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Context-Aware Video Reconstruction for Rolling Shutter Cameras

Context-Aware Video Reconstruction for Rolling Shutter Camer...

引用

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

作者： Fan, Bin Dai, Yuchao Zhang, Zhiyuan Liu, Qi He, Mingyi Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

With the ubiquity of rolling shutter (RS) cameras, it is becoming increasingly attractive to recover the latent global shutter (GS) video from two consecutive RS frames, which also places a higher demand on realism. Existing solutions, using deep neural networks or optimization, achieve promising performance. However;these methods generate intermediate GS frames through image warping based on the RS model, which inevitably result in black holes and noticeable motion artifacts. In this paper, we alleviate these issues by proposing a context-aware GS video reconstruction architecture. It facilitates the advantages such as occlusion reasoning, motion compensation, and temporal abstraction. Specifically, we first estimate the bilateral motion field so that the pixels of the two RS frames are warped to a common GS frame accordingly. Then, a refinement scheme is proposed to guide the GS frame synthesis along with bilateral occlusion masks to produce high-fidelity GS video frames at arbitrary times. Furthermore, we derive an approximated bilateral motion field model, which can serve as an alternative to provide a simple but effective GS frame initialization for related tasks. Experiments on synthetic and real data show that our approach achieves superior performance over state-of-the-art methods in terms of objective metrics and subjective visual quality. Code is available at https://***/Gitcvfb/CVR.

关键词： Visualization Pipelines Neural networks Cameras Motion compensation pattern recognition Task analysis

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Searching for Fast Model Families on Datacenter Accelerators

Searching for Fast Model Families on Datacenter Accelerators

引用

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

作者： Li, Sheng Tan, Mingxing Pang, Ruoming Li, Andrew Cheng, Liqun Le, Quoc, V Jouppi, Norman P. Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the emerging datacenter (DC) accelerators. In this paper, we search for fast and accurate CNN model families for efficient inference on DC accelerators. We first analyze DC accelerators and find that existing CNNs suffer from insufficient operational intensity, parallelism, and execution efficiency and exhibit FLOPs-latency nonproportionality. These insights let us create a DC-accelerator-optimized search space, with space-to-depth, space-to-batch, hybrid fused convolution structures with vanilla and depthwise convolutions, and block-wise activation functions. We further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency. Our LACS discovers that network depth should grow much faster than image size and network width, which is quite different from the observations from previous compound scaling. With the new search space and LACS, our search and scaling on datacenter accelerators results in a new model series named EfficientNet-X. EfficientNet-X is up to more than 2X faster than Efficient-Net (a model series with state-of-the-art trade-off on FLOPs and accuracy) on TPUv3 and GPUv100, with comparable accuracy. EfficientNet-X is also up to 7X faster than recent RegNet and ResNeSt on TPUv3 and GPUv100.

关键词： Convolutional codes computer vision Convolution Computational modeling Search methods Parallel processing Hardware

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CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data

CO-SNE: Dimensionality Reduction and Visualization for Hyper...

引用

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

作者： Guo, Yunhui Guo, Haoran Yu, Stella X. Univ Calif Berkeley ICSI Berkeley CA 94720 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Hyperbolic space can naturally embed hierarchies that often exist in real-world data and semantics. While high-dimensional hyperbolic embeddings lead to better representations, most hyperbolic models utilize low-dimensional embeddings, due to non-trivial optimization and visualization of high-dimensional hyperbolic data. We propose CO-SNE, which extends the Euclidean space visualization tool, t-SNE, to hyperbolic space. Like t-SNE, it converts distances between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of high-dimensional data X and low-dimensional embedding Y. However, unlike Euclidean space, hyperbolic space is inhomogeneous: A volume could contain a lot more points at a location far from the origin. CO-SNE thus uses hyperbolic normal distributions for X and hyperbolic Cauchy instead of t-SNE's Student's t-distribution for Y, and it additionally seeks to preserve X's individual distances to the Origin in Y. We apply CO-SNE to naturally hyperbolic data and supervisedly learned hyperbolic features. Our results demonstrate that CO-SNE deflates high-dimensional hyperbolic data into a low-dimensional space without losing their hyperbolic characteristics, significantly outperforming popular visualization tools such as PCA, t-SNE, UMAP, and HoroPCA which is also designed for hyperbolic data.

