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Guided Deep Metric Learning

Guided Deep Metric Learning

引用

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

作者： Jorge Gonzalez-Zapata Ivá n Reyes-Amezcua Daniel Flores-Araiza Mauricio Mendez-Ruiz Gilberto Ochoa-Ruiz Andres Mendez-Vazquez CINVESTAV Unidad Guadalajara Mexico Tecnol&#x00F3 gico de Monterrey School of Engineering and Sciences Mexico

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficulty of the dataset caused by a distributional shift in the data. These represent a significant drawback when attempting to learn the underlying data manifold. Therefore, there is a pressing need to develop better ways of obtaining generalization and representation of the underlying manifold. In this paper, we propose a novel approach to DML that we call Guided Deep Metric Learning, a novel architecture oriented to learning more compact clusters, improving generalization under distributional shifts in DML. This novel architecture consists of two independent models: A multi-branch master model, inspired from a Few-Shot Learning (FSL) perspective, generates a reduced hypothesis space based on prior knowledge from labeled data, which guides or regularizes the decision boundary of a student model during training under an offline knowledge distillation scheme. Experiments have shown that the proposed method is capable of a better manifold generalization and representation to up to 40% improvement (Recall@1, CIFAR10), using guidelines suggested by Musgrave et al. to perform a more fair and realistic comparison, which is currently absent in the literature.

关键词： Measurement Manifolds Training Analytical models Adaptation models Visualization computer architecture

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Evaluating Scalable Bayesian Deep Learning Methods for Robust computer vision

Evaluating Scalable Bayesian Deep Learning Methods for Robus...

引用

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

作者： Gustafsson, Fredrik K. Danelljan, Martin Schoen, Thomas B. Uppsala Univ Dept Informat Technol Uppsala Sweden Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781728193601

While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive uncertainty is commonly decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a neural network output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real-world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. Our proposed framework is specifically designed to test the robustness required in real-world computer vision applications. We also apply this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https://***/fregu856/evaluating_bdl.

关键词： computer vision

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Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Domain Agnostic Feature Learning for Image and Video Based F...

引用

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

作者： Saha, Suman Xu, Wenhao Kanakis, Menelaos Georgoulis, Stamatios Chen, Yuhua Paudel, Danda Pani Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this direction for biometric user authentication, and in particular face recognition, that tries to prevent spoof attacks. The state-of-the-art anti-spoofing techniques leverage the ability of deep neural networks to learn discriminative features, based on cues from the training set images or video samples, in an effort to detect spoof attacks. However, due to the particular nature of the problem, i.e. large variability due to factors like different backgrounds, lighting conditions, camera resolutions, spoof materials, etc., these techniques typically fail to generalize to new samples. In this paper, we explicitly tackle this problem and propose a class-conditional domain discriminator module, that, coupled with a gradient reversal layer, tries to generate live and spoof features that are discriminative, but at the same time robust against the aforementioned variability factors. Extensive experimental analysis shows the effectiveness of the proposed method over existing image- and video-based anti-spoofing techniques, both in terms of numerical improvement as well as when visualizing the learned features.

关键词： Face Lighting computer vision Authentication Feature extraction Cameras Visualization

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Joint Learning of Blind Video Denoising and Optical Flow Estimation

Joint Learning of Blind Video Denoising and Optical Flow Est...

引用

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

作者： Yu, Songhyun Park, Bumjun Park, Junwoo Jeong, Jechang Hanyang Univ Seoul South Korea Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Many deep-learning-based image/video denoising models have been developed, and recently, several approaches for training a denoising neural network without using clean images have been proposed. However, Noise2Noise method requires paired noisy data, and obtaining them is occasionally difficult, whereas other existing models trained using unpaired noisy data deliver limited performance. Obtaining an accurate optical flow from noisy videos is also a difficult task because conventional optical flow estimation methods are primarily focused on estimating the optical flow using clean videos. This study proposes a new framework to fine-tune video denoising and optical flow estimation networks using unpaired noisy videos. These two networks are jointly trained to realize synergy;an improvement in the denoising performance increases the accuracy of the flow estimation, and an improvement in the flow-estimation performance enhances the quality of the training data for the denoiser. Our experimental results reveal that proposed approach outperforms the existing training schemes in video denoising and also provides accurate optical flows even when the videos contain a considerable amount of noise.

