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Expression Transfer Using Flow-based Generative Models

Expression Transfer Using Flow-based Generative Models

引用

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

作者： Valenzuela, Andrea Segura, Carlos Diego, Ferran Gomez, Vicenc Univ Pompeu Fabra Barcelona Catalonia Spain Tel Res Barcelona Catalonia Spain

ISBN: (纸本)9781665448994

Among the different deepfake generation techniques, flow-based methods appear as natural candidates. Due to the property of invertibility, flow-based methods eliminate the necessity of person-specific training and are able to reconstruct any input image almost perfectly to human perception. We present a method for deepfake generation based on facial expression transfer using flow-based generative models. Our approach relies on simple latent vector operations akin to the ones used for attribute manipulation, but for transferring expressions between identity source-target pairs. We show the feasibility of this approach using a pre-trained Glow model and small sets of source and target images, not necessarily considered during prior training. We also provide an evaluation pipeline of the generated images in terms of similarities between identities and Action Units encoding the expression to be transferred. Our results show that an efficient expression transfer is feasible by using the proposed approach setting up a first precedent in deepfake content creation, and its evaluation, independently of the training identities.

关键词： Training computer vision Image coding conferences Computational modeling Pipelines pattern recognition

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Collaborative Image and Object Level Features for Image Colourisation

Collaborative Image and Object Level Features for Image Colo...

引用

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

作者： Pucci, Rita Micheloni, Christian Martinel, Niki Univ Udine Udine Italy

ISBN: (纸本)9781665448994

Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum. Previous approaches attacked the problem either by requiring intense user-interactions or by exploiting the ability of convolutional neural networks (CNNs) in learning image-level (context) features. However, obtaining human hints is not always feasible and CNNs alone are not able to learn entity-level semantics, unless multiple models pre-trained with supervision are considered. In this work, we propose a single network, named UCapsNet, that takes into consideration the image-level features obtained through convolutions and entity-level features captured by means of capsules. Then, by skip connections over different layers, we enforce collaboration between such the convolutional and entity factors to produce a high-quality and plausible image colourisation. We pose the problem as a classification task that can be addressed by a fully unsupervised approach, thus requires no human effort. Experimental results on three benchmark datasets show that our approach outperforms existing methods on standard quality metrics and achieves state-of-the-art performances on image colourisation. A large scale user study shows that our method is preferred over existing solutions. Code available at https://***/Riretta/Image_Colourisation_WiCV_2021.

关键词： Convolutional codes computer vision Semantics Collaboration Training data Feature extraction pattern recognition

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EQFace: A Simple Explicit Quality Network for Face recognition

EQFace: A Simple Explicit Quality Network for Face Recogniti...

引用

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

作者： Liu, Rushuai Tan, Weijun Shenzhen Deepcam Informat Technol Shenzhen Peoples R China LinkSprite Technol Longmont CO USA

ISBN: (纸本)9781665448994

As the deep learning makes big progresses in still-image face recognition, unconstrained video face recognition is still a challenging task due to low quality face images caused by pose, blur, occlusion, illumination etc. In this paper we propose a network for face recognition which gives an explicit and quantitative quality score at the same time when a feature vector is extracted. To our knowledge this is the first network that implements these two functions in one network online. This network is very simple by adding a quality network branch to the baseline network of face recognition. It does not require training datasets with annotated face quality labels. We evaluate this network on both still-image face datasets and video face datasets and achieve the state-of-the-art performance in many cases. This network enables a lot of applications where an explicit face quality scpre is used. We demonstrate three applications of the explicit face quality, one of which is a progressive feature aggregation scheme in online video face recognition. We design an experiment to prove the benefits of using the face quality in this application. Code will be available at https://***/deepcam-cn/facequality.

关键词： Training Knowledge engineering Deep learning Image recognition Face recognition Training data Lighting

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Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting

Camera Calibration and Player Localization in SoccerNet-v2 a...

