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

共 12859 条记录，以下是31-40 订阅

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FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection

FoundPAD: Foundation Models Reloaded for Face Presentation A...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Guray Ozgur Eduarda Caldeira Tahar Chettaoui Fadi Boutros Raghavendra Ramachandra Naser Damer Fraunhofer IGD Germany Norwegian University of Science and Technology (NTNU) Norway TU Darmstadt Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Although face recognition systems have seen a massive performance enhancement in recent years, they are still targeted by threats such as presentation attacks, leading to the need for generalizable presentation attack detection (PAD) algorithms. Current PAD solutions suffer from two main problems: low generalization to unknown scenarios and large training data requirements. Foundation models (FM) are pretrained on extensive datasets, achieving remarkable results when generalizing to unseen domains and allowing for efficient task-specific adaption even in low data availability settings. This is one of the first works to recognize the potential of FMs and adapt them for the downstream task of PAD. The FM under consideration is adapted with LoRA weights while simultaneously training a classification header. The resultant architecture, FoundPAD, is highly generalizable to unseen domains, achieving competitive results in several settings under different data availability scenarios and even when using synthetic training data. To encourage reproducibility and facilitate further research in PAD, we publicly release the implementation of FoundPAD at https://***/gurayozgur/FoundPAD.

关键词： Training computer vision Frequency modulation Foundation models Face recognition conferences Training data Transformers Reproducibility of results Iron

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MADation: Face Morphing Attack Detection with Foundation Models

MADation: Face Morphing Attack Detection with Foundation Mod...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Eduarda Caldeira Guray Ozgur Tahar Chettaoui Marija Ivanovska Peter Peer Fadi Boutros Vitomir Struc Naser Damer Fraunhofer IGD Germany University of Ljubljana Slovenia TU Darmstadt Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to create efficient ways to attack them, posing a threat to their secure deployment. Morphing attack detection (MAD) systems aim to detect a specific type of threat, morphing attacks, at an early stage, preventing them from being considered for verification in critical processes. Foundation models (FM) learn from extensive amounts of unlabelled data, achieving remarkable zero-shot generalization to unseen domains. Although this generalization capacity might be weak when dealing with domain-specific downstream tasks such as MAD, FMs can easily adapt to these settings while retaining the built-in knowledge acquired during pretraining. In this work, we recognize the potential of FMs to perform well in the MAD task when properly adapted to its specificities. To this end, we adapt FM CLIP architectures with LoRA weights while simultaneously training a classification header. The proposed framework, MADation surpasses our alternative FM and transformer-based frameworks and constitutes the first adaption of FMs to the MAD task. MADation presents competitive results with current MAD solutions in the literature and even surpasses them in several evaluation scenarios. To encourage reproducibility and facilitate further research in MAD, we publicly release the implementation of MADation at https://***/gurayozgur/MADation.

关键词： Training computer vision Frequency modulation Foundation models Face recognition conferences Training data Benchmark testing Transformers Reproducibility of results

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Evaluating Multimodal vision-Language Model Prompting Strategies for Visual Question Answering in Road Scene Understanding

Evaluating Multimodal Vision-Language Model Prompting Strate...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Aryan Keskar Srinivasa Perisetla Ross Greer University of California Merced

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Understanding complex traffic scenes is a crucial challenge in advancing autonomous driving systems. Visual Question Answering (VQA) tasks have emerged as a promising approach to extracting actionable insights from multimodal traffic data, enabling vehicles to make accurate, real-time decisions. The MAPLM-QA dataset, introduced as part of the 2025 WACV Large Language vision Models Challenge for Autonomous Driving (LLVM-AD), offers a robust benchmark for this task, comprising 14,000 multi-modal frames combining high-resolution panoramic images and rendered Bird's Eye View (BEV) depictions of LiDAR 3D point clouds. In this work, we explore the application of NVIDIA's vision-Language Model (ViLA) to address VQAs in MAPLM-QA. By employing detailed prompt engineering tailored to the dataset, we systematically evaluate ViLA's performance, identifying strengths in certain metrics such as quality assessment while highlighting challenges in lane counting, intersection recognition, and nuanced scene understanding. Our findings illustrate the potential of vision-Language Models (VLMs) in enhancing traffic scene analysis and autonomous driving, establishing a strong foundation and analysis of limitations for future research in leveraging VLMs and multimodal datasets toward scalable, robust traffic scene understanding.

