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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

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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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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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Multistage Fusion of Face Matchers

Multistage Fusion of Face Matchers

引用

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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Fuse-PN: A Novel Architecture for Anomaly pattern Segmentation in Aerial Agricultural Images

Fuse-PN: A Novel Architecture for Anomaly Pattern Segmentati...

引用

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

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Baid, Ujjwal SGGS Inst Engn & Technol Ctr Excellence Signal & Image Proc Nanded 431606 India

ISBN: (纸本)9781665448994

Deep learning and pattern recognition in smart farming has seen rapid growth as a building bridge between crop science and computer vision. One of the important application is anomaly segmentation in agriculture like weed, standing water, cloud shadow, etc. Our research work focuses on aerial farmland image dataset known as Agriculture vision. We propose to have data fusion of R, G, B, and NIR modalities that enhances the feature extraction and also propose Efficient Fused Pyramid Network (Fuse-PN) for anomaly pattern segmentation. The proposed encoder module is a bottom-up pathway having a compound scaled network and decoder module is a top-down pyramid network enhancing features at different scales having rich semantic features with lateral connections of low-level features. This proposed approach achieved a mean dice similarity score of 0.8271 for six agricultural anomaly patterns of Agriculture vision dataset and outperforms various approaches in literature.

关键词： Deep learning Training Image segmentation computer vision Semantics Data integration computer architecture

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Dot Distance for Tiny Object Detection in Aerial Images

Dot Distance for Tiny Object Detection in Aerial Images

引用

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

作者： Xu, Chang Wang, Jinwang Yang, Wen Yu, Lei Wuhan Univ Sch Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781665448994

Object detection has achieved great progress with the development of anchor-based and anchor-free detectors. However, the detection of tiny objects is still challenging due to the lack of appearance information. In this paper, we observe that Intersection over Union (IoU), the most widely used metric in object detection, is sensitive to slight offsets between predicted bounding boxes and ground truths when detecting tiny objects. Although some new metrics such as GIoU, DIoU and CIoU are proposed, their performance on tiny object detection is still below the expected level by a large margin. In this paper, we propose a simple but effective new metric called Dot Distance (DotD) for tiny object detection where DotD is defined as normalized Euclidean distance between the center points of two bounding boxes. Extensive experiments on tiny object detection dataset show that anchor-based detectors' performance is highly improved over their baselines with the application of DotD.

关键词： Measurement computer vision conferences Object detection Detectors Euclidean distance pattern recognition

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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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Shadow-Mapping for Unsupervised Neural Causal Discovery

Shadow-Mapping for Unsupervised Neural Causal Discovery

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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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Private-Shared Disentangled Multimodal VAE for Learning of Latent Representations

Private-Shared Disentangled Multimodal VAE for Learning of L...

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

作者： Lee, Mihee Pavlovic, Vladimir Rutgers State Univ Piscataway NJ 08854 USA

ISBN: (纸本)9781665448994

Multi-modal generative models represent an important family of deep models, whose goal is to facilitate representation learning on data with multiple views or modalities. However, current deep multi-modal models focus on the inference of shared representations, while neglecting the important private aspects of data within individual modalities. In this paper, we introduce a disentangled multi-modal variational autoencoder (DMVAE) that utilizes disentangled VAE strategy to separate the private and shared latent spaces of multiple modalities. We demonstrate the utility of DMVAE two image modalities of MNIST and Google Street View House Number (SVHN) datasets as well as image and text modalities from the Oxford-102 Flowers dataset. Our experiments indicate the essence of retaining the private representation as well as the private-shared disentanglement to effectively direct the information across multiple analysis-synthesis conduits.

关键词： computer vision conferences Computational modeling Data models pattern recognition Internet Task analysis

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Modeling Fashion Compatibility with Explanation by using Bidirectional LSTM

Modeling Fashion Compatibility with Explanation by using Bid...

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

作者： Pang Kaicheng Zou Xingxing Wong, Wai Keung Hong Kong Polytech Univ Inst Text & Clothing Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Peoples R China

ISBN: (纸本)9781665448994

The goal of this paper is to model the fashion compatibility of an outfit and provide the explanations. We first extract features of all attributes of all items via convolutional neural networks, and then train the bidirectional Long Shortterm Memory (Bi-LSTM) model to learn the compatibility of an outfit by treating these attribute features as a sequence. Gradient penalty regularization is exploited for training inter-factor compatibility net which is used to compute the loss for judgment and provide its explanation which is generated from the recognized reasons related to the judgment. To train and evaluate the proposed approach, we expanded the EVALUATION3 dataset in terms of the number of items and attributes. Experiment results show that our approach can successfully evaluate compatibility with reason.

关键词： Training Industries computer vision Computational modeling conferences Feature extraction pattern recognition

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