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

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An Extended Moore-Neighbour Tracing Algorithm for Ear Segmentation in a Multi-Feature Ear recognition System

An Extended Moore-Neighbour Tracing Algorithm for Ear Segmen...

引用

2024 International conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Adeniyi, Jide Kehinde Olanrewaju, Ahmed Babajide Adeniyi, Abidemi Emmanuel Brahma, Biswajit Awotunde, Joseph Bamidele Bhuyan, Hemanta Kumar Landmark University Department Of Computer Science Kwara State Omu-Aran Nigeria University Of Ibadan Department Of Computer Science Ibadan Nigeria Bowen University Department Of Computer Science Osun State Iwo Nigeria McKesson Corporation CA United States University Of Ilorin Department Of Computer Science Ilorin Nigeria Vignan's Foundation For SC Technology & Research Department Of It AP India

ISBN: (纸本)9798350371567

Ear recognition is an example of a biometric system that uses human biological traits for recognition. This kind of recognition has been recently examined due to its distinctive properties such as its invariant shape. When performing analysis on image processing or pattern recognition, one of the major problems encountered is the number of features involved. It is necessary to extract a well-defined feature to make the classification process more efficient. Hence, this paper aims to propose an ear recognition system that extracts two features from the human ear: textural and geometrical features. This is aimed at improving the accuracy of the biometric trait. The extracted features were saved as a template and used for matching. The proposed system was evaluated with two online ear image datasets (AMI Ear database and USTB Ear database) and it produced an accuracy of 98.15. © 2024 ieee.

关键词： Biometrics

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Reducing Domain Gap by Reducing Style Bias

Reducing Domain Gap by Reducing Style Bias

引用

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

作者： Nam, Hyeonseob Lee, HyunJae Park, Jongchan Yoon, Wonjun Yoo, Donggeun Lunit Inc Seoul South Korea

ISBN: (纸本)9781665445092

Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic Networks (SagNets) disentangle style encodings from class categories to prevent style biased predictions and focus more on the contents. Extensive experiments show that our method effectively reduces the style bias and makes the model more robust under domain shift. It achieves remarkable performance improvements in a wide range of cross-domain tasks including domain generalization, unsupervised domain adaptation, and semi-supervised domain adaptation on multiple datasets.(1)

关键词： computer vision Adaptation models Shape Decision making Aerospace electronics Robustness Encoding

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BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation

BGT-Net: Bidirectional GRU Transformer Network for Scene Gra...

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

作者： Dhingra, Naina Ritter, Florian Kunz, Andreas Swiss Fed Inst Technol Innovat Ctr Virtual Real Zurich Switzerland

ISBN: (纸本)9781665448994

Scene graphs are nodes and edges consisting of objects and object-object relationships, respectively. Scene graph generation (SGG) aims to identify the objects and their relationships. We propose a bidirectional GRU (BiGRU) transformer network (BGT-Net) for the scene graph generation for images. This model implements novel object-object communication to enhance the object information using a BiGRU layer. Thus, the information of all objects in the image is available for the other objects, which can be leveraged later in the object prediction step. This object information is used in a transformer encoder to predict the object class as well as to create object-specific edge information via the use of another transformer encoder. To handle the dataset bias induced by the long-tailed relationship distribution, softening with a log-softmax function and adding a bias adaptation term to regulate the bias for every relation prediction individually showed to be an effective approach. We conducted an elaborate study on experiments and ablations using open-source datasets, i.e., Visual Genome, Open-Images, and Visual Relationship Detection datasets, demonstrating the effectiveness of the proposed model over state of the art.

关键词： Visualization computer vision Image edge detection conferences Genomics pattern recognition Softening

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Distilling Audio-Visual Knowledge by Compositional Contrastive Learning

Distilling Audio-Visual Knowledge by Compositional Contrasti...

