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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是131-140 订阅

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Dissecting the High-Frequency Bias in Convolutional Neural Networks

Dissecting the High-Frequency Bias in Convolutional Neural N...

引用

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

作者： Abello, Antonio A. Hirata Jr, Roberto Wang, Zhangyang Univ Sao Paulo Butanta Sao Paulo SP Brazil Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665448994

For convolutional neural networks (CNNs), a common hypothesis that explains both their generalization capability and their characteristic brittleness is that these models are implicitly regularized to rely on imperceptible high-frequency patterns, more than humans would do. This hypothesis has seen some empirical validation, but most works do not rigorously divide the image frequency spectrum. We present a model to divide the spectrum in disjointed discs based on the distribution of energy and apply simple feature importance procedures to test whether high-frequencies are more important than lower ones. We find evidence that mid or high-level frequencies are disproportionately important for CNNs. The evidence is robust across different datasets and networks. Moreover, we find the diverse effects of the network's attributes, such as architecture and depth, on frequency bias and robustness in general.

关键词： computer vision conferences computer architecture Frequency conversion Robustness Frequency diversity pattern recognition

来源：评论

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Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example

Deep Graphics Encoder for Real-Time Video Makeup Synthesis f...

引用

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

作者： Kips, Robin Jiang, Ruowei Ba, Sileye Phung, Edmund Aarabi, Parham Gori, Pietro Perrot, Matthieu Bloch, Isabelle LOreal Res & Innovat Clichy France Inst Polytech Paris Telecom Paris LTCI Paris France Modiface Toronto ON Canada Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781665448994

While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task. In this paper, we introduce an inverse computer graphics method for automatic makeup synthesis from a reference image, by learning a model that maps an example portrait image with makeup to the space of rendering parameters. This method can be used by artists to automatically create realistic virtual cosmetics image samples, or by consumers, to virtually try-on a makeup extracted from their favorite reference image.

关键词： computer vision conferences Estimation Streaming media Rendering (computer graphics) Real-time systems pattern recognition

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Generative Flows as a General Purpose Solution for Inverse Problems

Generative Flows as a General Purpose Solution for Inverse P...

引用

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

作者： Chavez, Jose A. San Pablo Catholic Univ Arequipa Peru

ISBN: (纸本)9781665487399

Due to the success of generative flows to model data distributions, they have been explored in inverse problems. Given a pre-trained generative flow, previous work proposed to minimize the 2-norm of the latent variables as a regularization term. The intuition behind it was to ensure high likelihood latent variables that produce the closest restoration. However, high-likelihood latent variables may generate unrealistic samples as we show in our experiments. We therefore propose a solver to directly produce high-likelihood reconstructions. We hypothesize that our approach could make generative flows a general purpose solver for inverse problems. Furthermore, we propose 1x1 coupling functions to introduce permutations in a generative flow. It has the advantage that its inverse does not require to be calculated in the generation process. Finally, we evaluate our method for denoising, deblurring, inpainting, and colorization. We observe a compelling improvement of our method over prior works.

关键词： Couplings computer vision Inverse problems conferences Noise reduction Data models pattern recognition

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Finding Facial Forgery Artifacts with Parts-Based Detectors

Finding Facial Forgery Artifacts with Parts-Based Detectors

引用

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

作者： Schwarcz, Steven Chellappa, Rama Univ Maryland College Pk MD 20742 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781665448994

Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day. In this paper, we seek to develop a generalizable, explainable solution to detecting these manipulated videos. To achieve this, we design a series of forgery detection systems that each focus on one individual part of the face. These parts-based detection systems, which can be combined and used together in a single architecture, meet all of our desired criteria - they generalize effectively between datasets and give us valuable insights into what the network is looking at when making its decision. We thus use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deep-fake Detection Challenge datasets, examining not just what the detectors find but also collecting and analyzing useful related statistics on the datasets themselves.

关键词： Deep learning computer vision Social networking (online) Face recognition conferences Neural networks Detectors

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Comparison of deep transfer learning strategies for digital pathology 31

Comparison of deep transfer learning strategies for digital ...

引用

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

作者： Mormont, Romain Geurts, Pierre Maree, Raphael Univ Liege Liege Belgium

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we study deep transfer learning as a way of overcoming object recognition challenges encountered in the field of digital pathology. Through several experiments, we investigate various uses of pre-trained neural network architectures and different combination schemes with random forests for feature selection. Our experiments on eight classification datasets show that densely connected and residual networks consistently yield best performances across strategies. It also appears that network fine-tuning and using inner layers features are the best performing strategies, with the former yielding slightly superior results.

