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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是2011-2020 订阅

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Message from the 2023 General and Program Chairs

Message from the 2023 General and Program Chairs

引用

conference on computer vision and pattern recognition (CVPR)

来源：评论

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MonoSelfRecon: Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views

MonoSelfRecon: Purely Self-Supervised Explicit Generalizable...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Runfa Li Upal Mahbub Vasudev Bhaskaran Truong Nguyen UC San Diego Qualcomm

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Current monocular 3D scene reconstruction (3DR) works are either fully-supervised, or not generalizable, or implicit in 3D representation. We propose a novel framework - MonoSelfRecon that for the first time achieves explicit 3D mesh reconstruction for generalizable indoor scenes with monocular RGB views by purely self-supervision on voxel-SDF (signed distance function). MonoSelfRecon follows an Autoencoder-based architecture, decodes voxel-SDF and a generalizable Neural Radiance Field (NeRF), which is used to guide voxel-SDF in self-supervision. We propose novel self-supervised losses, which not only support pure self-supervision, but can be used together with supervised signals to further boost supervised training. Our experiments show that "MonoSelfRecon" trained in pure self-supervision outperforms current best self-supervised indoor depth estimation models and is comparable to 3DR models trained in fully supervision with depth annotations. MonoSelfRecon is not restricted by specific model design, which can be used to any models with voxel-SDF for purely self-supervised manner.

关键词： Training Solid modeling computer vision Three-dimensional displays Annotations conferences Estimation

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DVMSR: Distillated vision Mamba for Efficient Super-Resolution

DVMSR: Distillated Vision Mamba for Efficient Super-Resoluti...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaoyan Lei Wenlong zhang Weifeng Cao Zhengzhou University of Light Industry The HongKong Polytechnic University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Efficient Image Super-Resolution (SR) aims to accelerate SR network inference by minimizing computational complexity and network parameters while preserving performance. Existing state-of-the-art Efficient Image Super-Resolution methods are based on convolutional neural networks. Few attempts have been made with Mamba to harness its long-range modeling capability and efficient computational complexity, which have shown impressive performance on high-level vision tasks. In this paper, we propose DVMSR, a novel lightweight Image SR network that incorporates vision Mamba and a distillation strategy. The network of DVMSR consists of three modules: feature extraction convolution, multiple stacked Residual State Space Blocks (RSSBs), and a reconstruction module. Specifically, the deep feature extraction module is composed of several residual state space blocks (RSSB), each of which has several vision Mamba Moudles(ViMM) together with a residual connection. To achieve efficiency improvement while maintaining comparable performance, we employ a distillation strategy to the vision Mamba network for superior performance. Specifically, we leverage the rich representation knowledge of teacher network as additional supervision for the output of lightweight student networks. Extensive experiments have demonstrated that our proposed DVMSR can outperform state-of-the-art efficient SR methods in terms of model parameters while maintaining the performance of both PSNR and SSIM. The source code is available at https://***/nathan66666/***

关键词： Knowledge engineering Convolution Computational modeling Source coding Superresolution Feature extraction pattern recognition

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DIA: Diffusion based Inverse Network Attack on Collaborative Inference

DIA: Diffusion based Inverse Network Attack on Collaborative...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dake Chen Shiduo Li Yuke Zhang Chenghao Li Souvik Kundu Peter A. Beerel University of Southern California Los Angeles USA Tsinghua University Beijing China Intel Labs San Diego USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With the continuous expansion of neural networks in size and depth, and the growing popularity of machine learning as a service, collaborative inference systems present a promising approach for deploying models in resource-constrained computing environments. However, as the deployment of these systems gains traction, evaluating their privacy and security has become a critical issue. Towards this goal, this paper introduces a diffusion-based inverse network attack, named DIA, for collaborative inference systems that uses a novel feature map awareness conditioning mechanism to guide the diffusion model. Compared to prior approaches, our extensive empirical results demonstrate that the proposed attack achieves an average improvement of 29%, 20%, 30% in terms of SSIM, PSNR, and MSE when applied to convolutional neural networks (CNN), 18%, 17%, 61% to ResNet models, and 55%, 54%, 84% to vision transformers (ViTs). Our results identify the significant vulnerability of ViTs and analyze the potential sources of this vulnerability. Based on our analysis, we raise caution regarding the deployment of transformer-based models in collaborative inference systems, emphasizing the need for careful consideration regarding the security of such models in collaborative settings.

