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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11-20 订阅

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FX-MAD: Frequency-Domain Explainability and Explainability-Driven Unsupervised Detection of Face Morphing Attacks

FX-MAD: Frequency-Domain Explainability and Explainability-D...

引用

2025 ieee/cvf Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Huber, Marco Neto, Pedro C. Sequeira, Ana F. Damer, Naser Fraunhofer Institute for Computer Graphics Research Igd Darmstadt Germany Technische Universität Darmstadt Department of Computer Science Germany Inesc Tec Porto Portugal University of Porto Faculry of Engineering Portugal

ISBN: (纸本)9798331536626

Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match both identities, allowing serious security breaches. In this work, we apply a frequency-based explanation method from the area of explainable face recognition to shine a light on how FR models behave when processing a bona fide or attack pair from a frequency perspective. In extensive experiments, we used two different state-of-the-art FR models and six different morphing attacks to investigate possible differences in behavior. Our results show that FR models rely differently on different frequency bands when making decisions for bona fide pairs and morphing attacks. In the following step, we show that this behavioral difference can be used to detect morphing attacks in an unsupervised setup solely based on the observed frequency-importance differences in a generalizable manner. © 2025 ieee.

关键词： Frequency domain analysis

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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

2025 ieee/cvf Winter conference on Applications of computer vision workshops, WACVW 2025

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

ISBN: (纸本)9798331536626

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. © 2025 ieee.

关键词： Damage detection

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Interactive Rotated Object Detection for Novel Class Detection in Remotely Sensed Imagery

Interactive Rotated Object Detection for Novel Class Detecti...

引用

2025 ieee/cvf Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Burges, Marvin Dias, Philipe Ambrozio Woody, Carson Walters, Sarah Lunga, Dalton Tu Wien Vienna Vienna Austria Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798331536626

In this paper, we propose IRTR-DETR, an Interactive and Real-Time Rotated DEtection TRansformer that extends IRT-DETR to predict rotated bounding boxes. IRTR-DETR maintains the Human-In-The-Loop (HIL) workflow of IRTDETR but introduces rotation-aware heads for improved detection of objects with arbitrary orientations. Similarly to IRTDETR, IRTR-DETR can be trained with a small labeled sample set in an interactive setting, but we show that it can also be pretrained on related but not identical data-such as a building damage dataset-before being applied to tasks like identifying buildings under construction. We demonstrate the efficacy of our approach on the publicly available Tiny-DOTA and xBD dataset, as well as two study-cases on proprietary datasets of greenhouses and houses under construction ('waffle homes'). Detecting greenhouses is highly relevant in the context of damage assessment, while 'waffle homes' aid understanding typical floorplans and building codes in different areas, both thereby supporting population modeling, emergency response, and policy planning. Our method outperforms the state of the art in interactive rotated object detection on the Tiny-DOTA dataset by 5.7 percent, and improves upon the non interactive RTDETR by 7.85 to 19.39 percent (depending on the number of provided samples) while maintaining its real-time efficiency. © 2025 ieee.

关键词： pattern recognition

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Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text recognition

Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text Rec...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Lukas Hüttner Martin Mayr Thomas Gorges Fei Wu Mathias Seuret Andreas Maier Vincent Christlein Pattern Recognition Lab Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The continuous expansion of neural network sizes is a notable trend in machine learning, with transformer models exceeding 20 billion parameters in computer vision. This growth comes with rising demands for computational resources and large-scale datasets. Efficient techniques for transfer learning thus become an attractive option in setups with limited data, as in handwriting recognition. Recently, parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and weight-decomposed low-rank adaptation (DoRA), have gained wide-spread interest. In this paper, we explore tradeoffs in parameter-efficient transfer learning using the synthetically pretrained Transformer-Based Optical Character recognition (TrOCR) model for handwritten text recognition with LoRA and DoRA. Additionally, we analyze the performance of full fine-tuning with a limited number of samples, scaling from a few-shot learning scenario up to using the whole dataset. We conduct experiments on the popular IAM Handwriting database as well as the historical READ 2016 dataset. We find that (a) LoRA/DoRA does not outperform full fine-tuning as opposed to a recent paper and (b) LoRA/DoRA is not substantially faster than full fine-tuning of TrOCR.

关键词： Training Handwriting recognition computer vision Adaptation models Runtime Text recognition Databases Transfer learning Training data Transformers

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Gate-Shift-Pose: Enhancing Action recognition in Sports with Skeleton Information

Gate-Shift-Pose: Enhancing Action Recognition in Sports with...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Edoardo Bianchi Oswald Lanz Free University of Bozen-Bolzano Piazza Università 1 Bolzano Italy

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

This paper introduces Gate-Shift-Pose, an enhanced version of Gate-Shift-Fuse networks, designed for athlete fall classification in figure skating by integrating skeleton pose data alongside RGB frames. We evaluate two fusion strategies: early-fusion, which combines RGB frames with Gaussian heatmaps of pose keypoints at the input stage, and latefusion, which employs a multi-stream architecture with attention mechanisms to combine RGB and pose features. Experiments on the FR-FS dataset demonstrate that Gate-Shift-Pose significantly outperforms the RGB-only baseline, improving accuracy by up to 40% with ResNet18 and 20% with ResNet50. Early-fusion achieves the highest accuracy (98.08%) with ResNet50, leveraging the model's capacity for effective multimodal integration, while latefusion is better suited for lighter backbones like ResNet18. These results highlight the potential of multimodal architectures for sports action recognition and the critical role of skeleton pose information in capturing complex motion patterns.