关键词： Feature extraction Task analysis Biomedical imaging Training Support vector machines computer vision

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Multistage Fusion of Face Matchers

Multistage Fusion of Face Matchers

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

作者： Tulyakov, Sergey Sankaran, Nishant Mohan, Deen Setlur, Srirangaraj Govindaraju, Venu Univ Buffalo Ctr Unified Biometr & Sensors Buffalo NY 14260 USA

ISBN: (纸本)9781665448994

Multistage, or serial, fusion refers to the algorithms sequentially fusing an increased number of matching results at each step and making decisions about accepting or rejecting the match hypothesis, or going to the next step. Such fusion methods are beneficial in the situations where running additional matching algorithms needed for later stages is time consuming or expensive. The construction of multistage fusion methods is challenging, since it requires both learning fusion functions and finding optimal decision thresholds for each stage. In this paper, we propose the use of single neural network for learning the multistage fusion. In addition we discuss the choices for the performance measurements of the trained algorithms and for the selection of network training optimization criteria. We perform the experiments using three face matching algorithms and IJB-A and IJB-C databases.

关键词： Training Measurement computer vision Error analysis Databases Face recognition conferences

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Contrastive Domain Adaptation

Contrastive Domain Adaptation

引用

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

作者： Thota, Mamatha Leontidis, Georgios Univ Lincoln Sch Comp Sci Lincoln LN6 7TS England Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

ISBN: (纸本)9781665448994

Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain adaptation remains largely underexplored. In this paper, we propose to extend contrastive learning to a new domain adaptation setting, a particular situation occurring where the similarity is learned and deployed on samples following different probability distributions without access to labels. Contrastive learning learns by comparing and contrasting positive and negative pairs of samples in an unsupervised setting without access to source and target labels. We have developed a variation of a recently proposed contrastive learning framework that helps tackle the domain adaptation problem, further identifying and removing possible negatives similar to the anchor to mitigate the effects of false negatives. Extensive experiments demonstrate that the proposed method adapts well, and improves the performance on the downstream domain adaptation task.

关键词： Training computer vision Adaptation models Visualization Machine learning algorithms Pipelines Machine learning

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Application of computer vision and vector space model for tactical movement classification in badminton 30

Application of computer vision and vector space model for ta...

引用

30th ieee/cvf conference on computer vision and pattern recognition workshops (cvprw)

作者： Weeratunga, Kokum Dharmaratne, Anuja How, Khoo Boon Monash Univ Sch Informat Technol Clayton Vic Australia Monash Univ Sch Engn Clayton Vic Australia Natl Sports Inst Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781538607336

Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. The work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. The proposed approach uses computer vision techniques to automate data gathering from video footage. The image processing algorithm is validated using video footage of the highest level tournaments, including the Olympic Games. The average accuracy of player position detection is 96.03% and 97.09% on the two halves of a badminton court. Next, frequent trajectories of badminton players are extracted and classified according to their tactical relevance. The classification performs at 97.79% accuracy, 97.81% precision, 97.44% recall, and 97.62% F-score. The combination of automated player position detection, frequent trajectory extraction, and the subsequent classification can be used to automatically generate player tactical profiles.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Shadow-Mapping for Unsupervised Neural Causal Discovery

Shadow-Mapping for Unsupervised Neural Causal Discovery

引用

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

作者： Vowels, Matthew J. Camgoz, Necati Cihan Bowden, Richard Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781665448994

An important goal across most scientific fields is the discovery of causal structures underling a set of observations. Unfortunately, causal discovery methods which are based on correlation or mutual information can often fail to identify causal links in systems which exhibit dynamic relationships. Such dynamic systems (including the famous coupled logistic map) exhibit 'mirage' correlations which appear and disappear depending on the observation window. This means not only that correlation is not causation but, perhaps counter-intuitively, that causation may occur without correlation. In this paper we describe Neural Shadow-Mapping, a neural network based method which embeds high-dimensional video data into a low-dimensional shadow representation, for subsequent estimation of causal links. We demonstrate its performance at discovering causal links from video-representations of dynamic systems.

关键词： computer vision Correlation conferences Neural networks Estimation pattern recognition Dynamical systems

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ObjectGraphs: Using Objects and a Graph Convolutional Network for the Bottom-up recognition and Explanation of Events in Video

ObjectGraphs: Using Objects and a Graph Convolutional Networ...

引用

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

作者： Gkalelis, Nikolaos Goulas, Andreas Galanopoulos, Damianos Mezaris, Vasileios CERTH ITI 6th Km Charilaou Thermi RdPOB 60361 Thessaloniki Greece

ISBN: (纸本)9781665448994

In this paper a novel bottom-up video event recognition approach is proposed, ObjectGraphs, which utilizes a rich frame representation and the relations between objects within each frame. Following the application of an object detector (OD) on the frames, graphs are used to model the object relations and a graph convolutional network (GCN) is utilized to perform reasoning on the graphs. The resulting object-based frame-level features are then forwarded to a long short-term memory (LSTM) network for video event recognition. Moreover, the weighted in-degrees (WiDs) derived from the graph's adjacency matrix at frame level are used for identifying the objects that were considered most (or least) salient for event recognition and contributed the most (or least) to the final event recognition decision, thus providing an explanation for the latter. The experimental results show that the proposed method achieves state-of-the-art performance on the publicly available FCVID and YLI-MED datasets(1).

关键词： computer vision Computational modeling conferences Detectors Cognition pattern recognition Object recognition

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