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A Dual-stream Framework for 3D Mask Face Presentation Attack Detection 18

A Dual-stream Framework for 3D Mask Face Presentation Attack...

引用

18th ieee/CVF International conference on computer vision (ICCV)

作者： Chen, Shen Yao, Taiping Zhang, Keyue Chen, Yang Sun, Ke Ding, Shouhong Li, Jilin Huang, Feiyue Ji, Rongrong Tencent YouTu Lab Shenzhen Peoples R China Xiamen Univ Media Analyt & Comp Lab Xiamen Peoples R China

ISBN: (纸本)9781665401913

Face presentation attack detection (PAD) plays a vital role in face recognition systems. Many previous face anti-spoofing methods mainly focus on the 2D face representation attacks, which however, suffer from great performance degradation when facing high-fidelity 3D mask attacks. To address this issue, we propose a novel dual-stream framework consisting of the vanilla convolution stream and the central difference convolution stream. These two streams complement each other and learn more comprehensive features for 3D mask attacks detection. Moreover, we extend 3D PAD to a multi-classification task that contains real face, plaster attack and transparent attack, and utilize various data augmentations and label smoothing techniques to improve the generalizability on unseen attacks. The proposed method achieved the second place in the Chalearn 3D High-Fidelity Mask Face Presentation Attack Detection Challenge@ICCV2021 with a score of 3.15 (ACER).

关键词： Degradation computer vision Three-dimensional displays Smoothing methods Convolution Face recognition conferences

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CompositeTasking: Understanding Images by Spatial Composition of Tasks

CompositeTasking: Understanding Images by Spatial Compositio...

引用

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

作者： Popovic, Nikola Paudel, Danda Pani Probst, Thomas Sun, Guolei Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven ESAT PSI VISICS Leuven Belgium

ISBN: (纸本)9781665445092

We define the concept of CompositeTasking as the fusion of multiple, spatially distributed tasks, for various aspects of image understanding. Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network. To facilitate CompositeTasking, we introduce a novel task conditioning model - a single encoder-decoder network that performs multiple, spatially varying tasks at once. The proposed network takes an image and a set of pixel-wise dense task requests as inputs, and performs the requested prediction task for each pixel. Moreover, we also learn the composition of tasks that needs to be performed according to some CompositeTasking rules, which includes the decision of where to apply which task. It not only offers us a compact network for multi-tasking, but also allows for task-editing. Another strength of the proposed method is demonstrated by only having to supply sparse supervision per task. The obtained results are on par with our baselines that use dense supervision and a multi-headed multi-tasking design. The source code will be made publicly available at ***/nikola3794/composite-tasking.

关键词： computer vision Codes Multitasking pattern recognition Task analysis

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Achieving robustness in classification using optimal transport with hinge regularization

Achieving robustness in classification using optimal transpo...

引用

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

作者： Serrurier, Mathieu Mamalet, Franck Gonzalez-Sanz, Alberto Boissin, Thibaut Loubes, Jean-Michel del Barrio, Eustasio Univ Paul Sabatier Toulouse France IRT St Exupery Toulouse France Univ Valladolid Valladolid Spain

ISBN: (纸本)9781665445092

Adversarial examples have pointed out Deep Neural Network's vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustness, but make them harder to learn with classical loss functions. We propose a new framework for binary classification, based on optimal transport, which integrates this Lipschitz constraint as a theoretical requirement. We propose to learn 1-Lipschitz networks using a new loss that is an hinge regularized version of the Kantorovich-Rubinstein dual formulation for the Wasserstein distance estimation. This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound. We also prove that this hinge regularized version is still the dual formulation of an optimal transportation problem, and has a solution. We also establish several geometrical properties of this optimal solution, and extend the approach to multi-class problems. Experiments show that the proposed approach provides the expected guarantees in terms of robustness without any significant accuracy drop. The adversarial examples, on the proposed models, visibly and meaningfully change the input providing an explanation for the classification.

关键词： computer vision Computational modeling Transportation Estimation Fasteners Robustness pattern recognition

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No frame left behind: Full Video Action recognition

No frame left behind: Full Video Action Recognition

引用

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

作者： Liu, Xin Pintea, Silvia L. Nejadasl, Fatemeh Karimi Booij, Olaf van Gemert, Jan C. Delft Univ Technol Comp Vis Lab Delft Netherlands TomTom Amsterdam Netherlands

ISBN: (纸本)9781665445092

Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundreds of frames. A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action. Instead, here we propose full video action recognition and consider all video frames. To make this computational tractable, we first cluster all frame activations along the temporal dimension based on their similarity with respect to the classification task, and then temporally aggregate the frames in the clusters into a smaller number of representations. Our method is end-to-end trainable and computationally efficient as it relies on temporally localized clustering in combination with fast Hamming distances in feature space. We evaluate on UCF101, HMDB51, Breakfast, and Something-Something V1 and V2, where we compare favorably to existing heuristic frame sampling methods.

关键词： Training computer vision Philosophical considerations Semantics Memory management Sampling methods Nonhomogeneous media

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Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map

Self-Supervised Simultaneous Multi-Step Prediction of Road D...

