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Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery 32

Privacy Protection in Street-View Panoramas using Depth and ...

引用

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

作者： Uittenbogaard, Ries Sebastian, Clint Vijverberg, Julien Boom, Bas Gavrila, Dariu M. de With, Peter H. N. Delft Univ Technol Intelligent Vehicles Grp Delft Netherlands TU Eindhoven VCA Grp Eindhoven Netherlands Cyclomedia BV Waardenburg Netherlands

ISBN: (纸本)9781728132938

The current paradigm in privacy protection in street-view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurring, which automatically removes and inpaints moving objects (e.g. pedestrians, vehicles) in street-view imagery. We propose a novel moving object segmentation algorithm exploiting consistencies in depth across multiple street-view images that are later combined with the results of a segmentation network. The detected moving objects are removed and inpainted with information from other views, to obtain a realistic output image such that the moving object is not visible anymore. We evaluate our results on a dataset of 1000 images to obtain a peak noise-to-signal ratio (PSNR) and L1 loss of 27.2dB and 2.5%, respectively. To assess overall quality, we also report the results of a survey conducted on 35 professionals, asked to visually inspect the images whether object removal and inpainting had taken place. The inpainting dataset will be made publicly available for scientific benchmarking purposes at https://***/.

关键词： Others vision Applications and Systems

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REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory

REVEAL: Retrieval-Augmented Visual-Language Pre-Training wit...

引用

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

作者： Hu, Ziniu Iscen, Ahmet Sun, Chen Wang, Zirui Chang, Kai-Wei Sun, Yizhou Schmid, Cordelia Ross, David A. Fathi, Alireza Univ Calif Los Angeles Los Angeles CA 90024 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc.) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning. The project page of this work is ***.

关键词： and reasoning language vision

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Learning Steerable Function for Efficient Image Resampling

Learning Steerable Function for Efficient Image Resampling

引用

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

作者： Li, Jiacheng Chen, Chang Huang, Wei Lang, Zhiqiang Song, Fenglong Yan, Youliang Xiong, Zhiwei Univ Sci & Technol China Chengdu Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for x2 upsampling on Manga109.

关键词： Low-level vision

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Unifying Motion Deblurring and Frame Interpolation with Events

Unifying Motion Deblurring and Frame Interpolation with Even...

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

作者： Zhang, Xiang Yu, Lei Wuhan Univ Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Slow shutter speed and long exposure time of frame-based cameras often cause visual blur and loss of inter-frame information, degenerating the overall quality of captured videos. To this end, we present a unified framework of event-based motion deblurring and frame interpolation for blurry video enhancement, where the extremely low latency of events is leveraged to alleviate motion blur and facilitate intermediate frame prediction. Specifically, the mapping relation between blurry frames and sharp latent images is first predicted by a learnable double integral network, and a fusion network is then proposed to refine the coarse results via utilizing the information from consecutive blurry inputs and the concurrent events. By exploring the mutual constraints among blurry frames, latent images, and event streams, we further propose a self-supervised learning framework to enable network training with real-world blurry videos and events. Extensive experiments demonstrate that our method compares favorably against the state-of-the-art approaches and achieves remarkable performance on both synthetic and real-world datasets. Codes are available at https://***/XiangZ-0/EVDI.

关键词： Training Interpolation Visualization computer vision Codes Self-supervised learning Streaming media

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Turn a Silicon Camera into an InGaAs Camera 32

Turn a Silicon Camera into an InGaAs Camera

引用

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

作者： Lu, Feifan Zheng, Yinqiang Zhang, Bohan Lu, Feng Beihang Univ Sch CSE State Key Lab VR Technol & Syst Beijing Peoples R China Natl Inst Informat Tokyo Japan Peng Cheng Lab Shenzhen Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing Peoples R China

ISBN: (纸本)9781728132938

Short-wave infrared (SWIR) imaging has a wide range of applications for both industry and civilian. However, the InGaAs sensors commonly used for SWIR imaging suffer from a variety of drawbacks, including high price, low resolution, unstable quality, and so on. In this paper, we propose a novel solution for SWIR imaging using a common Silicon sensor, which has cheaper price, higher resolution and better technical maturity compared with the specialized InGaAs sensor. Our key idea is to approximate the response of the InGaAs sensor by exploiting the largely ignored sensitivity of a Silicon sensor, weak as it is, in the SWIR range. To this end, we build a multi-channel optical system to collect a new SWIR dataset and present a physically meaningful three-stage image processing algorithm on the basis of CNN. Both qualitative and quantitative experiments show promising experimental results, which demonstrate the effectiveness of the proposed method.

关键词： Image and Video Synthesis Low-level vision

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Towards 3D vision with Low-Cost Single-Photon Cameras

Towards 3D Vision with Low-Cost Single-Photon Cameras

引用

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

作者： Mu, Fangzhou Sifferman, Carter Jungerman, Sacha Li, Yiquan Han, Mark Gleicher, Michael Gupta, Mohit Li, Yin Univ Wisconsin Madison WI 53706 USA

ISBN: (纸本)9798350353013;9798350353006

We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras, operating as time resolved image sensors, illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process, account for its non-idealities, and adapt neural rendering to reconstruct 3D geometry from a set of spatially distributed sensors with known poses. We show that our approach can successfully recover complex 3D shapes from simulated data. We further demonstrate 3D object reconstruction from real-world captures, utilizing measurements from a commodity proximity sensor. Our work draws a connection between image-based modeling and active range scanning, and offers a step towards 3D vision with single-photon cameras. Our project webpage is at https://***/ towards_3d_vision/.