关键词： Heating systems Accuracy conferences computer architecture Logic gates Skeleton Multisensory integration pattern recognition Sports Residual neural networks

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Lights, Camera, Matching: The Role of Image Illumination in Fair Face recognition

Lights, Camera, Matching: The Role of Image Illumination in ...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Gabriella Pangelinan Grace Bezold Haiyu Wu Michael C. King Kevin W. Bowyer Florida Institute of Technology Melbourne FL USA University of Notre Dame Notre Dame IN

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Facial brightness is a key image quality factor impacting face recognition accuracy differentials across demographic groups. In this work, we aim to decrease the accuracy gap between the similarity score distributions for Caucasian and African American female mated image pairs, as measured by d’ between distributions. To balance brightness across demographic groups, we conduct three experiments, interpreting brightness in the face skin region either as median pixel value or as the distribution of pixel values. Balancing based on median brightness alone yields up to a 46.8% decrease in d’, while balancing based on brightness distribution yields up to a 57.6% decrease. In all three cases, the similarity scores of the individual distributions improve, with mean scores maximally improving 5.9% for Caucasian females and 3.7% for African American females.

关键词： Image quality computer vision Accuracy Face recognition conferences Brightness Lighting Licenses Cameras Skin

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U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmentation

U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmen...

引用

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： Wang, Xin Liu, Xiaoyu Huang, Peng Huang, Pu Hu, Shu Zhu, Hongtu Albany United States School of Physics and Electronics Shandong Normal University Jinan China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Department of Computer and Information Technology Purdue University West Lafayette United States University of North Carolina at Chapel Hill Chapel Hill United States

ISBN: (纸本)9783031818530

Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by finding flat minima in the loss landscape, thereby reducing overfitting. Our method was evaluated in the CVPR24 MedSAM on Laptop challenge, where U-MedSAM demonstrated promising performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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LatentPS: Image Editing Using Latent Representations in Diffusion Models

LatentPS: Image Editing Using Latent Representations in Diff...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Zilong Wu Hideki Murata Nayu Takahashi Qiyu Wu Yoshimasa Tsuruoka The University of Tokyo Dentsu Digital

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Generative image models have significantly advanced recently, enabling detailed image generation from textual prompts. However, accurately describing complex image with textual prompts alone is often challenging. To address this, we propose LatentPS, a novel method for image editing that modifies latent representations during the intermediate stages of the diffusion process. LatentPS leverages the properties of the diffusion process to maintain the structure of synthesized images while editing images through simple operations on latent representations directly. We show that LatentPS achieves promising results in various image editing tasks including moving, resizing and pasting.

关键词： Image quality computer vision Shape Image synthesis Image color analysis conferences Diffusion processes Diffusion models Object recognition

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Offline Signature Verification in the Banking Domain

Offline Signature Verification in the Banking Domain

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Valentina Arrigoni Group AI Products UniCredit Milan Italy

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

In this paper, we address a specific industrial application of offline signature verification, in a context arising in the banking domain. In this context, we need to verify signatures placed in different types of real-world documents, e.g. bank cheques. Due to the huge amount of clients the problem is framed into the writer-independent configuration. Peculiar to this industrial application is that the signatures are associated with additional information, i.e. the personal data associated to the bank account. We propose a deep learning architecture to solve the signature verification of our industrial application, in which we take advantage of the additional textual information with a pretraining of an image embedding component driven by a text-recognition task. Moreover, our model employs an attention mechanism over the image embeddings to learn more powerful features. We extensively compare our model with a state-of-the-art siamese approach, with a text recognition model adopted to specifically solve this task and using a general purpose image encoder pretrained on the ImageNet dataset instead of text recognition embeddings. Our model proves to be more effective to solve the offline signature verification in two real-world large scale datasets. Ablation studies also confirm the importance of our ideas: the pretraining of the image encoders with a text recognition task and the attention mechanism.

关键词： Handwriting recognition Data privacy computer vision Attention mechanisms Text recognition conferences Deep architecture Banking computer architecture

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Camera-based Intruder Detection and Monitoring of Ship Crew Work Hours

Camera-based Intruder Detection and Monitoring of Ship Crew ...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Md Mahmuddun Nabi Murad Bora San Turgut Awwab Ahmed Gokhan Camliyurt Yasin Yilmaz University of South Florida Tampa FL USA Istanbul University Istanbul Turkey Korea Maritime and Ocean University Busan South Korea

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Due to technological developments and commercial pressures, the crew size on ships has gradually decreased. As a result of the decrease in crew sizes as well as port and turnaround times, seafarers' work hours have increased and working on ships has become increasingly challenging. The health and performance of fatigued crews deteriorates and the risk of causing accidents increases. The working hour limits of seafarers are specified in international leg-islation, but there is no system that provides evidence to check whether the records of work and rest hours reflect the truth. In this study, a face recognition system is presented using CCTV cameras on ships to monitor the work and rest hours of the crew, and to detect intruders on ships. A new CCTV dataset from a commercial ship is presented to study intruder detection and work hours monitoring.

关键词： computer vision Face recognition conferences Seaports Cameras Marine vehicles Monitoring Accidents

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