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

作者： Cioppa, Anthony Deliege, Adrien Magera, Floriane Giancola, Silvio Barnich, Olivier Ghanem, Bernard Van Droogenbroeck, Marc Univ Liege Liege Belgium EVS Broadcast Equipment Seraing Belgium KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781665448994

Soccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera calibration and on its current limitations for the scientific community. More precisely, we tackle the absence of a large-scale calibration dataset and of a public calibration network trained on such a dataset. Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games. We further release our distilled network, and leverage it to provide 3 ways of representing the calibration results along with player localization. Finally, we exploit those representations within the current best architecture for the action spotting task of SoccerNet-v2, and achieve new state-of-the-art performances.

关键词： Location awareness Deep learning computer vision Games computer architecture Tools Cameras

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BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains

BalaGAN: Cross-Modal Image Translation Between Imbalanced Do...

引用

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

作者： Patashnik, Or Danon, Dov Zhang, Hao Cohen-Or, Daniel Tel Aviv Univ Tel Aviv Israel Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665448994

State-of-the-art image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity. We introduce a new unsupervised translation network, BalaGAN, specifically designed to tackle the domain imbalance problem. We leverage the latent modalities of the richer domain to turn the image-to-image translation problem, between two imbalanced domains, into a multi-class translation problem, more resembling the style transfer setting. Specifically, we analyze the source domain and learn a decomposition of it into a set of latent modes or classes, without any supervision. This leaves us with a multitude of balanced crossdomain translation tasks, between all pairs of classes, including the target domain. During inference, the trained network takes as input a source image, as well as a reference style image from one of the modes as a condition, and produces an image which resembles the source on the pixel-wise level, but shares the same mode as the reference. We show that employing modalities within the dataset improves the quality of the translated images, and that BalaGAN outperforms strong baselines of both unconditioned and style-transfer-based image-to-image translation methods, in terms of image quality and diversity.

关键词： Training Image quality computer vision conferences pattern recognition Task analysis

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Group Leakage Overestimates Performance: A Case Study in Keystroke Dynamics

Group Leakage Overestimates Performance: A Case Study in Key...

引用

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

作者： Ayotte, Blaine Banavar, Mahesh K. Hou, Daqing Schuckers, Stephanie Clarkson Univ Dept Elect & Comp Engn 8 Clarkson Ave Potsdam NY 13699 USA

ISBN: (纸本)9781665448994

Keystroke dynamics is a powerful behavioral biometric capable of user authentication based on typing patterns. As larger keystroke datasets become available, machine learning and deep learning algorithms are becoming popular. Knowledge of every possible impostor is not known during training which means that keystroke dynamics is an open set recognition problem. Treating open set recognition problems as closed set (assuming samples from all impostors are present) can cause models to incur data leakage, which can provide unrealistic overestimates of performance. It is a common problem in machine learning and can cause models to report higher accuracies than would be expected in the real world. In this paper, we outline open set recognition and discuss how, if not handled properly, it can lead to data leakage. The performance of common machine learning methods, such as SVM and MLP are investigated with and without leakage to clearly demonstrate the differences in performance. A synthetic dataset and a publicly available keystroke dynamics fixed-text dataset are used for research transparency and reproducibility.

关键词： Training Support vector machines Deep learning computer vision Machine learning algorithms Heuristic algorithms conferences

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Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation

Unsupervised Detection of Cancerous Regions in Histology Ima...

引用

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

作者： Stepec, Dejan Skocaj, Danijel Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1000 Slovenia XLAB Doo Pot Za Brdom 100 Ljubljana 1000 Slovenia

ISBN: (纸本)9781665448994

Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and obtain labeled data. Obtaining labeled data is especially difficult in biomedical applications, where only trained domain experts can provide labels, which often come in large diversity and complexity. Recently presented approaches for unsupervised detection of visual anomalies approaches omit the need for labeled data and demonstrate promising results in domains, where anomalous samples significantly deviate from the normal appearance. Despite promising results, the performance of such approaches still lags behind supervised approaches and does not provide a one-fits-all solution. In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.

关键词： Visualization computer vision Image analysis Histopathology conferences Biomedical measurement pattern recognition

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Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices

Extremely Lightweight Quantization Robust Real-Time Single-I...

