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Look, Listen, and Attack: Backdoor Attacks Against Video Action recognition

Look, Listen, and Attack: Backdoor Attacks Against Video Act...

引用

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

作者： Hasan Abed Al Kader Hammoud Shuming Liu Mohammed Alkhrashi Fahad AlBalawi Bernard Ghanem KAUST SDAIA Taif University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean test images, yet persistently predicts an attacker-defined label for any sample in the presence of the backdoor trigger. Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain. In this work, we revisit the traditional backdoor threat model and incorporate additional video-related aspects to that model. We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically, leading to highly effective attacks in the video domain. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models, where we show that attacking a single modality is enough for achieving a high attack success rate.

关键词： Threat modeling computer vision Accuracy conferences Artificial neural networks pattern recognition

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vision DiffMask: Faithful Interpretation of vision Transformers with Differentiable Patch Masking

Vision DiffMask: Faithful Interpretation of Vision Transform...

引用

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

作者： Angelos Nalmpantis Apostolos Panagiotopoulos John Gkountouras Konstantinos Papakostas Wilker Aziz University of Amsterdam

The lack of interpretability of the vision Transformer may hinder its use in critical real-world applications despite its effectiveness. To overcome this issue, we propose a post-hoc interpretability method called vision DiffMask, which uses the activations of the model’s hidden layers to predict the relevant parts of the input that contribute to its final predictions. Our approach uses a gating mechanism to identify the minimal subset of the original input that preserves the predicted distribution over classes. We demonstrate the faithfulness of our method, by introducing a faithfulness task, and comparing it to other state-of-the-art attribution methods on CIFAR-10 and ImageNet-1K, achieving compelling results. To aid reproducibility and further extension of our work, we open source our implementation here.

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Contrastive Learning for Depth Prediction

Contrastive Learning for Depth Prediction

引用

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

作者： Rizhao Fan Matteo Poggi Stefano Mattoccia Department of Computer Science and Engineering University of Bologna Italy

Depth prediction is at the core of several computer vision applications, such as autonomous driving and robotics. It is often formulated as a regression task in which depth values are estimated through network layers. Unfortunately, the distribution of values on depth maps is seldom explored. Therefore, this paper proposes a novel framework combining contrastive learning and depth prediction, allowing us to pay more attention to depth distribution and consequently enabling improvements to the overall estimation process. Purposely, we propose a window-based contrastive learning module, which partitions the feature maps into non-overlapping windows and constructs contrastive loss within each one. Forming and sorting positive and negative pairs, then enlarging the gap between the two in the representation space, constraints depth distribution to fit the feature of the depth map. Experiments on KITTI and NYU datasets demonstrate the effectiveness of our framework.

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Collaborative Blind Image Deblurring

Collaborative Blind Image Deblurring

引用

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

作者： Thomas Eboli Jean-Michel Morel Gabriele Facciolo CNRS ENS Paris-Saclay Centre Borelli Université Paris-Saclay City University of Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks. We show that when extracting patches of similar underlying blur is possible, jointly processing the stack of patches yields superior accuracy than handling them separately. Our collaborative scheme is implemented in a neural architecture with a pooling layer on the stack dimension. We present three practical patch extraction strategies for image sharpening, camera shake removal and optical aberration correction, and validate the proposed approach on both synthetic and real-world benchmarks. For each blur instance, the proposed collaborative strategy yields significant quantitative and qualitative improvements.

关键词： Optical design conferences Neural networks Collaboration computer architecture Optical imaging Cameras

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Refining Remote Photoplethysmography Architectures using CKA and Empirical Methods

Refining Remote Photoplethysmography Architectures using CKA...

引用

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

作者： Nathan Vance Patrick Flynn University of Notre Dame

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Model architecture refinement is a challenging task in deep learning research fields such as remote photoplethysmography (rPPG). One architectural consideration, the depth of the model, can have significant consequences on the resulting performance. In rPPG models that are overprovisioned with more layers than necessary, redundancies exist, the removal of which can result in faster training and reduced computational load at inference time. With too few layers the models may exhibit sub-optimal error rates. We apply Centered Kernel Alignment (CKA) to an array of rPPG architectures of differing depths, demonstrating that shallower models do not learn the same representations as deeper models, and that after a certain depth, redundant layers are added without significantly increased functionality. An empirical study confirms how the architectural deficiencies discovered using CKA impact performance, and we show how CKA as a diagnostic can be used to refine rPPG architectures.

关键词： Training Error analysis Computational modeling Refining Redundancy computer architecture Photoplethysmography

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Enhancing 2D Representation Learning with a 3D Prior

Enhancing 2D Representation Learning with a 3D Prior

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

作者： Mehmet Aygün Prithviraj Dhar Zhicheng Yan Oisin Mac Aodha Rakesh Ranjan University of Edinburgh Meta Inc.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Learning robust and effective representations of visual data is a fundamental task in computer vision. Traditionally, this is achieved by training models with labeled data which can be expensive to obtain. Self-supervised learning attempts to circumvent the requirement for labeled data by learning representations from raw unlabeled visual data alone. However, unlike humans who obtain rich 3D information from their binocular vision and through motion, the majority of current self-supervised methods are tasked with learning from monocular 2D image collections. This is noteworthy as it has been demonstrated that shape-centric visual processing is more robust compared to texture-biased automated methods. Inspired by this, we propose a new approach for strengthening existing self-supervised methods by explicitly enforcing a strong 3D structural prior directly into the model during training. Through experiments, across a range of datasets, we demonstrate that our 3D aware representations are more robust compared to conventional self-supervised baselines.