关键词： computer vision Analytical models Privacy Computational modeling Neural networks Collaboration Transformers

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VMRNN: Integrating vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting

VMRNN: Integrating Vision Mamba and LSTM for Efficient and A...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yujin Tang Peijie Dong Zhenheng Tang Xiaowen Chu Junwei Liang AI Thrust The Hong Kong University of Science and Technology (Guangzhou) DSA Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Computer Science Hong Kong Baptist University Department of Computer Science and Engineering The Hong Kong University of Science and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Combining Convolutional Neural Networks (CNNs) or vision Transformers(ViTs) with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded unparalleled results in predicting temporal and spatial dynamics. However, modeling extensive global information remains a formidable challenge; CNNs are limited by their narrow receptive fields, and ViTs struggle with the intensive computational demands of their attention mechanisms. The emergence of recent Mamba-based architectures has been met with enthusiasm for their exceptional long-sequence modeling capabilities, surpassing established vision models in efficiency and accuracy, which motivates us to develop an innovative architecture tailored for spatiotemporal forecasting. In this paper, we propose the VMRNN cell, a new recurrent unit that integrates the strengths of vision Mamba blocks with LSTM. We construct a network centered on VMRNN cells to tackle spatiotemporal prediction tasks effectively. Our extensive evaluations show that our proposed approach secures competitive results on a variety of tasks while maintaining a smaller model size. Our code is available at https://***/yyyujintang/VMRNN-PyTorch.

关键词： Recurrent neural networks Accuracy Computational modeling Microprocessors computer architecture Predictive models Transformers

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One Embedding to Predict Them All: Visible and Thermal Universal Face Representations for Soft Biometric Estimation via vision Transformers

One Embedding to Predict Them All: Visible and Thermal Unive...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nelida Mirabet-Herranz Chiara Galdi Jean-Luc Dugelay Campus SophiaTech EURECOM France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Human faces encode a vast amount of information including not only uniquely distinctive features of the individual but also demographic information such as a person’s age, gender, and weight. Such information is referred to as soft-biometrics, which are physical, behavioral or adhered human characteristics, classifiable in pre–defined human compliant categories. As we often say ’one look is worth a thousand words’. vision Transformers have emerged as a powerful deep learning architecture able to achieve accurate classifications for different computer vision tasks, but these models have not been yet applied to soft-biometrics. In this work, we propose the Bidirectional Encoder Face representation from image Transformers (BEFiT), a model that leverages the multi-attention mechanisms to capture local and global features and produce a multi-purpose face embedding. This unique embedding enables the estimation of different demographics without having to re-train the model for each soft-biometric trait, ensuring high efficiency without compromising accuracy. Our approach explores the use of visible and thermal images to achieve powerful face embedding in different light spectra. We demonstrate that the BEFiT embeddings can capture essential information for gender, age, and weight estimation, surpassing the performance of dedicated deep learning structures for the estimation of a single soft biometric trait. The code of BEFiT implementation is publicly available 1

关键词： Biometrics Training computer vision Accuracy Computational modeling Estimation Transformers

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UDAC: Under-Display Array Cameras

UDAC: Under-Display Array Cameras

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Chengyu Wang Jing Li Pavan C. Madhusudanarao Jinhan Hu Jitesh K. Singh WooJhon Choi Seok-Jun Lee Hamid R. Sheikh Samsung Research America Samsung Research India-Bangalore Samsung Electronics