关键词： Point cloud compression Visualization Solid modeling Three-dimensional displays Roads Question answering (information retrieval) Real-time systems Quality assessment Prompt engineering Autonomous vehicles

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Pre-capture Privacy via Adaptive Single-Pixel Imaging

Pre-capture Privacy via Adaptive Single-Pixel Imaging

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Yoko Sogabe Shiori Sugimoto Ayumi Matsumoto Masaki Kitahara NTT Corporation Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As cameras become ubiquitous in our living environment, invasion of privacy is becoming a significant concern. A common approach to privacy preservation is to remove personally identifiable information from a captured image, but there is a risk of the original image being leaked. In this paper, we propose a pre-capture privacy-aware imaging method that captures images from which the details of a pre-specified anonymized target have been eliminated. The proposed method applies a single-pixel imaging frame-work in which we introduce a feedback mechanism called an aperture pattern generator (APG). The introduced APG adaptively outputs the next aperture pattern to avoid sampling the anonymized target by using already acquired data as a clue. Furthermore, the anonymized target can be set to any object without changing hardware. Except for the removed detailed features of the anonymized target, the captured images are of comparable quality to those captured by a general camera and can be used for various computer vision applications. We target faces and license plates and experimentally show that the proposed method can capture clear images in which detailed features of the anonymized target are eliminated, achieving both privacy and utility.

关键词： Privacy computer vision Apertures Cameras Hardware Generators Sensors License plate recognition Faces Identification of persons

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Proceedings - 2020 ieee/cvf conference on computer vision and pattern recognition, CVPR 2020

Proceedings - 2020 IEEE/CVF Conference on Computer Vision an...

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2020 ieee/cvf conference on computer vision and pattern recognition, CVPR 2020

The proceedings contain 2 papers. The topics discussed include: attention mechanism exploits temporal contexts: real-time 3D human pose reconstruction;and cascaded deep monocular 3D human pose estimation with evolutio...

ISBN: (纸本)9781728171685

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Modality-Specific Strategies for Medical Image Segmentation Using Lightweight SAM Architectures

Modality-Specific Strategies for Medical Image Segmentation...

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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/cvf conference on computer vision and pattern recognition, CVPR 2024

作者： Dao, Thuy Ye, Xincheng Scarsbrook, Joshua Balarupan, Gowrienanthan Ribeiro, Fernanda L. Bollmann, Steffen School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia Queensland Digital Health Centre University of Queensland Brisbane Australia

ISBN: (纸本)9783031818530

Medical image segmentation tasks are often intricate and require medical domain expertise. Recent advancements in deep learning have expedited these demanding tasks, transitioning from specialized models tailored to each task to versatile foundation models capable of accommodating various image modalities. However, many of these foundation models are optimized for GPU computation, necessitating significant computational resources and constraining their practical utility in clinical settings. Furthermore, their variable accuracy across modalities and novel domains undermines their reliability in clinical practice. To address these limitations, we undertake a comparative investigation into deploying medical image segmentation models on CPU, focusing on accuracy and runtime efficiency, as part of the "CVPR 2024: Segment Anything In Medical Images On Laptop" challenge. Our methodology employs different models customized for each modality, including pre-trained EfficientViT-SAM and LiteMedSAM to yield the most precise and efficient outcomes. Additionally, to bolster model performance for datasets featuring small regions of interest, such as PET scans, we integrate a majority voting mechanism. We optimize runtime using the OpenVINO format within a C++ inference script. This approach improves inference runtime while maintaining competitive accuracy, achieving an average DSC score of 0.86 on the validation set and 0.75 on the testing set with an average runtime of 4.61 s on the testing set. Notably, given that most modalities are evaluated in a zero-shot manner, our findings suggest that the zero-shot capability of foundation models can be further refined through dataset-specific inference strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： C++ (programming language)

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Enriching Local patterns with Multi-Token Attention for Broad-Sight Neural Networks

Enriching Local Patterns with Multi-Token Attention for Broa...