关键词： Representation learning Dimensionality reduction computer vision Semantics Data visualization Gaussian distribution Nonhomogeneous media

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Continual Predictive Learning from Videos

Continual Predictive Learning from Videos

引用

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

作者： Chen, Geng Zhang, Wendong Lu, Han Gao, Siyu Wang, Yunbo Long, Mingsheng Yang, Xiaokang Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at all times. In practice, however, different prediction tasks may arrive sequentially so that the environments may change persistently throughout the training procedure. Can we develop predictive learning algorithms that can deal with more realistic, non-stationary physical environments? In this paper, we study a new continual learning problem in the context of video prediction, and observe that most existing methods suffer from severe catastrophic forgetting in this setup. To tackle this problem, we propose the continual predictive learning (CPI.) approach, which learns a mixture world model via predictive experience replay and performs test-time adaptation with non parametric task inference. We construct two new benchmarks based on RoboNet and KTH, in which different tasks correspond to different physical robotic environments or human actions. Our approach is shown to effectively mitigate forgetting and remarkably outperform the naive combinations of previous art in video prediction and continual learning.

关键词： Training computer vision Adaptation models Art Predictive models Benchmark testing Prediction algorithms

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Stable Long-Term Recurrent Video Super-Resolution

Stable Long-Term Recurrent Video Super-Resolution

引用

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

作者： Chiche, Benjamin Naoto Woiselle, Arnaud Frontera-Pons, Joana Starck, Jean-Luc Safran Elect & Def F-91344 Massy France Univ Paris Saclay Univ Paris Cite CNRS CEAAIM F-91191 Gif Sur Yvette France Inst Polytech Sci Avancees DR2I F-94200 Ivry France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recurrent models have gained popularity in deep learning (DL) based video super-resolution (VSR), due to their increased computational efficiency, temporal receptive field and temporal consistency compared to sliding-window based models. However, when inferring on long video sequences presenting low motion (i.e. in which some parts of the scene barely move), recurrent models diverge through recurrent processing, generating high frequency artifacts. To the best of our knowledge, no study about VSR pointed out this instability problem, which can be critical for some real-world applications. Video surveillance is a typical example where such artifacts would occur, as both the camera and the scene stay static for a long time. In this work, we expose instabilities of existing recurrent VSR networks on long sequences with low motion. We demonstrate it on a new long sequence dataset Quasi-Static Video Set, that we have created. Finally, we introduce a new framework of recurrent VSR networks that is both stable and competitive, based on Lipschitz stability theory. We propose a new recurrent VSR network, coined Middle Recurrent Video Super-Resolution (MRVSR), based on this framework. We empirically show its competitive performance on long sequences with low motion.

关键词： Deep learning computer vision Computational modeling Superresolution Video sequences Video surveillance Stability analysis

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Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services

Probabilistic Selective Encryption of Convolutional Neural N...

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

作者： Tian, Jinyu Zhou, Jiantao Duan, Jia Univ Macau State Key Lab Internet Things Smart City Dept Comp & Informat Sci Taipa Macao Peoples R China JD Explore JD Gauteng South Africa

ISBN: (纸本)9781665445092

Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them. In this work, we propose a selective encryption (SE) algorithm to protect CNN models from unauthorized access, with a unique feature of providing hierarchical services to users. Our algorithm firstly selects important model parameters via the proposed Probabilistic Selection Strategy (PSS). It then encrypts the most important parameters with the designed encryption method called Distribution Preserving Random Mask (DPRM), so as to maximize the performance degradation by encrypting only a very small portion of model parameters. We also design a set of access permissions, using which different amount of most important model parameters can be decrypted. Hence, different levels of model performance can be naturally provided for users. Experimental results demonstrate that the proposed scheme could effectively protect the classification model VGG19 by merely encrypting 8% parameters of convolutional layers. We also implement the proposed model protection scheme in the denoising model DnCNN, showcasing the hierarchical denoising services.

关键词： Training Degradation computer vision Costs Computational modeling Noise reduction Probabilistic logic

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JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction

JIFF: Jointly-aligned Implicit Face Function for High Qualit...

