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Rethinking and Improving the Robustness of Image Style Transfer

Rethinking and Improving the Robustness of Image Style Trans...

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

作者： Wang, Pei Li, Yijun Vasconcelos, Nuno UC La Jolla CA 92093 USA Adobe Res Los Altos CA USA

ISBN: (纸本)9781665445092

Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image. Surprisingly, however, this stylization quality is not robust and often degrades significantly when applied to features from more advanced and lightweight networks, such as those in the ResNet family. By performing extensive experiments with different network architectures, we find that residual connections, which represent the main architectural difference between VGG and ResNet, produce feature maps of small entropy, which are not suitable for style transfer. To improve the robustness of the ResNet architecture, we then propose a simple yet effective solution based on a softmax transformation of the feature activations that enhances their entropy. Experimental results demonstrate that this small magic can greatly improve the quality of stylization results, even for networks with random weights. This suggests that the architecture used for feature extraction is more important than the use of learned weights for the task of style transfer.

关键词： Visualization Smoothing methods Correlation computer architecture Network architecture Feature extraction Robustness

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Scattering Prompt Tuning: A Fine-tuned Foundation Model for SAR Object recognition

Scattering Prompt Tuning: A Fine-tuned Foundation Model for ...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Weilong Guo Shengyang Liv Jian Yang Key Laboratory of Space Utilization Chinese Academy of Sciences Technology and the Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic Aperture Radar (SAR) serves as a vital tool in various earth observation applications, providing robust imaging under challenging weather conditions. While the fine-tuned foundation models excel in many downstream tasks, they struggle with SAR object recognition because of SAR's unique imaging and scattering characteristics. In this study, we propose a novel approach named Scattering Prompt Tuning (SPT) based vision foundation model. It uses SAR image scattering information as a prompt and integrates learnable parameters into the pre-trained model's input space to help learn SAR's unique information. We also employ a lightweight Residual AdapterMLP for fine-tuning, design a Sequential Feature Aggregation (SFA) to selectively fuse features from different transformer blocks effectively, and develop a Dynamic Distributional Contrast loss (DCLoss) to maintain the proper distance between different objects in feature space. Additionally, a four-stage training strategy, incorporating semi-supervised learning, is deployed to enhance SAR object recognition performance further Our approach reaches a Top-1 accuracy of 37.9% and an AUROC of 0.83 on the final dataset, winning the first place in the SAR Classification track of PBVS 2024 Multi-modal Aerial View Object Classification Challenge, which is better than the latest advanced fine-tuned foundation models.

关键词： Training Scattering Imaging Semisupervised learning Radar imaging Radar polarimetry pattern recognition

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DIA: Diffusion based Inverse Network Attack on Collaborative Inference

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

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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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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 ieee/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 ieee.

关键词： Image enhancement

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Mixed-Privacy Forgetting in Deep Networks

Mixed-Privacy Forgetting in Deep Networks

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

作者： Golatkar, Aditya Achille, Alessandro Ravichandran, Avinash Polito, Marzia Soatto, Stefano Amazon Web Serv Seattle WA 98109 USA Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781665445092

We show that the influence of a subset of the training samples can be removed - or "forgotten" - from the weights of a network trained on large-scale image classification tasks, and we provide strong computable bounds on the amount of remaining information after forgetting. Inspired by real-world applications of forgetting techniques, we introduce a novel notion of forgetting in mixed-privacy setting, where we know that a "core" subset of the training samples does not need to be forgotten. While this variation of the problem is conceptually simple, we show that working in this setting significantly improves the accuracy and guarantees of forgetting methods applied to vision classification tasks. Moreover, our method allows efficient removal of all information contained in non-core data by simply setting to zero a subset of the weights with minimal loss in performance. We achieve these results by replacing a standard deep network with a suitable linear approximation. With opportune changes to the network architecture and training procedure, we show that such linear approximation achieves comparable performance to the original network and that the forgetting problem becomes quadratic and can be solved efficiently even for large models. Unlike previous forgetting methods on deep networks, ours can achieve close to the state-of-the-art accuracy on large scale vision tasks. In particular, we show that our method allows forgetting without having to trade off the model accuracy.

关键词： Training computer vision Linear approximation Network architecture Data models pattern recognition Task analysis

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Pulsar: Efficient Sphere-based Neural Rendering

Pulsar: Efficient Sphere-based Neural Rendering

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

作者： Lassner, Christoph Zollhofer, Michael Facebook Real Labs Redmond WA 98052 USA

ISBN: (纸本)9781665445092

We propose Pulsar, an efficient sphere-based differentiable rendering module that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from image observations. However, gradient-based optimization of neural mesh, voxel, or function representations suffers from multiple challenges, i.e., topological inconsistencies, high memory footprints, or slow rendering speeds. To alleviate these problems, Pulsar employs: 1) a sphere-based scene representation, 2) a modular, efficient differentiable projection operation, and 3) (optional) neural shading. Pulsar executes orders of magnitude faster than existing techniques and allows real-time rendering and optimization of representations with millions of spheres. Using spheres for the scene representation, unprecedented speed is obtained while avoiding topology problems. Pulsar is fully differentiable and thus enables a plethora of applications, ranging from 3D reconstruction to neural rendering.

