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Dynamic Appearance Modelling from Minimal Cameras

Dynamic Appearance Modelling from Minimal Cameras

引用

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

作者： Bridgeman, Lewis Guillemaut, Jean-Yves Hilton, Adrian Univ Surrey CVSSP Guildford Surrey England

ISBN: (纸本)9781665448994

We present a novel method for modelling dynamic texture appearance from a minimal set of cameras. Previous methods to capture the dynamic appearance of a human from multi-view video have relied on large, expensive camera setups, and typically store texture on a frame-by-frame basis. We fit a parameterised human body model to multi-view video from minimal cameras (as few as 3), and combine the partial texture observations from multiple viewpoints and frames in a learned framework to generate full-body textures with dynamic details given an input pose. Key to our method are our multi-band loss functions, which apply separate blending functions to the high and low spatial frequencies to reduce texture artefacts. We evaluate our method on a range of multi-view datasets, and show that our model is able to accurately produce full-body dynamic textures, even with only partial camera coverage. We demonstrate that our method outperforms other texture generation methods on minimal camera setups.

关键词： computer vision Computational modeling conferences Biological system modeling Cameras pattern recognition

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DFM: A Performance Baseline for Deep Feature Matching

DFM: A Performance Baseline for Deep Feature Matching

引用

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

作者： Efe, Ufuk Ince, Kutalmis Gokalp Alatan, A. Aydin Middle East Tech Univ Dept Elect & Elect Engn Ctr Image Anal OGAM Ankara Turkey

ISBN: (纸本)9781665448994

A novel image matching method is proposed that utilizes learned features extracted by an off-the-shelf deep neural network to obtain a promising performance. The proposed method uses pre-trained VGG architecture as a feature extractor and does not require any additional training specific to improve matching. Inspired by well-established concepts in the psychology area, such as the Mental Rotation paradigm, an initial warping is performed as a result of a preliminary geometric transformation estimate. These estimates are simply based on dense matching of nearest neighbors at the terminal layer of VGG network outputs of the images to be matched. After this initial alignment, the same approach is repeated again between reference and aligned images in a hierarchical manner to reach a good localization and matching performance. Our algorithm achieves 0.57 and 0.80 overall scores in terms of Mean Matching Accuracy (MMA) for 1 pixel and 2 pixels thresholds respectively on Hpatches dataset [4], which indicates a better performance than the state-of-the-art.

关键词： Deep learning Training Location awareness computer vision Image matching conferences Psychology

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Inaccuracy of State-Action Value Function For Non-Optimal Actions in Adversarially Trained Deep Neural Policies

Inaccuracy of State-Action Value Function For Non-Optimal Ac...

引用

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

作者： Korkmaz, Ezgi KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781665448994

The introduction of deep neural networks as function approximator for the state-action value function has led to the creation of a new research area for self-learning systems that explore policies from high dimensional input. While the success of deep neural policies has resulted in the deployment of these policies in diversified application domains, there are significant concerns regarding their robustness towards specifically crafted malicious perturbations introduced to their inputs. Several studies have focused on making deep neural policies resistant to such perturbations via training with the existence of these perturbations (i.e. adversarial training). In this paper we focus on conducting an investigation on the state-action value function learned by state-of-the-art adversarially trained deep neural policies and vanilla trained deep neural policies. We perform several experiments in the OpenAI Baselines and we show that the state-action value functions learned by vanilla trained deep neural policies have better estimates for the non-optimal actions than the state-of-the-art adversarially trained deep neural policies. We believe our study lays out intriguing properties of adversarial training and could be critical step towards obtaining robust and reliable policies.

关键词： Training Resistance Deep learning computer vision Systematics Perturbation methods conferences

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Leveraging Style and Content features for Text Conditioned Image Retrieval

Leveraging Style and Content features for Text Conditioned I...

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

作者： Chawla, Pranit Jandial, Surgan Badjatiya, Pinkesh Chopra, Ayush Sarkar, Mausoom Krishnamurthy, Balaji IIT Kharagpur Kharagpur W Bengal India IIT Hyderabad Kandi Telangana India Adobe Media & Data Sci Res Lab San Jose CA USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665448994

Image Search is a fundamental task playing a significant role in the success of wide variety of frameworks and applications. However, with the increasing sizes of product catalogues and the number of attributes per product, it has become difficult for users to express their needs effectively. Therefore, we focus on the problem of Image Retrieval with Text Feedback, which involves retrieving modified images according to the natural language feedback provided by users. In this work, we hypothesise that since an image can be delineated by its content and style features, modifications to the image can also take place in the two sub spaces respectively. Hence, we decompose an input image into its corresponding style and content features, apply modification of the text feedback individually in both the style and content spaces and finally fuse them for retrieval. Our experiments show that our approach outperforms a recent state of the art method in this task, TIRG, that seeks to use a single vector in contrast to leveraging the modification via text over style and content spaces separately.