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Learning Structured Gaussians to Approximate Deep Ensembles

Learning Structured Gaussians to Approximate Deep Ensembles

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

作者： Simpson, Ivor J. A. Vicente, Sara Campbell, Neill D. F. Univ Sussex Brighton E Sussex England Niantic London England Univ Bath Bath Avon England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper proposes using a sparse-structured multivariate Gaussian to provide a closed-form approximator for the output of probabilistic ensemble models used for dense image prediction tasks. This is achieved through a convolutional neural network that predicts the mean and covariance of the distribution, where the inverse covariance is parameterised by a sparsely structured Cholesky matrix. Similarly to distillation approaches, our single network is trained to maximise the probability of samples from pretrained probabilistic models, in this work we use a fixed ensemble of networks. Once trained, our compact representation can be used to efficiently draw spatially correlated samples from the approximated output distribution. Importantly, this approach captures the uncertainty and structured correlations in the predictions explicitly in a formal distribution, rather than implicitly through sampling alone. This allows direct introspection of the model, enabling visualisation of the learned structure. Moreover, this formulation provides two further benefits: estimation of a sample probability, and the introduction of arbitrary spatial conditioning at test time. We demonstrate the merits of our approach on monocular depth estimation and show that the advantages of our approach are obtained with comparable quantitative performance.

关键词： Solid modeling computer vision Uncertainty Correlation Computational modeling Estimation Predictive models

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Style Normalization and Restitution for Generalizable Person Re-identification

Style Normalization and Restitution for Generalizable Person...

引用

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

作者： Jin, Xin Lan, Cuiling Zeng, Wenjun Chen, Zhibo Zhang, Li Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781728171685

Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in filtering out identity-irrelevant interference and learning domain-invariant person representations. In this paper, we aim to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains. To achieve this goal, we propose a simple yet effective Style Normalization and Restitution (SNR) module. Specifically, we filter out style variations (e.g., illumination, color contrast) by Instance Normalization (IN). However, such a process inevitably removes discriminative information. We propose to distill identity-relevant feature from the removed information and restitute it to the network to ensure high discrimination. For better disentanglement, we enforce a dual causality loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features. Extensive experiments demonstrate the strong generalization capability of our framework. Our models empowered by the SNR modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used person ReID benchmarks, and also show superiority on unsupervised domain adaptation.

关键词： computer vision

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Exploit Visual Dependency Relations for Semantic Segmentation

Exploit Visual Dependency Relations for Semantic Segmentatio...

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

作者： Liu, Mingyuan Schonfeld, Dan Tang, Wei Univ Illinois Chicago IL 60607 USA

ISBN: (纸本)9781665445092

Dependency relations among visual entities are ubiquity because both objects and scenes are highly structured. They provide prior knowledge about the real world that can help improve the generalization ability of deep learning approaches. Different from contextual reasoning which focuses on feature aggregation in the spatial domain, visual dependency reasoning explicitly models the dependency relations among visual entities. In this paper, we introduce a novel network architecture, termed the dependency network or DependencyNet, for semantic segmentation. It unifies dependency reasoning at three semantic levels. Intra-class reasoning decouples the representations of different object categories and updates them separately based on the internal object structures. Inter-class reasoning then performs spatial and semantic reasoning based on the dependency relations among different object categories. We will have an in-depth investigation on how to discover the dependency graph from the training annotations. Global dependency reasoning further refines the representations of each object category based on the global scene information. Extensive ablative studies with a controlled model size and the same network depth show that each individual dependency reasoning component benefits semantic segmentation and they together significantly improve the base network. Experimental results on two benchmark datasets show the DependencyNet achieves comparable performance to the recent states of the art.

关键词： Training Deep learning Visualization computer vision Semantics Network architecture Cognition

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Learning Parallax Attention for Stereo Image Super-Resolution 32

Learning Parallax Attention for Stereo Image Super-Resolutio...

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

作者： Wang, Longguang Wang, Yingqian Liang, Zhengfa Lin, Zaiping Yang, Jungang An, Wei Guo, Yulan Natl Univ Def Technol Coll Elect Sci & Technol Changsha Hunan Peoples R China Natl Key Lab Sci & Technol Blind Signal Proc Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728132938

Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to incorporate this information for SR since disparities between stereo images vary significantly. In this paper, we propose a parallax-attention stereo super-resolution network (PASSRnet) to integrate the information from a stereo image pair for SR. Specifically, we introduce a parallax-attention mechanism with a global receptive field along the epipolar line to handle different stereo images with large disparity variations. We also propose a new and the largest dataset for stereo image SR (namely, FlickrI024). Extensive experiments demonstrate that the parallax-attention mechanism can capture correspondence between stereo images to improve SR performance with a small computational and memory cost. Comparative results show that our PASSRnet achieves the state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 datasets.

关键词： Computational Photography Deep Learning Low-level vision

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