关键词： Videos Noise reduction Optical flow Noise measurement Estimation Training computer vision

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Densely Self-guided Wavelet Network for Image Denoising

Densely Self-guided Wavelet Network for Image Denoising

引用

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

作者： Liu, Wei Yan, Qiong Zhao, Yuzhi SenseTime Res Hong Kong Peoples R China Harbin Inst Technol Harbin Heilongjiang Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

During the past years, deep convolutional neural networks have achieved impressive success in image denoising. In this paper, we propose a densely self-guided wavelet network (DSWN) for real-world image denoising. The basic structure of DSWN is a top-down self-guidance architecture which is able to efficiently incorporate multi- scale information and extract good local features to recover clean images. Moreover, such a structure requires a smaller number of parameters and enables us to achieve better effectiveness than Unet structure. To avoid information loss and achieve a better receptive field size, we embed wavelet transform into DSWN. In addition, we apply densely residual learning to convolution blocks to enhance the feature extraction capability of the proposed network. At the full resolution level of DSWN, we adopt a double branch structure to generate the final output. One branch of them tends to pay attention to dark areas and the other performs better on bright areas. Such a double branch strategy is able to handle the noise at different exposures. The proposed network is validated by BSD68, Kodak24 and SIDD+ benchmark. Additional experimental results show that the proposed network outperforms most state-of-the-art image denoising solutions.

关键词： computer vision pattern recognition conferences

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Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches

Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches

引用

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

作者： Agarwal, Shruti Farid, Hany Fried, Ohad Agrawala, Maneesh Univ Calif Berkeley Berkeley CA 94720 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781728193601

Recent advances in machine learning and computer graphics have made it easier to convincingly manipulate video and audio. These so-called deep-fake videos range from complete full-face synthesis and replacement (face-swap), to complete mouth and audio synthesis and replacement (lip-sync), and partial word-based audio and mouth synthesis and replacement. Detection of deep fakes with only a small spatial and temporal manipulation is particularly challenging. We describe a technique to detect such manipulated videos by exploiting the fact that the dynamics of the mouth shape - visemes - are occasionally inconsistent with a spoken phoneme. We focus on the visemes associated with words having the sound M (mama), B (baba), or P (papa) in which the mouth must completely close in order to pronounce these phonemes. We observe that this is not the case in many deep-fake videos. Such phonemeviseme mismatches can, therefore, be used to detect even spatially small and temporally localized manipulations. We demonstrate the efficacy and robustness of this approach to detect different types of deep-fake videos, including in-the-wild deep fakes.

关键词： computer graphics

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DNDNet: Reconfiguring CNN for Adversarial Robustness

DNDNet: Reconfiguring CNN for Adversarial Robustness

引用

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

作者： Goel, Akhil Agarwal, Akshay Vatsa, Mayank Singh, Richa Ratha, Nalini K. IIIT Delhi Delhi India IIT Jodhpur Jodhpur Rajasthan India IBM TJ Watson Res Ctr Yorktown Hts NY USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Several successful adversarial attacks have demonstrated the vulnerabilities of deep learning algorithms. These attacks are detrimental in building deep learning based dependable AI applications. Therefore, it is imperative to build a defense mechanism to protect the integrity of deep learning models. In this paper, we present a novel "defense layer" in a network which aims to block the generation of adversarial noise and prevents an adversarial attack in black-box and gray-box settings. The parameter-free defense layer, when applied to any convolutional network, helps in achieving protection against attacks such as FGSM, L2, Elastic-Net, and DeepFool. Experiments are performed with different CNN architectures, including VGG, ResNet, and DenseNet, on three databases, namely, MNIST, CIFAR-10, and PaSC. The results showcase the efficacy of the proposed defense layer without adding any computational overhead. For example, on the CIFAR-10 database, while the attack can reduce the accuracy of the ResNet-50 model to as low as 6.3%, the proposed "defense layer" retains the original accuracy of 81.32%.

关键词： Mathematical model Perturbation methods Machine learning computer architecture Robustness Computational modeling Databases

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Bilinear Parameterization For Differentiable Rank-Regularization

Bilinear Parameterization For Differentiable Rank-Regulariza...

