuo, Han-Jung Fang, Jen-Kuang Tsai, Yin-Te National University of Kaohsiung Department of Electrical Engineering Kaohsiung Taiwan Ase Group Corporate R & D Center Kaohsiung Taiwan Providence University Department of Computer Science and Communication Engineering Taichung Taiwan

ISBN: (纸本)9798350314694

Environmental noise is an imperceptible problem in daily life and has a significant impact on human health and quality of life. Thus, noise abnormalities need to be monitored. The cause of the noise is directly related to the environment and human activities. Because of the rapid development of micro-processing machines, image-processing equipment has become easily accessible so visual recognition technology with computer vision and machine learning rapidly develops. Facial recognition is one of the most developed technologies and gradually becomes applicable to various situations such as emotional awareness and safety monitoring. Long-term noise anomaly data and invasion data were collected using cloud edge flatbed integration. The abnormal time data and noise anomaly data were used to build an optimal model of feature detection. © 2023 ieee.

关键词： computer vision decibel environmental noise facial recognition machine learning

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