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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11890 条记录，以下是551-560 订阅

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Composed Query Image Retrieval Using Locally Bounded Features

Composed Query Image Retrieval Using Locally Bounded Feature...

引用

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

作者： Hosseinzadeh, Mehrdad Wang, Yang Univ Manitoba Winnipeg MB Canada

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

Composed query image retrieval is a new problem where the query consists of an image together with a requested modification expressed via a textual sentence. The goal is then to retrieve the images that are generally similar to the query image, but differ according to the requested modification. Previous methods usually consider the image as a whole. In this paper, we propose a novel method that represents the image using a set of local areas in the image. The relationship between each word in the modification text and each area in the image is then explicitly established, allowing the model to accurately correlate the modification text to parts of the image. We conduct extensive experiments on three benchmark datasets. The results show that our method outperforms other state-of-the-art approaches by a considerable margin.

关键词： Image retrieval Visualization Feature extraction Task analysis computer vision Benchmark testing Pipelines

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Skin-based identification from multispectral image data using CNNs 32

Skin-based identification from multispectral image data usin...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Uemori, Takeshi Ito, Atsushi Moriuchi, Yusuke Gatto, Alexander Murayama, Jun Sony Europe BV Stuttgart Germany Sony Corp Tokyo Japan

ISBN: (纸本)9781728132938

User identification from hand images only is still a challenging task. In this paper, we propose a new biometric identification system based solely on a skin patch from a multispectral image. The system is utilizing a novel modified 3D CNN architecture which is taking advantage of multispectral data. We demonstrate the application of our system for the example of human identification from multispectral images of hands. To the best of our knowledge, this paper is the first to describe a pose-invariant and robust to overlapping real-time human identification system using hands. Additionally, we provide a framework to optimize the required spectral bands for the given spatial resolution limitations.

关键词： Computational Photography Low-level vision vision Applications and Systems

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Assessing Personally Perceived Image Quality via Image Features and Collaborative Filtering 32

Assessing Personally Perceived Image Quality via Image Featu...

引用

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

作者： Korhonen, Jari Shenzhen Univ Sch Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781728132938

During the past few years, different methods for optimizing the camera settings and post-processing techniques to improve the subjective quality of consumer photos have been studied extensively. However, most of the research in the priorart has focused on finding the optimal method for an average user. Since there is large deviation in personal opinions and aesthetic standards, the next challenge is to find the settings and post-processing techniques thatfit to the individualusers 'personaltaste. In this study, we aim to predict the personallyperceived image quality by combining classical imagefeature analysis and collaboration filtering approach known from the recommendation systems. The experimental resultsfor the proposed method show promising results. As a practical application, our work can be used for personalizing the camera settings orpost-processingparameterfsor different users and images.

关键词： Low-level vision Statistical Learning vision Applications and Systems

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Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner

Minimally Invasive Surgery for Sparse Neural Networks in Con...

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

作者： Yu, Chong NVIDIA Beijing Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

With the development of deep learning, neural networks tend to be deeper and larger to achieve good performance. Trained models are more compute-intensive and memory-intensive, which lead to the big challenges on memory bandwidth, storage, latency, and throughput. In this paper, we propose the neural network compression method named minimally invasive surgery. Different from traditional model compression and knowledge distillation methods, the proposed method refers to the minimally invasive surgery principle. It learns the principal features from a pair of dense and compressed models in a contrastive manner. It also optimizes the neural networks to meet the specific hardware acceleration requirements. Through qualitative, quantitative, and ablation experiments, the proposed method shows a compelling performance, acceleration, and generalization in various tasks.

关键词： Knowledge engineering Minimally invasive surgery Computational modeling Throughput Probability distribution pattern recognition Task analysis

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Self-supervised Augmentation Consistency for Adapting Semantic Segmentation

Self-supervised Augmentation Consistency for Adapting Semant...

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

作者： Araslanov, Nikita Roth, Stefan Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Hessian AI Darmstadt Germany

ISBN: (纸本)9781665445092

We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. In contrast to previous work, we abandon the use of computationally involved adversarial objectives, network ensembles and style transfer. Instead, we employ standard data augmentation techniques photometric noise, flipping and scaling and ensure consistency of the semantic predictions across these image transformations. We develop this principle in a lightweight self-supervised framework trained on co-evolving pseudo labels without the need for cumbersome extra training rounds. Simple in training from a practitioner's standpoint, our approach is remarkably effective. We achieve significant improvements of the state-of-the-art segmentation accuracy after adaptation, consistent both across different choices of the backbone architecture and adaptation scenarios.

