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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

共 6858 条记录，以下是341-350 订阅

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NL-FFC: Non-Local Fast Fourier Convolution for Image Super Resolution

NL-FFC: Non-Local Fast Fourier Convolution for Image Super R...

引用

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

作者： Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Space Applicat Ctr Signal & Image Proc Grp Ahmadabad India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep neural networks have shown promising results in image super-resolution by learning a complex mapping from low resolution to high resolution image. However, most of the approaches learns to upsample by using convolution in spatial domain and are confined to local features. This results into restricting the receptive field of the network and therefore deteriorates the overall quality of the high-resolution image. To alleviate this issue, we propose an architecture that learns both local and global features, and fuses them together to generate high quality images. The network uses a non-local attention aided Fast Fourier Convolutions (NL-FFC) to widen the receptive field and learn long-range dependencies. The analyses further show that these Fourier features implicitly provide faster convergence on low frequency components only to learn prior for unobserved high frequency components. The model generalizes well to different datasets. We further investigate the role of non-local attention, and the ratio of local and global features to maximize the performance gain in the ablation study.

关键词： Deep learning Convolution Fuses Superresolution Neural networks Performance gain pattern recognition

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On Improving Cross-dataset Generalization of Deepfake Detectors

On Improving Cross-dataset Generalization of Deepfake Detect...

引用

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

作者： Nadimpalli, Aakash Varma Rattani, Ajita Wichita State Univ Sch Comp Wichita KS 67260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial manipulation by deep fake has caused major security risks and raised severe societal concerns. As a countermeasure, a number of deep fake detection methods have been proposed recently. Most of them model deep fake detection as a binary classification problem using a backbone convolutional neural network (CNN) architecture pretrained for the task. These CNN-based methods have demonstrated very high efficacy in deep fake detection with the Area under the Curve (AUC) as high as 0.99. However, the performance of these methods degrades significantly when evaluated across datasets. In this paper, we formulate deep fake detection as a hybrid combination of supervised and reinforcement learning (RL) to improve its cross-dataset generalization performance. The proposed method chooses the top-k augmentations for each test sample by an RL agent in an image-specific manner. The classification scores, obtained using CNN, of all the augmentations of each test image are averaged together for final real or fake classification. Through extensive experimental validation, we demonstrate the superiority of our method over existing published research in cross-dataset generalization of deep fake detectors, thus obtaining state-of-the-art performance.

关键词： Deepfakes computer vision conferences Detectors Reinforcement learning computer architecture pattern recognition

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AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data

AuxMix: Semi-Supervised Learning with Unconstrained Unlabele...

引用

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

作者： Banitalebi-Dehkordi, Amin Gujjar, Pratik Zhang, Yong Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised learning (SSL) has seen great strides when labeled data is scarce but unlabeled data is abundant. Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data. In this work, we show that state-of-the-art SSL algorithms suffer a degradation in performance in the presence of unlabeled auxiliary data that does not necessarily possess the same class distribution as the labeled set. We term this problem as Auxiliary-SSL and propose AuxMix, an algorithm that leverages self-supervised learning tasks to learn generic features in order to mask auxiliary data that are not semantically similar to the labeled set. We also propose to regularize learning by maximizing the predicted entropy for dissimilar auxiliary samples. We show an improvement of 5% over existing baselines on a ResNet-50 model when trained on CIFAR10 dataset with 4k labeled samples and all unlabeled data is drawn from the Tiny-Imagenet dataset. We report competitive results on several datasets and conduct ablation studies.