引用

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

作者： Ornhag, Marcus Valtonen Olsson, Carl Heyden, Anders Lund Univ Ctr Math Sci Lund Sweden Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden

ISBN: (纸本)9781728193601

Low rank approximation is a commonly occurring problem in many computer vision and machine learning applications. There are two common ways of optimizing the resulting models. Either the set of matrices with a given rank can be explicitly parametrized using a bilinear factorization, or low rank can be implicitly enforced using regularization terms penalizing non-zero singular values. While the former approach results in differentiable problems that can be efficiently optimized using local quadratic approximation, the latter is typically not differentiable (sometimes even discontinuous) and requires first order subgradient or splitting methods. It is well known that gradient based methods exhibit slow convergence for ill-conditioned problems. In this paper we show how many non-differentiable regularization methods can be reformulated into smooth objectives using bilinear parameterization. This allows us to use standard second order methods, such as Levenberg-Marquardt (LM) and Variable Projection (VarPro), to achieve accurate solutions for ill-conditioned cases. We show on several real and synthetic experiments that our second order formulation converges to substantially more accurate solutions than competing state-of-the-art methods.(1)

关键词： computer vision

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Tracked-Vehicle Retrieval by Natural Language Descriptions With Domain Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions W...

引用

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

作者： Huy Dinh-Anh Le Quang Qui-Vinh Nguyen Vuong Ai Nguyen Thong Duy-Minh Nguyen Nhat Minh Chung Tin-Trung Thá i Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper introduces our solution for Track 2 in AI City Challenge 2022. The task is Tracked-Vehicle Retrieval by Natural Language Descriptions with a real-world dataset of various scenarios and cameras. We mainly focus on developing a robust natural language-based vehicle retrieval system to address the domain bias problem due to unseen scenarios and multi-view multi-camera vehicle tracks. Specifically, we apply CLIP [16] to effectively extract both visual and textual representations for contrastive representation learning. Furthermore, for new scenarios in the test set, we pro-pose a novel Domain Adaptive Training method that utilizes information from labeled data and transfers it to the unseen domain by generating pseudo labels. By using this simple and effective strategy, we not only bridge the domain gap between the training set and test set, but also require less computational cost and data compared to previous top performance methods. Finally, we employ a context-sensitive post-processing method to address model’s uncertainty and eliminate the wrong retrieved vehicle track. Taking one step further, we also investigate the impact of different text formats and the number of pseudo labels data for the fine-tuning process. Our proposed method has achieved 3rd place in the AI City Challenge 2022, yielding a competitive performance of 47.73% MRR accuracy on the private test set, which verified the effectiveness of the proposed solution.

关键词： Training Representation learning Visualization Adaptive systems Uncertainty Computational modeling Urban areas

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AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Learning Model For Scene Understanding

AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Lear...

引用

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

作者： Jha, Ankit Kumar, Awanish Banerjee, Biplab Chaudhuri, Subhasis Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India Indian Inst Technol Ctr Studies Resources Engn Mumbai Maharashtra India

ISBN: (纸本)9781728193601

We tackle the problem of deep end-to-end multi-task learning (MTL) for visual scene understanding from monocular images in this paper. It is proven that learning several related tasks together helps in attaining improved performance per-task than training them independently. This is due to the fact that related tasks share important feature characteristics among themselves, which the MTL techniques can effectively explore for improved joint training. Based on this premise, we are interested in generic to specific feature extraction given the different tasks within a common framework. To this end, we propose a typical U-Net based encoder-decoder architecture called AdaMT-Net, where the densely-connected deep convolutional neural network (CNN) based feature encoder is shared among the tasks while the soft-attention based task-specific decoder modules produce the desired outcomes. One major issue in MTL is to assign the weights for the task-specific loss-terms in the final cumulative optimization function. As opposed to the manual approach, we propose a novel adaptive weight learning strategy by carefully exploring the loss-gradients per-task over the training iterations. Experimental results on the benchmark CityScapes, NYUv2, and ISPRS datasets confirm that AdaMT-Net achieves state-of-the-art performance on most of the evaluation metrics.

关键词： computer vision

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