关键词： Training Image segmentation computer vision Semantics computer architecture pattern recognition Complexity theory

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引用

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

作者： Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are that these maps resemble localization maps and that by combining such maps, one can obtain useful pseudo-labels for performing self-training. Our results surpass, by a large margin, the state of the art in weakly supervised phrase grounding. A similar gap in performance is obtained for a recently proposed downstream task called WWbL, in which only the image is input, without any text. Our code is available at https://***/talshaharabany/Similarity-Maps-forSelf-Training-Weakly-Supervised- Phrase-Grounding.

关键词： language reasoning vision

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Deep Depth from Focus with Differential Focus Volume

Deep Depth from Focus with Differential Focus Volume

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

作者： Yang, Fengting Huang, Xiaolei Zhou, Zihan Penn State Univ University Pk PA 16802 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Depth-from-focus (DFF) is a technique that infers depth using the focus change of a camera. In this work, we propose a convolutional neural network (CNN) to find the best-focused pixels in a focal stack and infer depth from the focus estimation. The key innovation of the network is the novel deep differential focus volume (DFV). By computing the first-order derivative with the stacked features over different focal distances, DFV is able to capture both the focus and context information for focus analysis. Besides, we also introduce a probability regression mechanism for focus estimation to handle sparsely sampled focal stacks and provide uncertainty estimation to the final prediction. Comprehensive experiments demonstrate that the proposed model achieves state-of-the-art performance on multiple datasets with good generalizability and fast speed.

关键词： Technological innovation computer vision Uncertainty Computational modeling Estimation computer architecture pattern recognition

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Understanding 3D Object Articulation in Internet Videos

Understanding 3D Object Articulation in Internet Videos

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

作者： Qian, Shengyi Jin, Linyi Rockwell, Chris Chen, Siyi Fouhey, David F. Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose to investigate detecting and characterizing the 3D planar articulation of objects from ordinary RGB videos. While seemingly easy for humans, this problem poses many challenges for computers. Our approach is based on a top-down detection system that finds planes that can be articulated. This approach is followed by optimizing for a 3D plane that explains a sequence of detected articulations. We show that this system can be trained on a combination of videos and 3D scan datasets. When tested on a dataset of challenging Internet videos and the Charades dataset, our approach obtains strong performance.

关键词： computers computer vision Three-dimensional displays Shape Surveillance Internet pattern recognition

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A variational EM framework with adaptive edge selection for blind motion deblurring 32

A variational EM framework with adaptive edge selection for ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yang, Liuge Ji, Hui Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (纸本)9781728132938

Blind motion deblurring is an important problem that receives enduring attention in last decade. Based on the observation that a good intermediate estimate of latent image for estimating motion-blur kernel is not necessarily the one closest to latent image, edge selection has proven itself a very powerful technique for achieving state-of-the-art performance in blind deblurring. This paper presented an interpretation of edge selection/reweighting in terms of variational Bayes inference, and therefore developed a novel variational expectation maximization (VEM) algorithm with built-in adaptive edge selection for blind deblurring. Together with a restart strategy for avoiding undesired local convergence, the proposed VEM method not only has a solid mathematical foundation but also noticeably outperformed the state-of-the-art methods on benchmark datasets.

关键词： Computational Photography Low-level vision

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Federated Learning with a Single Shared Image

Federated Learning with a Single Shared Image

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

作者： Soni, Sunny Saeed, Aaqib Asano, Yuki M. Univ Amsterdam Amsterdam Netherlands TU Eindhoven Eindhoven Netherlands

ISBN: (纸本)9798350365474

Federated Learning (FL) enables multiple machines to collaboratively train a machine learning model without sharing of private training data. Yet, especially for heterogeneous models, a key bottleneck remains the transfer of knowledge gained from each client model with the server. One popular method, FedDF, uses distillation to tackle this task with the use of a common, shared dataset on which predictions are exchanged. However, in many contexts such a dataset might be difficult to acquire due to privacy and the clients might not allow for storage of a large shared dataset. To this end, in this paper, we introduce a new method that improves this knowledge distillation method to only rely on a single shared image between clients and server. In particular, we propose a novel adaptive dataset pruning algorithm that selects the most informative crops generated from only a single image. With this, we show that federated learning with distillation under a limited shared dataset budget works better by using a single image compared to multiple individual ones. Finally, we extend our approach to allow for training heterogeneous client architectures by incorporating a non-uniform distillation schedule and client-model mirroring on the server side.

关键词： computer vision Federated Learning Limited Data Representation Learning

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