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

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Anchor-Free Person Search

Anchor-Free Person Search

引用

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

作者： Yan, Yichao Li, Jinpeng Qin, Jie Bai, Song Liao, Shengcai Liu, Li Zhu, Fan Shao, Ling Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id). Most existing works employ two-stage detectors like Faster-RCNN, yielding encouraging accuracy but with high computational overhead. In this work, we present the Feature-Aligned Person Search Network (AlignPS), the first anchor-free framework to efficiently tackle this challenging task AlignPS explicitly addresses the major challenges, which we summarize as the misalignment issues in different levels (i.e., scale, region, and task), when accommodating an anchor-free detector for this task More specifically, we propose an aligned feature aggregation module to generate more discriminative and robust feature embeddings by following a "re-id first" principle. Such a simple design directly improves the baseline anchor-free model on CUHK-SYSU by more than 20% in mAR Moreover AlignPS outperforms state-of-the-art two-stage methods, with a higher speed. The code is available at https://***/daodaofr/AlignPS.

关键词： computer vision Codes Search methods Detectors Feature extraction pattern recognition Task analysis

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Glancing at the Patch: Anomaly Localization with Global and Local Feature Comparison

Glancing at the Patch: Anomaly Localization with Global and ...

引用

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

作者： Wang, Shenzhi Wu, Liwei Cui, Lei Shen, Yujun SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Anomaly localization, with the purpose to segment the anomalous regions within images, is challenging due to the large variety of anomaly types. Existing methods typically train deep models by treating the entire image as a whole yet put little effort into learning the local distribution, which is vital for this pixel-precise task. In this work, we propose an unsupervised patch-based approach that gives due consideration to both the global and local information. More concretely, we employ a Local-Net and Global-Net to extract features from any individual patch and its surrounding respectively. Global-Net is trained with the purpose to mimic the local feature such that we can easily detect an abnormal patch when its feature mismatches that from the context. We further introduce an Inconsistency Anomaly Detection (IAD) head and a Distortion Anomaly Detection (DAD) head to sufficiently spot the discrepancy between global and local features. A scoring function derived from the multi-head design facilitates high-precision anomaly localization. Extensive experiments on a couple of real-world datasets suggest that our approach outperforms state-of-the-art competitors by a sufficiently large margin.

关键词： Location awareness Image segmentation computer vision Head Feature extraction Distortion pattern recognition

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Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution

Leveraging Multi scale Backbone with Multilevel supervision ...

引用

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

作者： Nathan, Sabari Kansal, Priya Couger Inc Shibuya Ku Tokyo Japan

ISBN: (纸本)9781665448994

This paper proposes an attention-based multi-level model with a multi-scale backbone for thermal image super-resolution. The model leverages the multi-scale backbone as well. The thermal image dataset is provided by PBVS 2020 in their thermal image super-resolution challenge. This dataset contains the images with three different resolution scales(low, medium, high) [1]. However, only the medium and high-resolution images are used to train the proposed architecture to generate the super-resolution images in x2, x4 scales. The proposed architecture is based on the Res2net blocks as the backbone of the network. Along with this, the coordinate convolution layer and dual attention are also used in the architecture. Further, multi-level supervision is implemented to supervise the output image resolution similarity with the real image at each block during training. To test the robustness of the proposed model, we evaluated our model on the Thermal-6 dataset [20]. The results show that our model is efficient to achieve state-of-the-art results on the PBVS dataset. Further the results on the Thermal-6 dataset show that the model has a decent generalization capacity.

关键词： Training Convolution conferences Superresolution computer architecture Robustness pattern recognition

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Composing Photos Like a Photographer

Composing Photos Like a Photographer

引用

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

作者： Hong, Chaoyi Du, Shuaiyuan Xian, Ke Lu, Hao Cao, Zhiguo Zhong, Weicai Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Hubei Peoples R China Huawei CBG Consumer Cloud Serv Prod & Big Data Platform Dept Shenzhen Peoples R China

ISBN: (纸本)9781665445092

We show that explicit modeling of composition rules benefits image cropping. Image cropping is considered a promising way to automate aesthetic composition in professional photography. Existing efforts, however;only model such professional knowledge implicitly, e.g., by ranking from comparative candidates. Inspired by the observation that natural composition traits always follow a specific rule, we propose to learn such rules in a discriminative manner, and more importantly, to incorporate learned composition clues explicitly in the model. To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules. The KCM can reveal the common laws hidden behind different composition rules and can inform the cropping model of what is important in composition. With the KCM, we present a novel cropping-by-composition paradigm and instantiate a network to implement composition-aware image cropping. Extensive experiments on two benchmarks justify that our approach enables effective, interpretable, and fast image cropping.

