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共 11897 条记录，以下是1501-1510 订阅

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An Iterative Quantum Approach for Transformation Estimation from Point Sets

An Iterative Quantum Approach for Transformation Estimation ...

引用

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

作者： Meli, Natacha Kuete Mannel, Florian Lellmann, Jan Univ Lubeck Inst Math & Image Comp Lubeck Germany

ISBN: (纸本)9781665469463

We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio.

关键词： computer vision Three-dimensional displays Annealing Estimation Quantum annealing pattern recognition Iterative methods

来源：评论

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Lightweight Portrait Matting via Regional Attention and Refinement

Lightweight Portrait Matting via Regional Attention and Refi...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhong, Yatao Zharkov, Ilya Microsoft Redmond WA 98052 USA

ISBN: (纸本)9798350318920;9798350318937

We present a lightweight model for high resolution portrait matting. The model does not use any auxiliary inputs such as trimaps or background captures and achieves real time performance for HD videos and near real time for 4K. Our model is built upon a two-stage framework with a low resolution network for coarse alpha estimation followed by a refinement network for local region improvement. However, a naive implementation of the two-stage model suffers from poor matting quality if not utilizing any auxiliary inputs. We address the performance gap by leveraging the vision transformer (ViT) as the backbone of the low resolution network, motivated by the observation that the tokenization step of ViT can reduce spatial resolution while retain as much pixel information as possible. To inform local regions of the context, we propose a novel cross region attention (CRA) module in the refinement network to propagate the contextual information across the neighboring regions. We demonstrate that our method achieves superior results and outperforms other baselines on three benchmark datasets while only uses 1/20 of the FLOPS compared to the existing state-of-the-art model.

关键词： Algorithms Algorithms Computational photography image and video synthesis Image recognition and understanding

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Mitigating Catastrophic Interference using Unsupervised Multi-Part Attention for RGB-IR Face recognition

Mitigating Catastrophic Interference using Unsupervised Mult...

引用

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

作者： Nikhal, Kshitij Uzuegbunam, Nkiruka Kennedy, Bridget Riggan, Benjamin S. Univ Nebraska Lincoln NE 68583 USA BlueHalo Arlington VA USA

ISBN: (纸本)9798350302493

Modern algorithms for RGB-IR facial recognitiona challenging problem where infrared probe images are matched with visible gallery images-leverage precise and accurate guidance from curated (i.e., labeled) data to bridge large spectral differences. However, supervised cross-spectral face recognition methods are often extremely sensitive due to over-fitting to labels, performing well in some settings but not in others. Moreover, when finetuning on data from additional settings, supervised cross-spectral face recognition are prone to catastrophic forgetting. Therefore, we propose a novel unsupervised framework for RGB-IR face recognition to minimize the cost and time inefficiencies pertaining to labeling large-scale, multi-spectral data required to train supervised cross-spectral recognition methods and to alleviate the effect of forgetting by removing over dependence on hard labels to bridge such large spectral differences. The proposed framework integrates an efficient backbone network architecture with part-based attention models, which collectively enhances common information between visible and infrared faces. Then, the framework is optimized using pseudo-labels and a new cross-spectral memory bank loss. This framework is evaluated on the ARL-VTF and TUFTS datasets, achieving 98.55% and 43.28% true accept rate, respectively. Additionally, we analyze effects of forgetting and show that our framework is less prone to these effects.

关键词： Face recognition

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Scene Text Image Super-resolution based on Text-conditional Diffusion Models

Scene Text Image Super-resolution based on Text-conditional ...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Noguchi, Chihiro Fukuda, Shun Yamanaka, Masao Toyota Motor Co Ltd Toyota Japan

ISBN: (纸本)9798350318920;9798350318937

Scene Text Image Super-resolution (STISR) has recently achieved great success as a preprocessing method for scene text recognition. STISR aims to transform blurred and noisy low-resolution (LR) text images in real-world settings into clear high-resolution (HR) text images suitable for scene text recognition. In this study, we leverage text-conditional diffusion models (DMs), known for their impressive text-to-image synthesis capabilities, for STISR tasks. Our experimental results revealed that text-conditional DMs notably surpass existing STISR methods. Especially when texts from LR text images are given as input, the text-conditional DMs are able to produce superior quality super-resolution text images. Utilizing this capability, we propose a novel framework for synthesizing LR-HR paired text image datasets. This framework consists of three specialized text-conditional DMs, each dedicated to text image synthesis, super-resolution, and image degradation. These three modules are vital for synthesizing distinct LR and HR paired images, which are more suitable for training STISR methods. Our experiments confirmed that these synthesized image pairs significantly enhance the performance of STISR methods in the TextZoom evaluation.