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

作者： Chen, Yanbei Xian, Yongqin Koepke, A. Sophia Shan, Ying Akata, Zeynep Univ Tubingen Tubingen Germany MPI Informat Tubingen Germany Tencent PCG Tubingen Germany MPI Intelligent Syst Tubingen Germany

ISBN: (纸本)9781665445092

Having access to multi-modal cues (e.g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality. In this work, we propose to transfer knowledge across heterogeneous modalities, even though these data modalities may not be semantically correlated. Rather than directly aligning the representations of different modalities, we compose audio, image, and video representations across modalities to uncover richer multi-modal knowledge. Our main idea is to learn a compositional embedding that closes the cross-modal semantic gap and captures the task-relevant semantics, which facilitates pulling together representations across modalities by compositional contrastive learning. We establish a new, comprehensive multi-modal distillation benchmark on three video datasets: UCF101, ActivityNet, and VGGSound. Moreover, we demonstrate that our model significantly outperforms a variety of existing knowledge distillation methods in transferring audio-visual knowledge to improve video representation learning. Code is released https://***/Yanbeic/CCL.

关键词： computer vision Codes Computational modeling Semantics Benchmark testing pattern recognition Task analysis

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Image Reconstruction from Neuromorphic Event Cameras using Laplacian-Prediction and Poisson Integration with Spiking and Artificial Neural Networks

Image Reconstruction from Neuromorphic Event Cameras using L...

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

作者： Duwek, Hadar Cohen Shalumov, Albert Tsur, Elishai Ezra Open Univ Israel Neurobiomorph Engn Lab NBEL Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781665448994

Event cameras are robust neuromorphic visual sensors, which communicate transients in luminance as events. Current paradigm for image reconstruction from event data relies on direct optimization of artificial Convolutional Neural Networks (CNNs). Here we proposed a two-phase neural network, which comprises a CNN, optimized for Laplacian prediction followed by a Spiking Neural Network (SNN) optimized for Poisson integration. By introducing Laplacian prediction into the pipeline, we provide image reconstruction with a network comprising only 200 parameters. We converted the CNN to SNN, providing a full neuromorphic implementation. We further optimized the network with Mish activation and a novel convoluted CNN design, proposing a hybrid of spiking and artificial neural network with < 100 parameters. Models were evaluated on both N-MNIST and N-Caltech101 datasets.

关键词： Visualization Laplace equations Neuromorphics Pipelines Cameras Sensors pattern recognition

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v2e: From Video Frames to Realistic DVS Events

v2e: From Video Frames to Realistic DVS Events

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

作者： Hu, Yuhuang Liu, Shih-Chii Delbruck, Tobi Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

To help meet the increasing need for dynamic vision sensor (DVS) event camera data, this paper proposes the v2e toolbox that generates realistic synthetic DVS events from intensity frames. It also clarifies incorrect claims about DVS motion blur and latency characteristics in recent literature. Unlike other toolboxes, v2e includes pixel-level Gaussian event threshold mismatch, finite intensity-dependent bandwidth, and intensity-dependent noise. Realistic DVS events are useful in training networks for uncontrolled lighting conditions. The use of v2e synthetic events is demonstrated in two experiments. The first experiment is object recognition with N-Caltech 101 dataset. Results show that pretraining on various v2e lighting conditions improves generalization when transferred on real DVS data for a ResNet model. The second experiment shows that for night driving, a car detector trained with v2e events shows an average accuracy improvement of 40% compared to the YOLOv3 trained on intensity frames.

关键词： Training Visualization Lighting vision sensors Tools Cameras pattern recognition

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StruMonoNet: Structure-Aware Monocular 3D Prediction

StruMonoNet: Structure-Aware Monocular 3D Prediction

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

作者： Yang, Zhenpei Li, Li Erran Huang, Qixing Univ Texas Austin Austin TX 78712 USA Columbia Univ New York NY 10027 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Monocular 3D prediction is one of the fundamental problems in 3D vision. Recent deep learning-based approaches have brought us exciting progress on this problem. However, existing approaches have predominantly focused on end-to-end depth and normal predictions, which do not filly utilize the underlying 3D environment's geometric structures. This paper introduces StruMonoNet, which detects and enforces a planar structure to enhance pixel-wise predictions. StruMonoNet innovates in leveraging a hybrid representation that combines visual feature and a surfel representation for plane prediction. This formulation allows us to combine the power of visual feature learning and the flexibility of geometric representations in incorporating geometric relations. As a result, StruMonoNet can detect relations between planes such as adjacent planes, perpendicular planes, and parallel planes, all of which are beneficial for dense 3D prediction. Experimental results show that StruMonoNet considerably outperforms state-of-the-art approaches on NYUv2 and ScanNet.