关键词： computer vision Fuses conferences Image retrieval Natural languages pattern recognition Task analysis

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SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep Convolutional Block

SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep...

引用

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

作者： Jo, Younghyun Yang, Sejong Kim, Seon Joo Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665448994

Multiple high-resolution (HR) images can be generated from a single low-resolution (LR) image, as super-resolution (SR) is an underdetermined problem. Recently, the conditional normalizing flow-based model, SRFlow, shows remarkable performance by learning an exact mapping from HR image manifold to a latent space. The flow-based SR model allows sampling multiple output images from a learned SR space with a given LR image. In this work, we propose SRFlow-DA which has a more suitable architecture for the SR task based on the original SRFlow model. Specifically, our approach enlarges the receptive field by stacking more convolutional layers in the affine couplings, and so our model can get more expressive power. At the same time, we reduce the total number of model parameters for efficiency. Compared to SRFlow, our SRFlow-DA achieves better or comparable PSNR and LPIPS for x4 and x8 SR tasks, while having a reduced number of parameters. In addition, our method generates visually clear results without excessive sharpness artifacts.

关键词： Couplings Training Manifolds computer vision conferences Superresolution Stacking

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PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes

PanoDR: Spherical Panorama Diminished Reality for Indoor Sce...

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

作者： Gkitsas, Vasileios Sterzentsenko, Vladimiros Zioulis, Nikolaos Albanis, Georgios Zarpalas, Dimitrios Ctr Res & Technol Hellas Thessaloniki Greece

ISBN: (纸本)9781665448994

The rising availability of commercial 360 degrees cameras that democratize indoor scanning, has increased the interest for novel applications, such as interior space re-design. Diminished Reality (DR) fulfills the requirement of such applications, to remove existing objects in the scene, essentially translating this to a counterfactual inpainting task. While recent advances in data-driven inpainting have shown significant progress in generating realistic samples, they are not constrained to produce results with reality mapped structures. To preserve the 'reality' in indoor (re-)planning applications, the scene's structure preservation is crucial. To ensure structure-aware counterfactual inpainting, we propose a model that initially predicts the structure of a indoor scene and then uses it to guide the reconstruction of an empty - background only - representation of the same scene. We train and compare against other state-of-the-art methods on a version of the Structured3D dataset [47] modified for DR, showing superior results in both quantitative metrics and qualitative results, but more interestingly, our approach exhibits a much faster convergence rate. Code and models are available at ***/VCL3D/PanoDR/

关键词： Measurement Image segmentation computer vision conferences Predictive models Planning pattern recognition

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Anchor-based Plain Net for Mobile Image Super-Resolution

Anchor-based Plain Net for Mobile Image Super-Resolution

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

作者： Du, Zongcai Liu, Jie Tang, Jie Wu, Gangshan Nanjing Ubivers State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781665448994

Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward. Though existing methods have achieved remarkable success, the majority of them demand plenty of computational resources and large amount of RAM, and thus they can not be well applied to mobile device. In this paper, we aim at designing efficient architecture for 8-bit quantization and deploy it on mobile device. First, we conduct an experiment about meta-node latency by decomposing lightweight SR architectures, which determines the portable operations we can utilize. Then, we dig deeper into what kind of architecture is beneficial to 8-bit quantization and propose anchor-based plain net (ABPN). Finally, we adopt quantization-aware training strategy to further boost the performance. Our model can outperform 8-bit quantized FSRCNN by nearly 2dB in terms of PSNR, while satisfying realistic needs at the same time. Code is avaliable at https://***/NJUJet/SR_Mobile_Quantization.

关键词： Training computer vision Quantization (signal) conferences Computational modeling Superresolution Random access memory

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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...

引用

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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Quad-DIP for X-ray cargo image decomposition

Quad-DIP for X-ray cargo image decomposition

引用

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

作者： Hu, Zheng Li, Qiang Fu, Gang Xing, Yuxiang Zhang, Li Nuctech Co Ltd AI Ctr Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781665448994

To identify different cargoes on vehicles accurately in scanned image is a tough issue. An unsupervised image decomposition method, based on a novel dual-stage double-DIP (DDIP) network, named as Quad-DIP, was proposed for the decomposition of X-ray scanned image of a cargo vehicle into vehicle and goods separately without ground truth data. The model could be effectively trained based on the fact that, firstly, the structure contents of same type vehicles were similar in the images, and secondly, the contents of goods on different vehicles were different and independent to each other. Our work focus on the content-wise correlation between them. The vehicle structure could be identified from two inputs containing the same type of vehicles, and the image could be decomposed into two components of vehicle structure and cargo information accurately after the training of Quad-DIP. We examine the accuracy of this method on the collected X-ray cargo vehicle dataset. The decomposition of Quad-DIP was more accurate than those of other published methods in literature.

关键词： Training Deep learning computer vision Correlation conferences Generative adversarial networks Image decomposition

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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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