关键词： Training Representation learning Visualization computer vision Solid modeling Three-dimensional displays Shape

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Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image

Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh...

引用

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

作者： Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Tiwari, Anil Kumar IIT Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata India IIT Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9781665448994

The Bokeh Effect is one of the most desirable effects in photography for rendering artistic and aesthetic photos. Usually, it requires a DSLR camera with different aperture and shutter settings and certain photography skills to generate this effect. In smartphones, computational methods and additional sensors are used to overcome the physical lens and sensor limitations to achieve such effect. Most of the existing methods utilized additional sensor's data or pre-trained network for fine depth estimation of the scene and sometimes use portrait segmentation pretrained network module to segment salient objects in the image. Because of these reasons, networks have many parameters, become runtime intensive and unable to run in mid-range devices. In this paper, we used an end-to-end Deep Multi-Scale Hierarchical Network (DMSHN) model for direct Bokeh effect rendering of images captured from the monocular camera. To further improve the perceptual quality of such effect, a stacked model consisting of two DMSHN modules is also proposed. Our model does not rely on any pretrained network module for Monocular Depth Estimation or Saliency Detection, thus significantly reducing the size of model and run time. Stacked DMSHN achieves state-of-the-art results on a large scale EBB! dataset with around 6x less runtime compared to the current state-of-the-art model in processing HD quality images.

关键词： Photography Image segmentation Runtime Estimation Rendering (computer graphics) Cameras Real-time systems

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Uncertainty Estimation for Tumor Prediction with Unlabeled Data

Uncertainty Estimation for Tumor Prediction with Unlabeled D...

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

作者： Juyoung Yun Shahira Abousamra Chen Li Rajarsi Gupta Tahsin Kurc Dimitris Samaras Alison Van Dyke Joel Saltz Chao Chen Department of Computer Science Stony Brook University USA Department of Biomedical Informatics Stony Brook University USA National Cancer Institute Maryland USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating uncertainty of a neural network is crucial in providing transparency and trustworthiness. In this paper, we focus on uncertainty estimation for digital pathology prediction models. To explore the large amount of unlabeled data in digital pathology, we propose to adopt novel learning method that can fully exploit unlabeled data. The proposed method achieves superior performance compared with different baselines including the celebrated Monte-Carlo Dropout. Closeup inspection of uncertain regions reveal insight into the model and improves the trustworthiness of the models.

关键词： Pathology Uncertainty Accuracy Monte Carlo methods Neural networks Estimation Predictive models

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(ASNA) An Attention-based Siamese-Difference Neural Network with Surrogate Ranking Loss function for Perceptual Image Quality Assessment

(ASNA) An Attention-based Siamese-Difference Neural Network ...

引用

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

作者： Ayyoubzadeh, Seyed Mehdi Royat, Ali McMaster Univ Hamilton ON Canada Sharif Univ Technol Tehran Iran

ISBN: (纸本)9781665448994

Recently, deep convolutional neural networks (DCNN) that leverage the adversarial training framework for image restoration and enhancement have significantly improved the processed images' sharpness. Surprisingly, although these DCNNs produced crispier images than other methods visually, they may get a lower quality score when popular measures are employed for evaluating them. Therefore it is necessary to develop a quantitative metric to reflect their performances, which is well-aligned with the perceived quality of an image. Famous quantitative metrics such as Peak signal-to-noise ratio (PSNR), The structural similarity index measure (SSIM), and Perceptual Index (PI) are not well-correlated with the mean opinion score (MOS) for an image, especially for the neural networks trained with adversarial loss functions. This paper has proposed a convolutional neural network using an extension architecture of the traditional Siamese network so-called Siamese-Difference neural network. We have equipped this architecture with the spatial and channel-wise attention mechanism to increase our method's performance. Finally, we employed an auxiliary loss function to train our model. The suggested additional cost function surrogates ranking loss to increase Spearman's rank correlation coefficient while it is differentiable concerning the neural network parameters. Our method achieved superior performance in NTIRE 2021 Perceptual Image Quality Assessment Challenge. The implementations of our proposed method are publicly available.(1 2)

关键词： Image quality Training Visualization Technological innovation PSNR Neural networks computer architecture

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Omni-Crack30k: A Benchmark for Crack Segmentation and the Reasonable Effectiveness of Transfer Learning

Omni-Crack30k: A Benchmark for Crack Segmentation and the Re...

引用

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

作者： Christian Benz Volker Rodehorst Bauhaus-Universität Weimar Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Large benchmarking datasets, such as ImageNet, COCO, Cityscapes, or ScanNet, have enormously promoted research in computer vision. For the domain of crack segmentation, no such large and well-maintained benchmark exists. Crack segmentation is characterized by the decentralized creation of datasets, almost all of which have their specific right to existence. Each dataset covers a different aspect of the surprisingly complex landscape of materials, acquisition conditions, and appearances linked to crack segmentation. The OmniCrack30k dataset forms the first large-scale, systematic, and thorough approach to provide a sustainable basis for tracking methodical progress in the field of crack segmentation. It contains 30k samples from over 20 datasets summing up to 9 billion pixels in total. Featuring materials as diverse as asphalt, ceramic, concrete, masonry, and steel, it paves the road towards universal crack segmentation, a currently under-explored topic. Experiments indicate the effectiveness of transfer learning for crack segmentation: nnU-Net achieves a mean clIoU4px of 64% outperforming all other approaches by at least 10% points.

关键词： Training computer vision Systematics Semantic segmentation Roads Transfer learning computer architecture

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