关键词： Visualization computer vision Three-dimensional displays Boosting pattern recognition

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Adaptive Spatial-Temporal Fusion of Multi-Objective Networks for Compressed Video Perceptual Enhancement

Adaptive Spatial-Temporal Fusion of Multi-Objective Networks...

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

作者： Zheng, He Li, Xin Liu, Fanglong Jiang, Lielin Zhang, Qi Li, Fu Dang, Qingqing He, Dongliang Baidu Inc Dept Comp Vis Technol VIS Bldg 2Baidu Sci Pk Beijing Peoples R China

ISBN: (纸本)9781665448994

Perceptual quality enhancement of heavily compressed videos is a difficult, unsolved problem because there still not exists a suitable perceptual similarity loss function between two video pairs. Motivated by the fact that it is hard to design unified training objectives which are perceptual-friendly for enhancing regions with smooth content and regions with rich textures simultaneously, in this paper, we propose a simple yet effective novel solution dubbed "Adaptive Spatial-Temporal Fusion of Two-Stage Multi-Objective Networks" (ASTF) to adaptive fuse the enhancement results from networks trained with two different optimization objectives. Specifically, the proposed ASTF takes an enhancement frame along with its neighboring frames as input to jointly predict a mask to indicate regions with high-frequency textual details. Then we use the mask to fuse two enhancement results which can retain both smooth content and rich textures. Extensive experiments show that our method achieves a promising performance of compressed video perceptual quality enhancement.

关键词： Training computer vision Adaptation models Adaptive systems Fuses conferences pattern recognition

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EnD: Entangling and Disentangling deep representations for bias correction

EnD: Entangling and Disentangling deep representations for b...

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

作者： Tartaglione, Enzo Barbano, Carlo Alberto Grangetto, Marco Univ Turin Comp Sci Dept Turin Italy

ISBN: (纸本)9781665445092

Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems, like the presence of biases in the training data, which question the generalization capability of these models. In this work we propose EnD, a regularization strategy whose aim is to prevent deep models from learning unwanted biases. In particular, we insert an "information bottleneck" at a certain point of the deep neural network, where we disentangle the information about the bias, still letting the useful information for the training task forward-propagating in the rest of the model. One big advantage of EnD is that it does not require additional training complexity (like decoders or extra layers in the model), since it is a regularizer directly applied on the trained model. Our experiments show that EnD effectively improves the generalization on unbiased test sets, and it can be effectively applied on realcase scenarios, like removing hidden biases in the COVID19 detection from radiographic images.

关键词： Training COVID-19 Radiography Deep learning computer vision Training data Data models

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Novel Design Ideas that Improve Video-Understanding Networks with Transformers

Novel Design Ideas that Improve Video-Understanding Networks...

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International Joint conference on Neural Networks (IJCNN)

作者： Hu, Yaxin Barth, Erhardt Univ Lubeck Inst Neuro & Bioinformat Lubeck Germany Pattern Recognit Co GmbH Lubeck Germany Univ Lubeck Inst Neuro & Bioinformat Lubeck Germany

ISBN: (纸本)9798350359329;9798350359312

With the development of deep learning, video understanding has become a promising and challenging research field. In recent years, different transformer architectures have shown state-of-the-art performance on most benchmarks. Although transformers can process longer temporal sequences and therefor perform better than convolution networks, they require huge datasets and have high computational costs. The inputs to video transformers are usually clips sampled out of a video, and the length of the clips is limited by the available computing resources. In this paper, we introduce novel methods to sample and tokenize the input video, such as to better capture the dynamics of the input without a large increase in computational costs. Moreover, we introduce the MinBlocks as a novel architecture inspired by neural processing in biological vision. The combination of variable tubes and MinBlocks improves network performance by 10.67%.

关键词： Video Transformer Video Understanding Action recognition Variable Tubes MinBlocks UniFormerV2

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