关键词： Degradation computer vision conferences Self-supervised learning Semisupervised learning Prediction algorithms Entropy

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Rethinking Adversarial Examples in Wargames

Rethinking Adversarial Examples in Wargames

引用

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

作者： Chen, Yuwei Chinese Aeronaut Estab Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Artificial intelligence technology is increasingly widely used in games, especially for wargames. The addition of artificial intelligence algorithms enables these games to solve decision-making problems in complex environments more quickly, surpassing the vast majority of experienced human players in competitive games. However, due to the vulnerability of the neural network before adversarial examples, all modules using artificial intelligence algorithms are at risk of being attacked. For wargames, adversarial examples will make the units in the game no longer able to follow the established routes or actions to perform tasks. Based on such risks, this paper proposes a deceptive concept scheme of attacking intelligent modules in wargames through adversarial examples, and proposes challenges and prospects for current technologies. To our knowledge, we are the first team to analyze the impact of adversarial examples in the running process of wargames, namely the OODA loop, and simulate them in the corresponding wargaming software. In the end, we found that when artificial intelligence technology is widely used in war games, adversarial examples will have a subversive impact on several activities in several steps, which will directly lead to the failure to complete the established game goals.

关键词： Neural networks Decision making Games Prediction algorithms Software pattern recognition Security

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Poisons that are learned faster are more effective

Poisons that are learned faster are more effective

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

作者： Sandoval-Segura, Pedro Singla, Vasu Fowl, Liam Geiping, Jonas Goldblum, Micah Jacobs, David Goldstein, Tom Univ Maryland College Pk MD 20742 USA New York Univ New York NY USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Imperceptible poisoning attacks on entire datasets have recently been touted as methods for protecting data privacy. However, among a number of defenses preventing the practical use of these techniques, early-stopping stands out as a simple, yet effective defense. To gauge poisons' vulnerability to early-stopping, we benchmark error-minimizing, error-maximizing, and synthetic poisons in terms of peak test accuracy over 100 epochs and make a number of surprising observations. First, we find that poisons that reach a low training loss faster have lower peak test accuracy. Second, we find that a current state-of-the-art error-maximizing poison is 7x less effective when poison training is stopped at epoch 8. Third, we find that stronger, more transferable adversarial attacks do not make stronger poisons. We advocate for evaluating poisons in terms of peak test accuracy.

关键词： Training Data privacy Privacy computer vision Toxicology Correlation Perturbation methods

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Sea Situational Awareness (SeaSAw) Dataset

Sea Situational Awareness (SeaSAw) Dataset

引用

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

作者： Kaur, Parneet Aziz, Arslan Jain, Darshan Patel, Harshil Hirokawa, Jonathan Townsend, Lachlan Reimers, Christoph Hua, Fiona

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Vessels move 90% of international cargo by volume, with the marine economy contributing to 5.1% of global GDP. As one of the oldest industries, the marine industry has yet to embrace innovations in modern technology to safeguard the blue economy. Situational awareness from intelligent vessel systems can enable enhanced safety and decision-making for mariners. As the foundation for these intelligent systems, advanced perception technology requires sufficient real-world operational data to leverage recent AI technologies. In this work, we introduce the Sea Situational Awareness (SeaSAw) dataset - a novel dataset that is comprised of 1.9 million images with 14.6 million objects associated with 20.4 million attributes from 12 object classes, making it the largest maritime dataset for object detection, fine-grained classification and tracking. Furthermore, this dataset consists of 9 sources in combination with various RGB cameras, mounted on different moving vessels, operating in different geographic locations globally, having variations in scenario, weather and illumination conditions. This data collection took place across 4 years with rigorous efforts on data selection, annotation, management and analysis to enhance the marine perception technology.

关键词： Industries computer vision Technological innovation Image resolution Lighting Object detection Safety

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On-Sensor Binarized Fully Convolutional Neural Network for Localisation and Coarse Segmentation

On-Sensor Binarized Fully Convolutional Neural Network for L...

引用

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

作者： Liu, Yanan Lu, Yao Shanghai Univ Sch Microelect Shanghai Peoples R China Univ Bristol Visual Informat Lab Bristol Avon England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Current neural networks are compatible with high-performance GPU/CPUs. However, implementing neural networks on emerging embedded sensor for inference is challenging due to sensor's unique hardware architecture and stringent computing resources. With this in mind, this work presents new methods to implement fully convolutional neural networks (FCNs) on Pixel Processor Array (PPA) sensors with many techniques to fully use the limited resources on sensor. Specifically, we, for the first time, design and train binarized FCN for both binary weights and activations using batchnorm, group convolution, and learnable threshold for binarization, producing networks small enough to be embedded on the focal plane of the PPA, with limited local memory resources, and using parallel elementary add/subtract, shifting, and bit operations only. We demonstrate the first implementation of an FCN on a PPA device, performing three convolution layers entirely in the pixel-level processors. We use this architecture to demonstrate inference generating heat maps for object segmentation and localisation at over 280 FPS using the SCAMP-5 PPA vision chip.