引用

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

作者： Cao, Yukang Chen, Guanying Han, Kai Yang, Wenqi Wong, Kwan-Yee K. Univ Hong Kong Hong Kong Peoples R China CUHK Shenzhen Future Network Intelligence Inst FNii Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper addresses the problem of single view 3D human reconstruction. Recent implicit function based methods have shown impressive results, but they fail to recover fine face details in their reconstructions. This largely degrades user experience in applications like 3D telepresence. In this paper, we focus on improving the quality of face in the reconstruction and propose a novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of the implicit function based approach and model based approach. We employ a 3D morphable face model as our shape prior and compute space-aligned 3D features that capture detailed face geometry information. Such space-aligned 3D features are combined with pixel-aligned 2D features to jointly predict an implicit face function for high quality face reconstruction. We further extend our pipeline and introduce a coarse-to-fine architecture to predict high quality texture for our detailed face model. Extensive evaluations have been carried out on public datasets and our proposed JIFF has demonstrates superior performance (both quantitatively and qualitatively) over existing state-of-the-arts.

关键词： Solid modeling Three-dimensional displays Telepresence Shape Face recognition Computational modeling computer architecture

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Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection 32

Graph Convolutional Label Noise Cleaner: Train a Plug-and-pl...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhong, Jia-Xing Li, Nannan Kong, Weijie Liu, Shan Li, Thomas H. Li, Ge Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China Inst Intelligent Video Audio Technol Longgang Shenzhen Peoples R China Tencent Amer Palo Alto CA USA

ISBN: (纸本)9781728132938

Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly apply fully supervised action classifiers to weakly supervised anomaly detection, and take maximum advantage of these well-developed classifiers. For this purpose, we devise a graph convolutional network to correct noisy labels. Based upon feature similarity and temporal consistency, our network propagates supervisory signals from high-confidence snippets to low-confidence ones. In this manner, the network is capable of providing cleaned supervision for action classifiers. During the test phase, we only need to obtain snippet-wise predictions from the action classifier without any extra post-processing. Extensive experiments on 3 datasets at different scales with 2 types of action classifiers demonstrate the efficacy of our method. Remarkably, we obtain the frame-level AUC score of 82.12% on UCF-Crime.

关键词： Action recognition Video Analytics vision Applications and Systems

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Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection

Target-aware Dual Adversarial Learning and a Multi-scenario ...

引用

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

作者： Liu, Jinyuan Fan, Xin Huang, Zhanbo Wu, Guanyao Liu, Risheng Zhong, Wei Luo, Zhongxuan Dalian Univ Technol Sch Software Technol Dalian Peoples R China Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Aiming at generating an image of high visual quality, previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. These approaches neglect that modality differences implying the complementary information are extremely important for both fusion and subsequent detection task. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network. The fusion network with one generator and dual discriminators seeks commons while learning from differences, which preserves structural information of targets from the infrared and textural details from the visible. Furthermore, we build a synchronized imaging system with calibrated infrared and optical sensors, and collect currently the most comprehensive benchmark covering a wide range of scenarios. Extensive experiments on several public datasets and our benchmark demonstrate that our method outputs not only visually appealing fusion but also higher detection mAP than the state-of-the-art approaches. The source code and benchmark are available at https://***/dlut-dimt/TarDAL.

关键词： Visualization Fuses Object detection Benchmark testing Adversarial machine learning Sensor systems pattern recognition

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StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis

StEP: Style-based Encoder Pre-training for Multi-modal Image...

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

作者： Meshry, Moustafa Ren, Yixuan Davis, Larry S. Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665445092

We propose a novel approach for multi-modal Image-to-image (I2I) translation. To tackle the one-to-many relationship between input and output domains, previous works use complex training objectives to learn a latent embedding, jointly with the generator, that models the variability of the output domain. In contrast, we directly model the style variability of images, independent of the image synthesis task. Specifically, we pre-train a generic style encoder using a novel proxy task to learn an embedding of images, from arbitrary domains, into a low-dimensional style latent space. The learned latent space introduces several advantages over previous traditional approaches to multi-modal I2I translation. First, it is not dependent on the target dataset, and generalizes well across multiple domains. Second, it learns a more powerful and expressive latent space, which improves the fidelity of style capture and transfer. The proposed style pre-training also simplifies the training objective and speeds up the training significantly. Furthermore, we provide a detailed study of the contribution of different loss terms to the task of multi-modal I2I translation, and propose a simple alternative to VAEs to enable sampling from unconstrained latent spaces. Finally, we achieve state-of-the-art results on six challenging benchmarks with a simple training objective that includes only a GAN loss and a reconstruction loss.

关键词： Training computer vision Codes Image synthesis Benchmark testing Generative adversarial networks Generators

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