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ieee Workshop on Applications of computer vision (WACV)

作者： Hankyul Kang Jongbin Ryu Ajou University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In neural networks, recognizing visual patterns is challenging because global average pooling disregards local patterns and solely relies on over-concentrated activation. Global average pooling enforces the network to learn objects regardless of their location, so features tend to be activated only in specific regions. To support this claim, we provide a novel analysis of the problems that over-concentration brings about in networks with extensive experiments. We analyze the over-concentration through problems arising from feature variance and dead neurons that are not activated. Based on our analysis, we introduce a multi-token attention pooling layer to alleviate the over-concentration problem. Our attention-pooling layer captures broad-sight local patterns by learning multiple tokens with the proposed distillation algorithm. It resolves the high bias and high variance errors of learned multi-tokens, which is crucial when aggregating local patterns with multi-tokens. Our method applies to various vision tasks and network architectures such as CNN, ViT, and MLP-Mixer. The proposed method improves baselines with few extra resources, and a network employing our pooling method works favorably against state-of-the-art networks. We open-source the code at https://***/Lab-LVM/imagenet-models.

关键词： Visualization computer vision Codes Neurons Object detection Network architecture pattern recognition Object recognition Biological neural networks

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Proceedings - 2024 ieee/cvf conference on computer vision and pattern recognition, CVPR 2024

Proceedings - 2024 IEEE/CVF Conference on Computer Vision an...

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2024 ieee/cvf conference on computer vision and pattern recognition, CVPR 2024

ISBN: (纸本)9798350353006

The proceedings contain 2715 papers. The topics discussed include: revisiting adversarial training at scale;SPIDeRS: structured polarization for invisible depth and reflectance sensing;MA-LMM: memory-augmented large multimodal model for long-term video understanding;geometrically-driven aggregation for zero-shot 3D point cloud understanding;TextCraftor: your text encoder can be image quality controller;ViLa-MIL: dual-scale vision-language multiple instance learning for whole slide image classification;HumanNorm: learning normal diffusion model for high-quality and realistic 3D human generation;AnEmpirical study of scaling law for scene text recognition;improving image restoration through removing degradations in textual representations;and steganographic passport: an owner and user verifiable credential for deep model ip protection without retraining.

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2008 ieee computer Society conference on computer vision and pattern recognition workshops, CVPR workshops

2008 IEEE Computer Society Conference on Computer Vision and...

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2008 ieee computer Society conference on computer vision and pattern recognition workshops, CVPR workshops

ISBN: (纸本)9781424423408

The proceedings contain 235 papers. The topics discussed include: creating and exploring a large photorealistic virtual space;can similar scenes help surface layout estimation?;meta-tag propagation by co-training an ensemble classifier for improving image search relevance;utility data annotation with Amazon mechanical Turk;SAVE: a framework for semantic annotation of visual events;a walk through the web's video clips;autotagging Facebook: social network context improves photo annotation;effective image database search via dimensionality reduction;scalable classifiers for Internet vision tasks;computing iconic summaries of general visual concepts;Internet video category recognition;super-resolution texturing for online virtual globes;orientation distribution estimation for q-ball imaging;variational registration of tensor-valued images;and a methodology for quality assessment in tensor images.

关键词： Tensors

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Proceedings - 2021 ieee/cvf conference on computer vision and pattern recognition, CVPR 2021

Proceedings - 2021 IEEE/CVF Conference on Computer Vision an...

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2021 ieee/cvf conference on computer vision and pattern recognition, CVPR 2021

ISBN: (纸本)9781665445092

The proceedings contain 1658 papers. The topics discussed include: single-stage instance shadow detection with bidirectional relation learning;learning Delaunay surface elements for mesh reconstruction;fusing the old with the new: learning relative camera pose with geometry-guided uncertainty;uncertainty guided collaborative training for weakly supervised temporal action detection;privacy-preserving collaborative learning with automatic transformation search;rethinking and improving the robustness of image style transfer;style-aware normalized loss for improving arbitrary style transfer;faster meta update strategy for noise-robust deep learning;a hyperbolic-to-hyperbolic graph convolutional network;training networks in null space of feature covariance for continual learning;and exponential moving average normalization for self-supervised and semi-supervised learning.

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