引用

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

作者： Amirloo, Elmira Rohani, Mohsen Banijamali, Ershad Luo, Jun Poupart, Pascal Huawei Noahs Ark Lab Toronto ON Canada Univ Waterloo Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9781665445092

While supervised learning is widely used for perception modules in conventional autonomous driving solutions, scalability is hindered by the huge amount of data labeling needed. In contrast, while end-to-end architectures do not require labeled data and are potentially more scalable, interpretability is sacrificed. We introduce a novel architecture that is trained in a fully self-supervised fashion for simultaneous multi-step prediction of space-time cost map and road dynamics. Our solution replaces the manually designed cost function for motion planning with a learned high dimensional cost map that is naturally interpretable and allows diverse contextual information to be integrated without manual data labeling. Experiments on real world driving data show that our solution leads to lower number of collisions and road violations in long planning horizons in comparison to baselines, demonstrating the feasibility of fully self-supervised prediction without sacrificing scalability.

关键词： Costs Roads Scalability Supervised learning Dynamics computer architecture Manuals

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SLADE: A Self-Training Framework For Distance Metric Learning

SLADE: A Self-Training Framework For Distance Metric Learnin...

引用

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

作者： Duan, Jiali Lin, Yen-Liang Son Tran Davis, Larry S. Kuo, C-C Jay Univ Southern Calif Los Angeles CA 90089 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings. We use self-supervised representation learning to initialize the teacher model. To better deal with noisy pseudo labels generated by the teacher network, we design a new feature basis learning component for the student network, which learns basis functions of feature representations for unlabeled data. The learned basis vectors better measure the pairwise similarity and are used to select high-confident samples for training the student network. We evaluate our method on standard retrieval benchmarks: CUB-200, Cars196 and In-shop. Experimental results demonstrate that with additional unlabeled data, our approach significantly improves the performance over the state-of-the-art methods.

关键词： Training computer vision Art Benchmark testing Extraterrestrial measurements Data models pattern recognition

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Reconsidering Representation Alignment for Multi-view Clustering

Reconsidering Representation Alignment for Multi-view Cluste...

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

作者： Trosten, Daniel J. Lokse, Sigurd Jenssen, Robert Kampffmeyer, Michael UiT Arctic Univ Norway Dept Phys & Technol Tromso Norway UiT Machine Learning Grp Tromso Norway

ISBN: (纸本)9781665445092

Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering. However, we identify several drawbacks with naively aligning representation distributions. We demonstrate that these drawbacks both lead to less separable clusters in the representation space, and inhibit the model's ability to prioritize views. Based on these observations, we develop a simple baseline model for deep multi-view clustering. Our baseline model avoids representation alignment altogether, while performing similar to, or better than, the current state of the art. We also expand our baseline model by adding a contrastive learning component. This introduces a selective alignment procedure that preserves the model's ability to prioritize views. Our experiments show that the contrastive learning component enhances the baseline model, improving on the current state of the art by a large margin on several datasets(1).

关键词： computer vision Computational modeling Adversarial machine learning pattern recognition

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Generalization on Unseen Domains via Inference-time Label-Preserving Target Projections

Generalization on Unseen Domains via Inference-time Label-Pr...

引用

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

作者： Pandey, Prashant Raman, Mrigank Varambally, Sumanth Prathosh, A. P. IIT Delhi Delhi India

ISBN: (纸本)9781665445092

Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem. While many approaches have been proposed to solve this problem, they only utilize source data during training but do not take advantage of the fact that a single target example is available at the time of inference. Motivated by this, we propose a method that effectively uses the target sample during inference beyond mere classification. Our method has three components - (i) A label-preserving feature or metric transformation on source data such that the source samples are clustered in accordance with their class irrespective of their domain (ii) A generative model trained on the these features (iii) A label-preserving projection of the target point on the source-feature manifold during inference via solving an optimization problem on the input space of the generative model using the learned metric. Finally, the projected target is used in the classifier. Since the projected target feature comes from the source manifold and has the same label as the real target by design, the classifier is expected to perform better on it than the true target. We demonstrate that our method outperforms the state-of-the-art Domain Generalization methods on multiple datasets and tasks.

关键词： Manifolds Training computer vision Machine learning Extraterrestrial measurements Data models pattern recognition

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Generative Hierarchical Features from Synthesizing Images

Generative Hierarchical Features from Synthesizing Images

引用

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

作者： Xu, Yinghao Shen, Yujun Zhu, Jiapeng Yang, Ceyuan Zhou, Bolei Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data. However, how the features learned from solving the task of image generation are applicable to other vision tasks remains seldom explored. In this work, we show that learning to synthesize images can bring remarkable hierarchical visual features that are generalizable across a wide range of applications. Specifically, we consider the pre-trained StyleGAN generator as a learned loss function and utilize its layer-wise representation to train a novel hierarchical encoder. The visual feature produced by our encoder, termed as Generative Hierarchical Feature (GH-Feat), has strong transferability to both generative and discriminative tasks, including image editing, image harmonization, image classification, face verification, landmark detection, and layout prediction. Extensive qualitative and quantitative experimental results demonstrate the appealing performance of GH-Feat.

关键词： Visualization computer vision Image synthesis Face recognition Layout Generative adversarial networks Feature extraction

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AdaBins: Depth Estimation Using Adaptive Bins

AdaBins: Depth Estimation Using Adaptive Bins

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

作者： Bhat, Shariq Farooq Alhashim, Ibraheem Wonka, Peter KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781665445092

We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model.

关键词： Measurement Image segmentation Three-dimensional displays Image resolution Estimation computer architecture Transformers

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