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共 1569 条记录，以下是161-170 订阅

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Connecting Language and vision for Natural Language-Based Vehicle Retrieval

Connecting Language and Vision for Natural Language-Based Ve...

引用

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

作者： Bai, Shuai Zheng, Zhedong Wang, Xiaohan Lin, Junyang Zhang, Zhu Zhou, Chang Yang, Hongxia Yang, Yi Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Technol Sydney ReLER Lab Sydney NSW Australia Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Vehicle search is one basic task for the efficient traffic management in terms of the AI City. Most existing practices focus on the image-based vehicle matching, including vehicle re-identification and vehicle tracking. In this paper, we apply one new modality, i.e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario. the natural language-based vehicle search poses one new challenge of fine-grained understanding of both vision and language modalities. To connect language and vision, we propose to jointly train the state-of-the-art vision models with the transformer-based language model in an end-to-end manner. Except for the network structure design and the training strategy, several optimization objectives are also revisited in this work. the qualitative and quantitative experiments verify the effectiveness of the proposed method. Our proposed method has achieved the 1st place on the 5th AI City Challenge, yielding competitive performance 18.69% MRR accuracy on the private test set. We hope this work can pave the way for the future study on using language description effectively and efficiently for real-world vehicle retrieval systems. the code will be available at https://***/ShuaiBai623/AIC2021-T5-CLV.

关键词： Training Smart cities computer architecture Search problems Robustness pattern recognition Task analysis

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Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues

Joint Physical-Digital Facial Attack Detection Via Simulatin...

引用

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

作者： Xianhua He Dashuang Liang Song Yang Zhanlong Hao Hui Ma Binjie Mao Xi Li Yao Wang Pengfei Yan Ajian Liu Vision AI Department Meituan M.U.S.T MAIS CASIA China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenarios that address physical attacks and digital attacks, respectively. However, few methods are considered to integrate a model that simultaneously addresses both physical and digital attacks, implying the necessity to develop and maintain multiple models. To jointly detect physical and digital attacks within a single model, we propose an innovative approach that can adapt to any network architecture. Our approach mainly contains two types of data augmentation, which we call Simulated Physical Spoofing Clues augmentation (SPSC) and Simulated Digital Spoofing Clues augmentation (SDSC). SPSC and SDSC augment live samples into simulated attack samples by simulating spoofing clues of physical and digital attacks, respectively, which significantly improve the capability of the model to detect "unseen" attack types. Extensive experiments show that SPSC and SDSC can achieve state-of-the-art generalization in Protocols 2.1 and 2.2 of the UniAttackData dataset, respectively. Our method won first place in "Unified Physical-Digital Face Attack Detection" of the 5th Face Anti-spoofing Challenge@cvpr2024. Our final submission obtains 3.75% APCER, 0.93% BPCER, and 2.34% ACER, respectively. Our code is available at https://***/Xianhua-He/cvpr2024-face-anti-spoofing-challenge.

关键词： Adaptation models computer vision Protocols Codes Face recognition conferences Computational modeling

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Multiway Point Cloud Mosaicking with Diffusion and Global Optimization

Multiway Point Cloud Mosaicking with Diffusion and Global Op...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Shengze Jin Iro Armeni Marc Pollefeys Dániel Baráth Department of Computer Science ETH Zurich Switzerland Department of Civil and Environmental Engineering Stanford University Microsoft Mixed Reality & AI Lab Zurich Switzerland

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a novel framework for multiway point cloud mosaicking (named Wednesday), designed to co-align sets of partially overlapping point clouds - typically obtained from 3D scanners or moving RGB-D cameras - into a unified coordinate system. At the core of our approach is ODIN, a learned pairwise registration algorithm that iteratively identifies overlaps and refines attention scores, employing a diffusion-based process for denoising pairwise correlation matrices to enhance matching accuracy. Further steps include constructing a pose graph from all point clouds, performing rotation averaging, a novel robust algorithm for re-estimating translations optimally in terms of consensus maximization and translation optimization. Finally, the point cloud rotations and positions are optimized jointly by a diffusion-based approach. Tested on four diverse, large-scale datasets, our method achieves state-of-the-art pairwise and multiway registration results by a large margin on all benchmarks. Our code and models are available at https://***/jinsz/Multiway-Point-Cloud-Mosaicking-with-Diffusion-and-Global-Optimization.