关键词： Photography computer vision Computational modeling Benchmark testing pattern recognition

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Dynamic Transfer for Multi-Source Domain Adaptation

Dynamic Transfer for Multi-Source Domain Adaptation

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

作者： Li, Yunsheng Yuan, Lu Chen, Yinpeng Wang, Pei Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92093 USA Microsoft Redmond WA USA

ISBN: (纸本)9781665445092

Recent works of multi-source domain adaptation focus on learning a domain-agnostic model, of which the parameters are static. However, such a static model is difficult to handle conflicts across multiple domains, and suffers from a performance degradation in both source domains and target domain. In this paper, we present dynamic transfer to address domain conflicts, where the model parameters are adapted to samples. The key insight is that adapting model across domains is achieved via adapting model across samples. Thus, it breaks down source domain barriers and turns multi-source domains into a single-source domain. This also simplifies the alignment between source and target domains, as it only requires the target domain to be aligned with any part of the union of source domains. Furthermore, we find dynamic transfer can be simply modeled by aggregating residual matrices and a static convolution matrix. Experimental results show that, without using domain labels, our dynamic transfer outperforms the state-of-theart method by more than 3% on the large multi-source domain adaptation datasets - DomainNet.

关键词： Degradation Adaptation models computer vision Codes Convolution pattern recognition

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Complex System pattern recognition Algorithm Based on BP Neural Network 5

Complex System Pattern Recognition Algorithm Based on BP Neu...

引用

5th International conference on computers, Information Processing and Advanced Education, CIPAE 2024

作者： Fan, Xue College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798331527662

This article explores the intricate and complex system pattern recognition algorithm based on backpropagation (BP) neural networks, which are efforts to leverage leverage's capabilities. Complicated systems, as seen in this case, are widely characterized by complex, nonlinear, and dynamic interactions and systems that pose challenges to traditional models and patterns of recognition. To this effect, a BP Neural Network that involves an artificial neural network plays a critical role in learning accuracy and efficiency that is integrated in addressing these intricate deficiencies. BP Neural Networks encompass an extensive and comprehensive dataset that helps discern correlations and underlying patterns in a complex system. The iterative adjustment of weights through the minimization of errors helps in accurately classifying and predicting patterns in complex systems. A typical approach involves training the BP Neural Networks through an extensive dataset, which allows underpinning to underpin the correlations and patterns in a complex system. The experimental results of this article revealed that the BP Neural network algorithms efficiently identified complex patterns, thus providing valuable insights into the model's efficiency in bioinformatics, finance, and engineering. © 2024 ieee.

关键词： Neural network models

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Improving the Efficiency-Accuracy Trade-off of DETR-Style Models in Practice

Improving the Efficiency-Accuracy Trade-off of DETR-Style Mo...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Yumin Suh Dongwan Kim Abhishek Aich Samuel Schulter Jong-Chyi-Su Bohyung Han Manmohan Chandraker NEC Laboratories America Seoul National University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We aim to provide a comprehensive view of the inference efficiency of DETR-style detection models. We explore the effect of basic efficiency techniques and identify the factors that are easy to implement, yet effectively improve the efficiency-accuracy trade-off. Specifically, we investigate the effect of input resolution, multi-scale feature enhancement, and backbone pre-training. Our experiments support that 1) adjusting the input resolution is a simple yet effective way to achieve a better efficiency-accuracy trade-off. 2) Multi-scale feature enhancement can be lightened with a marginal decrease in accuracy, and 3) improved backbone pre-training can further improve the trade-off.

关键词： computer vision Accuracy conferences pattern recognition

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A Novel Approach for Flower Classification using Deep Networks 2

A Novel Approach for Flower Classification using Deep Networ...