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Under-display cameras (UDC) provide uninterrupted display experience on smartphones, but the captured images suffer from quality degradations such as diffraction blur, and reduced signal-to-noise ratio due to the presence of the OLED panel which obstructs the camera. In this work, we propose to use under-display array cameras (UDAC) consisting of two cameras placed under the OLED display panel to further reduce image artifacts and enhance image quality. We rotate the OLED panel by 45 degrees for one of the cameras to utilize the complimentary nature of the point spread function. We also propose an algorithm to combine images from the two cameras, which includes occlusion and optical flow analysis, image warping, color match and image blending. Experiments on real UDC devices show that the proposed method is superior in reducing the artifacts and improving the signal-to-noise ratio. The proposed method is simple but effective, and it can be easily extended to various configurations and incorporated with various algorithms.

关键词： Image quality Image color analysis Pipelines Organic light emitting diodes Cameras Image restoration pattern recognition

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Contrastive Clothing and Pose Generation for Cloth-Changing Person Re-Identification

Contrastive Clothing and Pose Generation for Cloth-Changing ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vuong D. Nguyen Pranav Mantini Shishir K. Shah Dept. of Computer Science Quantitative Imaging Lab University of Houston

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Cloth-Changing Person Re-Identification (CCRe-ID) aims at matching an individual across cameras after a long period of time, presenting variations in clothing compounded with changes in pose, viewpoint, etc. In this work, we propose CCPG: Contrastive Clothing and Pose Generation framework for CCRe-ID. Beyond appearance, CCPG captures cloth-invariant body shape information using a Relational Graph Attention Network. Training a robust CCRe-ID model requires a wide range of clothing variations and expensive cloth labeling, which is lacked in current CCRe-ID datasets. To address this, we propose a GAN-based model for clothing and pose transfer across identities to augment images of more clothing variations and of different persons wearing similar clothing. The augmented batch of images serve as inputs to our proposed Fine-grained Contrastive Losses, which not only supervise the Re-ID model to learn discriminative person embeddings under long-term scenarios but also ensure in-distribution data generation. Results on CCRe-ID datasets demonstrate the effectiveness of our CCPG framework. Code will be available here.

关键词： Training computer vision Shape conferences Computational modeling Clothing Data models

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Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Surv...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marcos V. Conde Florin-Alexandru Vasluianu Radu Timofte Jianxing Zhang Jia Li Fan Wang Xiaopeng Li Zikun Liu Hyunhee Park Sejun Song Changho Kim Zhijuan Huang Hongyuan Yu Cheng Wan Wending Xiang Jiamin Lin Hang Zhong Qiaosong Zhang Yue Sun Xuanwu Yin Kunlong Zuo Senyan Xu Siyuan Jiang Zhijing Sun Jiaying Zhu Liangyan Li Ke Chen Yunzhe Li Yimo Ning Guanhua Zhao Jun Chen Jinyang Yu Kele Xu Qisheng Xu Yong Dou CAIDAS & IFI Computer Vision Lab. University of Würzburg Samsung Research China - Beijing (SRC-B) Samsung MX(Mobile eXperience) Business Xiaomi Inc. Georgia Institute of Technology University of Science and Technology of China McMaster University Computer Dept. National University of Defense Technology Changsha China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. Th goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. The performance of the top-5 sub-missions is reviewed and provided here as a gauge for the current state-of-the-art in RAW Image Super-Resolution.

关键词： Surveys Degradation computer vision Reviews conferences Superresolution Pipelines Noise pattern recognition

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Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models

Sensor Equivariance: A Framework for Semantic Segmentation w...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hannes Reichert Manuel Hetzel Andreas Hubert Konrad Doll Bernhard Sick University of Applied Sciences Aschaffenburg Germany University of Kassel Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data processing depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Furthermore, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable using projection properties in convolutional neural networks. To that end, we propose an architecture for using the SensorConv layer in the Detectron2 [21] framework. We collected a dataset of equirectangular images for our experiments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an augmentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our experiment, we show that our method can generalize better across different camera sensors.

关键词： computer vision Convolution Semantic segmentation conferences Cameras Distortion Data processing

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