关键词： Point cloud compression computer vision Accuracy three-dimensional displays Pairwise error probability Pipelines Noise reduction

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Faculty of Electrical Engineering Visual Recognition Group Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Department of Computer Science Computer Vision and Geometry Group ETH Zürich

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://***/danini/absolute-pose-from-oriented-and-sealed-features.

关键词： Location awareness Accuracy Simultaneous localization and mapping Measurement units Pose estimation Noise Inertial navigation

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Evaluating the Impact of Wide-Angle Lens Distortion on Learning-based Depth Estimation

Evaluating the Impact of Wide-Angle Lens Distortion on Learn...

引用

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

作者： Buquet, Julie Zhang, Jinsong Roulet, Patrice thibault, Simon Lalonde, Jean-Francois Univ Laval Quebec City PQ Canada Immervision Montreal PQ Canada

ISBN: (纸本)9781665448994

Most computer vision research focuses on narrow angle lenses and is not adapted to super-wide-angle (aka spherical) lenses. this is mainly because current neural networks are not designed or trained to interpret the significant barrel distortion that is introduced in the captured image by such wide angle *** these lenses capture a half-sphere or a section of sphere on the object space, barrel distortion appears when the image is projected on a 2D flat image sensor. By controlling this distortion at the lens design stage, camera designers can create some areas with augmented resolution [26]. In this work, we present an analysis of the impact of such augmented resolution on computer vision algorithm accuracy, using the problem of single image depth estimation as a case study. To this end, 360 degrees panorama datasets are warped to simulate different wideangle lens datasets, which are then used to train identical neural networks. Each lens presents specific areas of the image with augmented resolution using spatially-varying non-linear distortion. We show that this property leads to better local accuracy in depth estimation. We also demonstrate that considering lens manufacturing improves performance when tested on realistic lenses, especially in the area of augmented resolution. We further show that this property helps to locally come closer to performances obtained on perspective images without cropping the field of view.

关键词： Training computer vision Nonlinear distortion Neural networks Estimation Tools Cameras

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Assessing the Performance of Efficient Face Anti-Spoofing Detection Against Physical and Digital Presentation Attacks

Assessing the Performance of Efficient Face Anti-Spoofing De...

引用

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

作者： Luis S. Luevano Yoanna Martínez-Díaz Heydi Méndez-Vázquez Miguel González-Mendoza Davide Frey Inria CNRS IRISA Univ Rennes Rennes France Advanced Technologies Application Center (CENATAV) Havana Cuba School of Engineering and Sciences Tecnologico de Monterrey N.L. Mexico

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we examine how pre-processing and training methods impact on the performance of Lightweight CNNs through evaluations on MobileNetV3 with a spoofing detection head, dubbed "MobileNetV3-Spoof". Using the UniAttackData dataset from the 5th Face Anti-Spoofing Challenge@cvpr2024, which covers a broad spectrum of spoofing scenarios including deepfake and adversarial attack samples, we assess how well the model performs over different setups, including pre-trained models and models trained from scratch with or without initial face detection and alignment. Our results show that pre-processing steps significantly boost the model’s ability to identify spoof samples, especially against complex attacks. through detailed comparisons, we offer insights that could guide data curation and the creation of more effective and efficient anti-spoofing techniques suitable for real-world use in the era of digital face attacks. We make our code publicly available at: https://***/Inria-CENATAV-Tec/Assessing-Efficient-FAS-cvpr2024

关键词： Training Deepfakes Accuracy Sensitivity Face recognition Computational modeling conferences

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Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems

Towards Accurate Visual and Natural Language-Based Vehicle R...

引用

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

作者： Khorramshahi, Pirazh Rambhatla, Sai Saketh Chellappa, Rama Johns Hopkins Univ Baltimore MD 21218 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665448994

In this work, we consider two tracks of the 2021 NVIDIA AI City Challenge, the City-Scale Multi-Camera Vehicle Re-identification and Natural language-based Vehicle Retrieval. For the vehicle re-identification task, we employ the state-of-art Excited Vehicle Re-Identification deep representation learning model coupled with best training practices and domain adaptation techniques to obtain robust embeddings. We further refine the re-identification results through a series of post-processing steps to remove camera and vehicle orientation bias that is inherent in the task of re-identification. We also take advantage of multiple observations of a vehicle using track-level information and finally obtain fine-grained retrieval results. For the task of Natural language-based vehicle retrieval we leverage the recently proposed Contrastive Language-Image Pre-training model and propose a simple yet effective text-based vehicle retrieval system. We compare our performance against the top submissions to the challenge and our systems are ranked 8th in the public leaderboard for both tracks.

