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Single image HDR synthesis using a Densely Connected Dilated ConvNet

Single image HDR synthesis using a Densely Connected Dilated...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Akhil, K. A. Jiji, C., V Coll Engn Trivandrum Trivandrum Kerala India

ISBN: (纸本)9781665448994

Visual representations using high dynamic range (HDR) images have become increasingly popular because of their high quality and expressive ability. HDR images are expected to be used in a broad range of applications, including digital cinema, photography, and broadcast. The generation of a HDR image from a single exposure Low Dynamic Range (LDR) image is a challenging task where one must make up for missing data due to underexposure or overexposure and color quantization. In this paper, we propose a deep convolutional neural network (CNN) model with a stack of dilated convolutional blocks for reconstructing a HDR image from a single LDR image. Within each dilation block, the dilation rate of the convolution layer is three and progressively decreases to one. Multiple dilation convolution blocks are further connected densely to improve the representation capacity of the network. As the network is trained in a supervised manner, the additional information is reconstructed from learned features. Our experimental results show that the model effectively captures missing information that was lost from the original image.

关键词： Photography Visualization Quantization (signal) Convolution Dynamic range pattern recognition Convolutional neural networks

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Self-Supervised Learning of Pose-Informed Latents

Self-Supervised Learning of Pose-Informed Latents

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jean, Raphael St-Charles, Pierre-Luc Pirk, Soren Brodeur, Simon Menya Solut Sherbrooke PQ Canada Mila Montreal PQ Canada AMLRT Montreal PQ Canada Google Res Mountain View CA USA

ISBN: (纸本)9781665487399

Siamese network architectures trained for self-supervised instance recognition can learn powerful visual representations that are useful in various tasks. Many such approaches maximize the similarity between representations of augmented images of the same object. In this paper, we depart from traditional self-supervised learning benchmarks by defining a novel methodology for new challenging tasks such as zero shot pose estimation. Our goal is to show that common Siamese networks can effectively be trained on frame pairs from video sequences to generate pose-informed representations. Unlike parallel efforts that focus on introducing new image-space operators for data augmentation, we argue that extending the augmentation strategy by using different frames of a video leads to more powerful representations. To show the effectiveness of this approach, we use the Objectron and UCF101 datasets to learn representations and evaluate them on pose estimation, action recognition, and object re-identification. Furthermore, we carefully validate our method against a number of baselines.

关键词： Training computer vision Visualization Pose estimation Video sequences Self-supervised learning Benchmark testing

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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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Insights from the Future for Continual Learning

Insights from the Future for Continual Learning

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Douillard, Arthur Valle, Eduardo Ollion, Charles Robert, Thomas Cord, Matthieu Sorbonne Univ Paris France Heuritech Paris France Univerty Campinas Campinas Brazil CMAP Ecole Polytech Palaiseau France Valeo Ai Paris France

ISBN: (纸本)9781665448994

Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting. In this work, we propose prescient continual learning, a novel experimental setting, to incorporate existing information about the classes, prior to any training data. Usually, each task in a traditional continual learning setting evaluates the model on present and past classes, the latter with a limited number of training samples. Our setting adds future classes, with no training samples at all. We introduce Ghost Model, a representation-learning-based model for continual learning using ideas from zero-shot learning. A generative model of the representation space in concert with a careful adjustment of the losses allows us to exploit insights from future classes to constraint the spatial arrangement of the past and current classes. Quantitative results on the AwA2 and aP&Y datasets and detailed visualizations showcase the interest of this new setting and the method we propose to address it.(1)

关键词： Training computer vision Computational modeling conferences Training data pattern recognition Task analysis

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Explainable Noisy Label Flipping for Multi-Label Fashion Image Classification

Explainable Noisy Label Flipping for Multi-Label Fashion Ima...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ferreira, Beatriz Quintino Costeira, Joao P. Gomes, Joao P. Univ Lisbon ISR IST Lisbon Portugal

ISBN: (纸本)9781665448994

In online shopping applications, the daily insertion of new products requires an overwhelming annotation effort. Usually done by humans, it comes at a huge cost and yet generates high rates of noisy/missing labels that seriously hinder the effectiveness of CNNs in multi-label classification. We propose SELF-ML, a classification framework that exploits the relation between visual attributes and appearance together with the "low-rank" nature of the feature space. It learns a sparse reconstruction of image features as a convex combination of very few images - a basis - that are correctly annotated. Building on this representation, SELF-ML has a module that relabels noisy annotations from the derived combination of the clean data. Due to such structured reconstruction, SELF-ML gives an explanation of its label-flipping decisions. Experiments on a real-world shopping dataset show that SELF-ML significantly increases the number of correct labels even with few clean annotations.