引用

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

作者： Ayazoglu, Mustafa Aselsan Res Ankara Turkey

ISBN: (纸本)9781665448994

Single-Image Super Resolution (SISR) is a classical computer vision problem and it has been studied for over decades. With the recent success of deep learning methods, recent work on SISR focuses solutions with deep learning methodologies and achieves state-of-the-art results. However most of the state-of-the-art SISR methods contain millions of parameters and layers, which limits their practical applications. In this paper, we propose a hardware (Synaptics Dolphin NPU) limitation aware, extremely lightweight quantization robust real-time super resolution network (XLSR). The proposed model's building block is inspired from root modules introduced in [15] for Image classification. We successfully applied root modules to SISR problem, further more to make the model uint8 quantization robust we used Clipped ReLU at the last layer of the network and achieved great balance between reconstruction quality and runtime. Furthermore, although the proposed network contains 30x fewer parameters than VDSR [16] its performance surpasses it on Div2K validation set. The network proved itself by winning Mobile AI 2021 Real-Time Single Image Super Resolution Challenge.

关键词： Performance evaluation Deep learning computer vision Quantization (signal) Image resolution Runtime Computational modeling

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Towards computer vision and Deep Learning Facilitated Pollination Monitoring for Agriculture

Towards Computer Vision and Deep Learning Facilitated Pollin...

引用

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

作者： Ratnayake, Malika Nisal Dyer, Adrian G. Dorin, Alan Monash Univ Fac Informat Technol Clayton Vic Australia RMIT Univ Sch Media & Commun Melbourne Vic Australia Monash Univ Dept Physiol Clayton Vic Australia

ISBN: (纸本)9781665448994

Globally, pollinators affect 35% of agricultural land and play a key role in food production. Consequently, monitoring is useful to understand the contribution insects make towards crop pollination. Traditional sampling techniques used in insect monitoring have several drawbacks, including that they are labour intensive and potentially unreliable. Some of these drawbacks may be overcome using computer vision and deep learning-based approaches to automate pollination monitoring. In this paper, we present a pipeline for computer vision-based pollination monitoring and propose a novel algorithm, Polytrack, that tracks multiple insects simultaneously in complex agricultural environments. Our algorithm uses deep learning and foreground/background segmentation to detect and track insects. We achieved precision and recall rates of 0.975 and 0.972 respectively when monitoring honeybees foraging in our test sites within the polytunnels of an industrial strawberry farm. Polytrack includes a flower identification module to automate collection of insect-flower interaction data, and a low-resolution processing mode that reduces computational demands placed on the processor to bring the software towards the requirements of low-powered monitoring hardware.

关键词： Deep learning computer vision Visualization Insects Pipelines Production Agriculture

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Multi-Target Multi-Camera Vehicle Tracking for City-Scale Traffic Management

Multi-Target Multi-Camera Vehicle Tracking for City-Scale Tr...

引用

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

作者： Shim, Kyujin Yoon, Sungjoon Ko, Kangwook Kim, Changick Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9781665448994

Multi-target multi-camera (MTMC) tracking is one of the important fields in computer vision, where multiple objects are tracked across multiple cameras. MTMC tracking can be applied to various tasks such as crowd analysis, city-scale traffic management, and transportation systems analysis for intelligent city planning. However, it is challenging due to the large variety of conditions of each camera, such as perspective and illumination. Furthermore, MTMC tracking for vehicles is more problematic because of the relatively large inter-class similarity and intra-class variability. In this paper, we tackle the MTMC tracking problem for vehicles by dividing it into three main steps: (i) vehicle detection and feature extraction, (ii) multi-target single-camera tracking using the appearance feature of each vehicle, and (iii) multi-camera association of local trajectories from each camera. Our method shows comparable results with other highly-ranked methods in AI City Challenge 2021 and outperforms a recent MTMC tracking method that ranked first place in AI City Challenge 2020.

关键词： computer vision Urban planning Pipelines Transportation Cameras Feature extraction Prediction algorithms

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