关键词： Training Visualization computer vision Adaptation models conferences Urban areas Cameras

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Modular Memorability: Tiered Representations for Video Memorability Prediction

Modular Memorability: Tiered Representations for Video Memor...

引用

conference on computer vision and pattern recognition (cvpr)

作者： théo Dumont Juan Segundo Hevia Camilo L. Fosco Mines Paris PSL – Research University Paris France Memorable AI Boston USA

the question of how to best estimate the memorability of visual content is currently a source of debate in the memorability community. In this paper, we propose to explore how different key properties of images and videos affect their consolidation into memory. We analyze the impact of several features and develop a model that emulates the most important parts of a proposed “pathway to memory”: a simple but effective way of representing the different hurdles that new visual content needs to surpass to stay in memory. this framework leads to the construction of our M3-S model, a novel memorability network that processes input videos in a modular fashion. Each module of the network emulates one of the four key steps of the pathway to memory: raw encoding, scene understanding, event understanding and memory consolidation. We find that the different representations learned by our modules are non-trivial and substantially different from each other. Additionally, we observe that certain representations tend to perform better at the task of memorability prediction than others, and we introduce an in-depth ablation study to support our results. Our proposed approach surpasses the state of the art on the two largest video memorability datasets and opens the door to new applications in the field. Our code is available at https://***/tekal-ai/modular-memorability.

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Wildfires Detection and Segmentation Using Deep CNNs and vision Transformers 26th

Wildfires Detection and Segmentation Using Deep CNNs and Vi...

引用

26th International conference on pattern recognition, ICPR 2022

作者： Ghali, Rafik Akhloufi, Moulay A. Department of Computer Science Université de Moncton MonctonNB Canada

ISBN: (纸本)9783031377419

Wildfires are an important natural risk which causes enormous damage to the environment. Many researchers are working to improve firefighting using AI. Various vision-based fire detection methods have been proposed to detect fire. However, these techniques are still limited when it comes to identifying the precise fire’s shape as well as small fire areas. For such, we propose deep wildland fire detection and segmentation models based on deep Convolutional Neural Networks (CNNs) and vision Transformers. A novel deep ensemble learning method, which combines EfficientNet-B5 and DenseNet-201 models, is proposed to identify and classify wildfires on aerial images. vision Transformers (TransUNet, MedT, and TransFire) are adopted in segmenting fire pixels and in detecting the precise shape of the fire areas using aerial and ground images. the achieved results are promising and show the potential of using deep CNNs and vision Transformers for forest fire detection and segmentation. © 2023, Springer Nature Switzerland AG.

关键词： Fires

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Learning from Synthetic Human Group Activities

Learning from Synthetic Human Group Activities

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conference on computer vision and pattern recognition (cvpr)

作者： Che-Jui Chang Danrui Li Deep Patel Parth Goel Honglu Zhou Seonghyeon Moon Samuel S. Sohn Sejong Yoon Vladimir Pavlovic Mubbasir Kapadia Rutgers University NEC Laboratories The College of New Jersey Roblox

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

the study of complex human interactions and group activities has become a focal point in human-centric computer vision. However, progress in related tasks is often hindered by the challenges of obtaining large-scale labeled datasets from real-world scenarios. To address the limitation, we introduce M3 Act, a synthetic data generator for multi-view multi-group multi-person human atomic actions and group activities. Powered by Unity Engine, M3 Act features mul-tiple semantic groups, highly diverse and photorealistic images, and a comprehensive set of annotations, which facilitates the learning of human-centered tasks across single-person, multi-person, and multi-group conditions. We demonstrate the advantages of M3 Act across three core experiments. the results suggest our synthetic dataset can significantly improve the performance of several downstream methods and replace real-world datasets to reduce cost. Notably, M3 Act improves the state-of-the-art MOTRv2 on DanceTrack dataset, leading to a hop on the leaderboard from 10 th to 2 nd place. Moreover, M3 Act opens new research for controllable 3D group activity generation. We define multiple metrics and propose a competitive baseline for the novel task. Our code and data are available at our project page: http://***/M3Act.

关键词： Measurement computer vision three-dimensional displays Costs Semantics Generators pattern recognition

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