关键词： 3D Algorithms Algorithms etc Generative models for image Low-level and physics-based vision video

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Accenture-MM1: A Multimodal Person recognition Dataset

Accenture-MM1: A Multimodal Person Recognition Dataset

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： O'Brien, Kyle Rybak, Michelle Huang, Jiong Stevens, Adam Fredriksz, Madeline Chaberski, Michael Russell, Danielle Castin, Lindsey Jou, Michelle Gurrapadi, Nishant Bosch, Marc Accenture Fed Serv Arlington VA 22209 USA

ISBN: (纸本)9798350370287;9798350370713

In this paper we present a new dataset to fuel multimodal research in uncooperative and surveillance scenarios. Accenture Multimodality 1 (ACC-MM1) is a large-scale multimodal biometric recognition dataset composed of imagery and video. The dataset includes challenges such as long ranges, high pitch angles, varied atmospheric conditions, and mixed image quality levels. Ultimately, a dataset containing 227 unique subjects, 303 hours of video, and 12,344 still images was captured in indoor and outdoor conditions. In addition to traditional modalities (face, gait, etc.), data for a novel biometric modality, activity gait, was collected. Covariates included appearance changes, walking with weighted loads, and body distortions. Furthermore, to enable standardized performance testing of ACC-MM1, an evaluation protocol was created. Baseline performance of popular and novel recognition algorithms is reported to encourage research in the challenging conditions present in ACC-MM1.

关键词： Biometrics

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Scene Graph Driven Text-Prompt Generation for Image Inpainting

Scene Graph Driven Text-Prompt Generation for Image Inpainti...

引用

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

作者： Shukla, Tripti Maheshwari, Paridhi Singh, Rajhans Shukla, Ankita Kulkarni, Kuldeep Turaga, Pavan Adobe Res India San Jose CA 95110 USA Stanford Univ Stanford CA USA Arizona State Univ Tempe AZ USA

ISBN: (纸本)9798350302493

Scene editing methods are undergoing a revolution, driven by text-to-image synthesis methods. Applications in media content generation have benefited from a careful set of engineered text prompts, that have been arrived at by the artists by trial and error. There is a growing need to better model prompt generation, for it to be useful for a broad range of consumer-grade applications. We propose a novel method for text prompt generation for the explicit purpose of consumer-grade image inpainting, i.e. insertion of new objects into missing regions in an image. Our approach leverages existing inter-object relationships to generate plausible textual descriptions for the missing object, that can then be used with any text-to-image generator. Given an image and a location where a new object is to be inserted, our approach first converts the given image to an intermediate scene graph. Then, we use graph convolutional networks to 'expand' the scene graph by predicting the identity and relationships of the new object to be inserted, with respect to the existing objects in the scene. The output of the expanded scene graph is cast into a textual description, which is then processed by a text-to-image generator, conditioned on the given image, to produce the final inpainted image. We conduct extensive experiments on the Visual Genome dataset, and show through qualitative and quantitative metrics that our method is superior to other methods.

关键词： computer vision

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Super-Fibonacci Spirals: Fast, Low-Discrepancy Sampling of SO(3)

Super-Fibonacci Spirals: Fast, Low-Discrepancy Sampling of S...

引用

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

作者： Alexa, Marc TU Berlin Berlin Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Super-Fibonacci spirals are an extension of Fibonacci spirals, enabling fast generation of an arbitrary but fixed number of 3D orientations. The algorithm is simple and fast. A comprehensive evaluation comparing to other methods shows that the generated sets of orientations have low discrepancy, minimal spurious components in the power spectrum, and almost identical Voronoi volumes. This makes them useful for a variety of applications, in particular Monte Carlo sampling.