引用

2nd International conference on Data Science and Information System, ICDSIS 2024

作者： Jeevan Reddy, A. Natarajan, B. Abinaya, A. Tamilselvi, M. Murali, P. Geetha, K. Chennai India Vit University School Of Computer Science And Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Department Of Electroncis And Communication Engineering Chennai Avadi India Roever Engineering College Department Of Computer Science And Engineering Tamil nadu Perambalur India Saveetha Institute Of Medical And Technical Sciences Saveetha School Of Engineering Department Of Cse Tamilnadu Chennai India Chennai India

ISBN: (纸本)9798350381665

The introduction of transformer models, which utilize a self-attention mechanism within deep neural networks, represents a notable breakthrough in natural language processing. This advancement has spurred researchers to investigate its applicability in computer vision tasks. Transformer-based models have showcased remarkable performance compared to traditional convolutional and recurrent neural networks across a spectrum of visual tasks, highlighting their prowess in representation learning. This study aims to classify vision transformer models according to their task-specific functionalities and conducts an extensive evaluation to discern their advantages and limitations. Moreover, it proposes effective strategies for integrating transformers into real-world device-based applications. Investigating the use of vision transformers, inspired by the transformer model, on diverse benchmark datasets, this research focuses on their ability to identify broad-based visual characteristics and distant dependencies through self-attentional mechanisms. Through meticulous examination on the Oxford Flowers dataset, this research delves into various pre-training methods, model topologies, and hyperparameters to enhance recognition accuracy. The findings reveal that vision transformers not only surpass previous state-of-the-art techniques but also exhibit the capacity to discern subtle differences among flower species with remarkable 98.5% accuracy. Furthermore, this research work explores the interpretability of the vision transformer model, elucidating how it recognizes and integrates crucial visual characteristics during classification. By analyzing the model' s attention maps, insights into its decision-making process are provided. In essence, this research underscores the potential of vision transformers for fine-grained image classification and contributes to the burgeoning field of research on their efficacy in computer vision tasks. © 2024 ieee.

关键词： computer vision

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Brain Controlled Communication system using Machine Learning techniques of computer vision 5

Brain Controlled Communication system using Machine Learning...

引用

5th ieee International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Sabharwal, Munish Sultania, Shantam Jindal, Harsh Galgotias University Greater Noida India Wissen Technology Banglore India Chandigarh University Dept. Of Computer Science And Engineering Mohali India

ISBN: (纸本)9798350330861

Nowadays computers have become a necessity for all computers have made a great leap for us and with the help of that we are able to move to a golden age of Artificial Intelligence. Artificial Intelligence or A.I has huge advantages and potentials for making the lives easy and more accessible. However, we as a society have to keep on innovating and developing new innovations and products to help different aspects of the community. The proposed system uses a brain-controlled interface, eye tracking along with a camera and screen for communication and training. The system requires input in the form of an image captured with the help of a camera. After the image is taken as an input, it will be used to perform eye-tracking and eye-blink detection. Both of these actions will be performed by employing a machine learning algorithm. The algorithm will process a morse code entered by the user, and then will enable the speech functionality using built-in speakers. Thus, rendering the system speak out whatever the user wanted to convey. The proposed system follows a coordinated approach for eye pattern detection and mapping. It detects the gazing spot of the eyes, and calculates the midpoint of the eye gaze. As a result, it predicts the eye blink pattern. This eye pattern recognition system is proposed to help out people with disabilities who find it difficult to communicate. © 2023 ieee.

关键词： Cameras

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MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space

MOS: Towards Scaling Out-of-distribution Detection for Large...

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

作者： Huang, Rui Li, Yixuan Univ Wisconsin Madison Dept Comp Sci Madison WI 53706 USA

ISBN: (纸本)9781665445092

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Existing solutions are mainly driven by small datasets, with low resolution and very few class labels (e.g., CIFAR). As a result, OOD detection for large-scale image classification tasks remains largely unexplored. In this paper, we bridge this critical gap by proposing a group-based OOD detection framework, along with a novel OOD scoring function termed MOS. Our key idea is to decompose the large semantic space into smaller groups with similar concepts, which allows simplifying the decision boundaries between in- vs. out-of-distribution data for effective OOD detection. Our method scales substantially better for high-dimensional class space than previous approaches. We evaluate models trained on ImageNet against four carefully curated OOD datasets, spanning diverse semantics. MOS establishes state-of-the-art performance, reducing the average FPR95 by 14.33% while achieving 6x speedup in inference compared to the previous best method.

关键词： Bridges computer vision Image resolution Semantics Machine learning Benchmark testing pattern recognition

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