关键词： Performance evaluation Heating systems Convolution Neural networks computer architecture Object segmentation Parallel processing

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Asymmetric Information Distillation Network for Lightweight Super Resolution

Asymmetric Information Distillation Network for Lightweight ...

引用

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

作者： Zong, Zhikai Zha, Lin Jiang, Jiande Liu, Xiaoxiao Qingdao Hiimage Technol Co Ltd Hisense Visual Technol Co Ltd Qingdao Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The purpose of this paper is to design a lightweight network to achieve image super resolution performance equivalent to SRResNet. We design an asymmetric information distillation block (AIDB) with distillation information multiplexing and asymmetric information extraction capabilities to better achieve this goal. Distillation information multiplexing refers to the repeated processing of distilled information to supplement the ability of key information extraction. Asymmetric information enhancement block (AIEB) refers to identify different features in the image by the horizontal and vertical feature extraction. AIEB greatly reduces the number of parameters, and distillation information multiplexing works as a supplement to the lost high dimensional information. A large number of experiments show that our asymmetric information distillation network (AIDN) achieves a better balance of performance and complexity than SOTA model. Moreover, Our proposed AIDN ranked second in the model complexity track of NTIRE2022 efficient super resolution challenge. Compared with the first place in this track, we achieves higher PSNR performance on testset with a slight disadvantage in the number of parameters. The code is available at https://***/zzksdu/AIDN.

关键词： Multiplexing Measurement computer vision Image resolution Computational modeling conferences Feature extraction

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Spacing Loss for Discovering Novel Categories

Spacing Loss for Discovering Novel Categories

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

作者： Joseph, K. J. Paul, Sujoy Aggarwal, Gaurav Biswas, Soma Rai, Piyush Han, Kai Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Google Res Bengaluru India Indian Inst Sci Bengaluru India Lndian Inst Technol Kanpur Kanpur Uttar Pradesh India Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into single-stage and two-stage methods based on whether they require access to labeled and unlabeled data together while discovering new classes. Next, we devise a simple yet powerful loss function that enforces separability in the latent space using cues from multi-dimensional scaling, which we refer to as Spacing Loss. Our proposed formulation can either operate as a standalone method or can be plugged into existing methods to enhance them. We validate the efficacy of Spacing Loss with thorough experimental evaluation across multiple settings on CIFAR-10 and CIFAR-100 datasets.

关键词： Training computer vision conferences Computational modeling Machine learning Benchmark testing Data models

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Semi-Supervised Training to Improve Player and Ball Detection in Soccer

Semi-Supervised Training to Improve Player and Ball Detectio...

引用

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

作者： Vandeghen, Renaud Cioppa, Anthony Van Droogenbroeck, Marc Univ Liege Liege Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Accurate player and ball detection has become increasingly important in recent years for sport analytics. As most state-of-the-art methods rely on training deep learning networks in a supervised fashion, they require huge amounts of annotated data, which are rarely available. In this paper, we present a novel generic semi-supervised method to train a network based on a labeled image dataset by leveraging a large unlabeled dataset of soccer broadcast videos. More precisely, we design a teacher-student approach in which the teacher produces surrogate annotations on the unlabeled data to be used later for training a student which has the same architecture as the teacher. Furthermore, we introduce three training loss parametrizations that allow the student to doubt the predictions of the teacher during training depending on the proposal confidence score. We show that including unlabeled data in the training process allows to substantially improve the performances of the detection network trained only on the labeled data. Finally, we provide a thorough performance study including different proportions of labeled and unlabeled data, and establish the first benchmark on the new SoccerNet-v3 detection task, with an mAP of 52.3%.

关键词： Training Adaptation models Annotations Benchmark testing Data models pattern recognition Proposals

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