关键词： computer vision Spirals Three-dimensional displays Monte Carlo methods Tracking Pose estimation Optimization methods

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Steering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning

Steering Prototypes with Prompt-tuning for Rehearsal-free Co...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Li, Zhuowei Zhao, Long Zhang, Zizhao Zhang, Han Liu, Di Liu, Ting Metaxas, Dimitris N. Rutgers State Univ New Brunswick NJ 08901 USA Google Res Mountain View CA USA Google Cloud AI Mountain View CA USA

ISBN: (纸本)9798350318920;9798350318937

In the context of continual learning, prototypes-as representative class embeddings-offer advantages in memory conservation and the mitigation of catastrophic forgetting. However, challenges related to semantic drift and prototype interference persist. In this study, we introduce the Contrastive Prototypical Prompt (CPP) approach. Through task-specific prompt-tuning, underpinned by a contrastive learning objective, we effectively address both aforementioned challenges. Our evaluations on four challenging class-incremental benchmarks reveal that CPP achieves a significant 4% to 6% improvement over state-of-the-art methods. Importantly, CPP operates without a rehearsal buffer and narrows the performance divergence between continual and offline joint-learning, suggesting an innovative scheme for Transformer-based continual learning systems(1).

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Enhancing Skeleton-Based Action recognition in Real-World Scenarios through Realistic Data Augmentation

Enhancing Skeleton-Based Action Recognition in Real-World Sc...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Cormier, Mickael Schmid, Yannik Beyerer, Juergen Fraunhofer IOSB Karlsruhe Germany Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer Ctr Machine Learning Dresden Germany

ISBN: (纸本)9798350370287;9798350370713

Skeleton-based action recognition is a prominent research area that provides a concise representation of human motion. However, real-world scenarios pose challenges to the reliability of human pose estimation, which is fundamental to such recognition. The existing literature mainly focuses on laboratory experiments with near-perfect skeletons, and fails to address the complexities of the real world. To address this, we propose simple yet highly effective data augmentation techniques based on the observation of erroneous human pose estimation, which enhance state-of-theart methods for real-world skeleton-based action recognition. These techniques yield significant improvements (up to +4.63 accuracy) on the widely used UAV Human Dataset, a benchmark for evaluating real-world action recognition. Experimental results demonstrate the effectiveness of our augmentation techniques in compensating for erroneous and noisy pose estimation, leading to significant improvements in action recognition accuracy. By bridging the gap between laboratory experiments and real-world scenarios, our work paves the way for more reliable and practical skeleton-based action recognition systems. To facilitate reproducibility and further development, the Skelbumentations library is released at https:// github. com/ MickaelCormier/Skelbumentations. This library provides the code implementation of our augmentation techniques, enabling researchers and practitioners to easily augment skeleton sequences and improve the performance of skeleton-based action recognition models in real-world applications.

关键词： Musculoskeletal system

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Appearance Label Balanced Triplet Loss for Multi-modal Aerial View Object Classification

Appearance Label Balanced Triplet Loss for Multi-modal Aeria...

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

作者： Puttagunta, Raghunath Sai Li, Zhu Bhattacharyya, Shuvra York, George Univ Missouri Kansas City MO 64110 USA Univ Maryland College Pk MD USA US Air Force Acad Colorado Springs CO USA

ISBN: (纸本)9798350302493

Automatic target recognition (ATR) using image data is an important computer vision task with widespread applications in remote sensing for surveillance, object tracking, urban planning, agriculture, and more. Although there have been continuous advancements in this task, there is still significant room for further advancements, particularly with aerial images. This work extracts rich information from multimodal synthetic aperture radar (SAR) and electro-optical (EO) aerial images to perform object classification. Compared to EO images, the advantages of SAR images are that they can be captured at night and in any weather condition. Compared to EO images, the disadvantage of SAR images is that they are noisy. Overcoming the noise inherent to SAR images is a challenging, but worthwhile, task because of the additional information SAR images provide the model. This work proposes a training strategy that involves the creation of appearance labels to generate triplet pairs for training the network with both triplet loss and cross-entropy loss. During the development phase of the 2023 Perception Beyond Visual Spectrum (PBVS) Multi-modal Aerial Image Object Classification (MAVOC) challenge, our ResNet-34 model achieved a top-1 accuracy of 64.29% for Track 1 and our ensemble learning model achieved a top-1 accuracy 75.84% for Track 2. These values are 542% and 247% higher than the baseline values. Overall, this work ranked 3rd in both Track 1 and Track 2.

关键词： Automatic target recognition

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