关键词： Visualization computer vision Annotations conferences Buildings pattern recognition Noise measurement

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Combining Magnification and Measurement for Non-Contact Cardiac Monitoring

Combining Magnification and Measurement for Non-Contact Card...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Nowara, Ewa M. McDuff, Daniel Veeraraghavan, Ashok Rice Univ Houston TX 77251 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665448994

Deep learning approaches currently achieve the state-of-the-art results on camera-based vital signs measurement. One of the main challenges with using neural models for these applications is the lack of sufficiently large and diverse datasets. Limited data increases the chances of overfitting models to the available data which in turn can harm generalization. In this paper, we show that the generalizability of imaging photoplethysmography models can be improved by augmenting the training set with "magnified" videos. These augmentations are specifically designed to reveal useful features for recovering the photoplethysmogram. We show that using augmentations of this form is more effective at improving model robustness than other commonly used data augmentation approaches. We show better within-dataset and especially cross-dataset performance with our proposed data augmentation approach on three publicly available datasets.

关键词： Training Deep learning Photoplethysmography Data models Skin Robustness pattern recognition

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Adaptive Spatial-Temporal Fusion of Multi-Objective Networks for Compressed Video Perceptual Enhancement

Adaptive Spatial-Temporal Fusion of Multi-Objective Networks...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zheng, He Li, Xin Liu, Fanglong Jiang, Lielin Zhang, Qi Li, Fu Dang, Qingqing He, Dongliang Baidu Inc Dept Comp Vis Technol VIS Bldg 2Baidu Sci Pk Beijing Peoples R China

ISBN: (纸本)9781665448994

Perceptual quality enhancement of heavily compressed videos is a difficult, unsolved problem because there still not exists a suitable perceptual similarity loss function between two video pairs. Motivated by the fact that it is hard to design unified training objectives which are perceptual-friendly for enhancing regions with smooth content and regions with rich textures simultaneously, in this paper, we propose a simple yet effective novel solution dubbed "Adaptive Spatial-Temporal Fusion of Two-Stage Multi-Objective Networks" (ASTF) to adaptive fuse the enhancement results from networks trained with two different optimization objectives. Specifically, the proposed ASTF takes an enhancement frame along with its neighboring frames as input to jointly predict a mask to indicate regions with high-frequency textual details. Then we use the mask to fuse two enhancement results which can retain both smooth content and rich textures. Extensive experiments show that our method achieves a promising performance of compressed video perceptual quality enhancement.

关键词： Training computer vision Adaptation models Adaptive systems Fuses conferences pattern recognition

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Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay

Dual-Teacher Class-Incremental Learning With Data-Free Gener...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Choi, Yoojin El-Khamy, Mostafa Lee, Jungwon Samsung Semicond Inc SoC R&D San Diego CA 92121 USA Samsung Elect Syst LSI Suwon South Korea

ISBN: (纸本)9781665448994

This paper proposes two novel knowledge transfer techniques for class-incremental learning (CIL). First, we propose data-free generative replay (DF-GR) to mitigate catastrophic forgetting in CIL by using synthetic samples from a generative model. In the conventional generative replay, the generative model is pre-trained for old data and shared in extra memory for later incremental learning. In our proposed DF-GR, we train a generative model from scratch without using any training data, based on the pre-trained classification model from the past, so we curtail the cost of sharing pre-trained generative models. Second, we introduce dual-teacher information distillation (DT-ID) for knowledge distillation from two teachers to one student. In CIL, we use DT-ID to learn new classes incrementally based on the pre-trained model for old classes and another model (pre-)trained on the new data for new classes. We implemented the proposed schemes on top of one of the state-of-the-art CIL methods and showed the performance improvement on CIFAR-100 and ImageNet datasets.

关键词： Data privacy computer vision conferences Training data Data models pattern recognition Knowledge transfer

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Expression Transfer Using Flow-based Generative Models

Expression Transfer Using Flow-based Generative Models

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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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Initialization and Transfer Learning of Stochastic Binary Networks from Real-Valued Ones

Initialization and Transfer Learning of Stochastic Binary Ne...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Livochka, Anastasiia Shekhovtsov, Alexander Ukrainian Catholic Univ Lvov Ukraine Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9781665448994

We consider the training of binary neural networks (BNNs) using the stochastic relaxation approach, which leads to stochastic binary networks (SBNs). We identify that a severe obstacle to training deep SBNs without skip connections is already the initialization phase. While smaller models can be trained from a random (possibly data-driven) initialization, for deeper models and large datasets, it becomes increasingly difficult to obtain non-vanishing and low variance gradients when initializing randomly. In this work, we initialize SBNs from real-valued networks with ReLU activations. Real valued networks are well established, easier to train and benefit from many techniques to improve their generalization properties. We propose that closely approximating their internal features can provide a good initialization for SBN. We transfer features incrementally, layer-by-layer, accounting for noises in the SBN, exploiting equivalent reparametrizations of ReLU networks and using a novel transfer loss formulation. We demonstrate experimentally that with the proposed initialization, binary networks can be trained faster and achieve a higher accuracy than when initialized randomly.

关键词： Training computer vision conferences Transfer learning Neural networks Stochastic processes pattern recognition

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