关键词： Training Three-dimensional displays Scalability Rendering (computer graphics) Real-time systems Distance measurement Topology

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CLCC: Contrastive Learning for Color Constancy

CLCC: Contrastive Learning for Color Constancy

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

作者： Yi-Chen Lo Chia-Che Chang Hsuan-Chao Chiu Yu-Hao Huang Chia-Ping Chen Yu-Lin Chang Jou, Kevin MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9781665445092

In this paper;we present CLCC, a novel contrastive learning framework for color constancy. Contrastive learning has been applied for learning high-quality visual representations for image classification. One key aspect to yield useful representations for image classification is to design illuminant invariant augmentations. However, the illuminant invariant assumption conflicts with the nature of the color constancy task, which aims to estimate the illuminant given a raw image. Therefore, we construct effective contrastive pairs for learning better illuminant-dependent features via a novel raw-domain color augmentation. On the NUS-8 dataset, our method provides 17.5% relative improvements over a strong baseline, reaching state-of-the-art performance without increasing model complexity. Furthermore, our method achieves competitive performance on the Gehler dataset with 3x fewer parameters compared to top-ranking deep learning methods. More importantly, we show that our model is more robust to different scenes under close proximity of illuminants, significantly reducing 28.7% worstcase error in data-sparse regions. Our code is available at https://github. coin/ howardyclo/cicc-cvpr21.

关键词： Deep learning Visualization computer vision Image color analysis Computational modeling Data models pattern recognition

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Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts

Exploring Data-Efficient 3D Scene Understanding with Contras...

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

作者： Hou, Ji Graham, Benjamin Niesner, Matthias Xie, Saining Tech Univ Munich Munich Germany Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

The rapid progress in 3D scene understanding has come with growing demand for data;however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited;even given sufficient data, acquiring 3D labels (e.g. instance masks) requires intensive human labor. In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both point-level correspondences and spatial contexts in a scene. Our method achieves state-of-the-art results on a suite of benchmarks where training data or labels are scarce. Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary;and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations.

关键词： computer vision Three-dimensional displays Annotations Semantics Training data Benchmark testing pattern recognition

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Enhancing the Transferability of Adversarial Attacks through Variance Tuning

Enhancing the Transferability of Adversarial Attacks through...

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

作者： Wang, Xiaosen He, Kun Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781665445092

Deep neural networks are vulnerable to adversarial examples that mislead the models with imperceptible perturbations. Though adversarial attacks have achieved incredible success rates in the white-box setting, most existing adversaries often exhibit weak transferability in the black-box setting, especially under the scenario of attacking models with defense mechanisms. In this work, we propose a new method called variance tuning to enhance the class of iterative gradient based attack methods and improve their attack transferability. Specifically, at each iteration for the gradient calculation, instead of directly using the current gradient for the momentum accumulation, we further consider the gradient variance of the previous iteration to tune the current gradient so as to stabilize the update direction and escape from poor local optima. Empirical results on the standard ImageNet dataset demonstrate that our method could significantly improve the transferability of gradient-based adversarial attacks. Besides, our method could be used to attack ensemble models or be integrated with various input transformations. Incorporating variance tuning with input transformations on iterative gradient-based attacks in the multi-model setting, the integrated method could achieve an average success rate of 90.1% against nine advanced defense methods, improving the current best attack performance significantly by 85.1%.

关键词： Deep learning computer vision Codes Perturbation methods Computational modeling pattern recognition Iterative methods

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ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References

ChallenCap: Monocular 3D Capture of Challenging Human Perfor...

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

作者： He, Yannan Pang, Anqi Chen, Xin Liang, Han Wu, Minye Ma, Yuexin Xu, Lan ShanghaiTech Univ Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9781665445092

Capturing challenging human motions is critical for numerous applications, but it suffers from complex motion patterns and severe self-occlusion under the monocular setting. In this paper, we propose ChallenCap - a template-based approach to capture challenging 3D human motions using a single RGB camera in a novel learning-and-optimization framework, with the aid of multi-modal references. We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner. We further adopt a robust motion optimization stage to increase the tracking accuracy, by jointly utilizing the learned motion details from the supervised multi-modal references as well as the reliable motion hints from the input image reference. Extensive experiments on our new challenging motion dataset demonstrate the effectiveness and robustness of our approach to capture challenging human motions.

关键词： computer vision Three-dimensional displays Tracking Cameras Robustness Hybrid power systems pattern recognition

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