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Deciphering the face

Deciphering the face

引用

2011 IEEE computer Society conference on computer vision and pattern recognition workshops, CVPRW 2011

作者： Martinez, Aleix M. Ohio State University United States

ISBN: (纸本)9781457705298

We argue that to make robust computer vision algorithms for face analysis and recognition, these should be based on configural and shape features. In this model, the most important task to be solved by computer vision researchers is that of accurate detection of facial features, rather than recognition. We base our arguments on recent results in cognitive science and neuroscience. In particular, we show that different facial expressions of emotion have diverse uses in human behavior/cognition and that a facial expression may be associated to multiple emotional categories. These two results are in contradiction with the continuous models in cognitive science, the limbic assumption in neuroscience and the multidimensional approaches typically employed in computer vision. Thus, we propose an alternative hybrid continuous-categorical approach to the perception of facial expressions and show that configural and shape features are most important for the recognition of emotional constructs by humans. We illustrate how these image cues can be successfully exploited by computer vision algorithms. Throughout the paper, we discuss the implications of these results in applications in face recognition and human-computer interaction. © 2011 IEEE.

关键词： computer vision

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Motion Aware Double Attention Network for Dynamic Scene Deblurring

Motion Aware Double Attention Network for Dynamic Scene Debl...

引用

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

作者： Yang, Dan Yamac, Mehmet Huawei Technol Oy Finland Co Ltd Helsinki Finland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Motion deblurring in dynamic scenes is a challenging task when the blurring is caused by one or a combination of various reasons such as moving objects, camera movement, etc. Since event cameras can detect changes in intensity with a low latency, necessary motion information is inherently captured in event data, which could be quite useful for deblurring standard camera images. The degradation intensity does not show homogeneity across an image due to factors like object depth, speed, etc. We propose a two-branch network structure, Motion Aware Double Attention Network (MADANet), that pays special attention to areas with high blur. As part of the network, event data is first used by the high blur region segmentation module that creates a probability-like score for areas exhibiting high relative motion to the camera. Then, the event data is also injected to feature maps in the main body, where there is a second attention mechanism available for each branch. The effective usage of event data and two-level attention mechanisms makes the network very compact. During the experiment, it was shown that the proposed network could achieve state-of-the-art performance not only on the benchmark dataset from GoPro, but also on two newly collected datasets, one of which contains real event data.

关键词： Degradation Image segmentation Motion segmentation Dynamics Data visualization Cameras pattern recognition

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On Improving Cross-dataset Generalization of Deepfake Detectors

On Improving Cross-dataset Generalization of Deepfake Detect...

引用

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

作者： Nadimpalli, Aakash Varma Rattani, Ajita Wichita State Univ Sch Comp Wichita KS 67260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial manipulation by deep fake has caused major security risks and raised severe societal concerns. As a countermeasure, a number of deep fake detection methods have been proposed recently. Most of them model deep fake detection as a binary classification problem using a backbone convolutional neural network (CNN) architecture pretrained for the task. These CNN-based methods have demonstrated very high efficacy in deep fake detection with the Area under the Curve (AUC) as high as 0.99. However, the performance of these methods degrades significantly when evaluated across datasets. In this paper, we formulate deep fake detection as a hybrid combination of supervised and reinforcement learning (RL) to improve its cross-dataset generalization performance. The proposed method chooses the top-k augmentations for each test sample by an RL agent in an image-specific manner. The classification scores, obtained using CNN, of all the augmentations of each test image are averaged together for final real or fake classification. Through extensive experimental validation, we demonstrate the superiority of our method over existing published research in cross-dataset generalization of deep fake detectors, thus obtaining state-of-the-art performance.

关键词： Deepfakes computer vision conferences Detectors Reinforcement learning computer architecture pattern recognition

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Internet Video Category recognition

Internet Video Category Recognition

引用

IEEE conference on computer vision and pattern recognition

作者： Schindler, Grant Zitnick, Larry Brown, Matthew Georgia Inst Technol Atlanta GA 30332 USA Microsoft Res Atlanta GA USA Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9781424423392

In this paper, we examine the problem of internet video categorization. Specifically, we explore the representation of a video as a "bag of words" using various combinations of spatial and temporal descriptors. The descriptors incorporate both spatial and temporal gradients as well as optical flow information. We achieve state-of-the-art results on a standard human activity recognition database and demonstrate promising category recognition performance on two new databases of approximately 1000 and 1500 online user-submitted videos, which we will be making available to the community.

关键词： pattern recognition

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Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image recognition

Incremental Meta-Learning via Episodic Replay Distillation f...

引用

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

作者： Wang, Kai Liu, Xialei Bagdanov, Andy Herranz, Luis Jui, Shangling van de Weijer, Joost Comp Vis Ctr Barcelona Spain Nankai Univ Tianjin Peoples R China Univ Florence MICC Florence Italy Huawei Kirin Solut Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more generalizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively.

关键词： Training computer vision Image recognition Upper bound conferences pattern recognition Task analysis

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Analysis and Extensions of Adversarial Training for Video Classification

Analysis and Extensions of Adversarial Training for Video Cl...

引用

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

作者： Kinfu, Kaleab A. Vidal, Rene Johns Hopkins Univ Math Inst Data Sci Baltimore MD 21218 USA

ISBN: (纸本)9781665487399

Adversarial training (AT) is a simple yet effective defense against adversarial attacks to image classification systems, which is based on augmenting the training set with attacks that maximize the loss. However, the effectiveness of AT as a defense for video classification has not been thoroughly studied. Our first contribution is to show that generating optimal attacks for video requires carefully tuning the attack parameters, especially the step size. Notably, we show that the optimal step size varies linearly with the attack budget. Our second contribution is to show that using a smaller (sub-optimal) attack budget at training time leads to a more robust performance at test time. Based on these findings, we propose three defenses against attacks with variable attack budgets. The first one, Adaptive AT, is a technique where the attack budget is drawn from a distribution that is adapted as training iterations proceed. The second, Curriculum AT, is a technique where the attack budget is increased as training iterations proceed. The third, Generative AT, further couples AT with a denoising generative adversarial network to boost robust performance. Experiments on the UCF101 dataset demonstrate that the proposed methods improve adversarial robustness against multiple attack types.

关键词： Training computer vision conferences Noise reduction Generative adversarial networks Robustness pattern recognition

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Integrating Pose and Mask Predictions for Multi-person in Videos

Integrating Pose and Mask Predictions for Multi-person in Vi...

引用

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

作者： Heo, Miran Hwang, Sukjun Oh, Seoung Wug Lee, Joon-Young Kim, Seon Joo Yonsei Univ Seoul South Korea Adobe Res San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In real-world applications for video editing, humans are arguably the most important objects. When editing videos of humans, the efficient tracking of fine-grained masks and body joints is the fundamental requirement. In this paper, we propose a simple and efficient system for jointly tracking pose and segmenting high-quality masks for all humans in the video. We design a pipeline that globally tracks pose and locally segments fine-grained masks. Specifically, CenterTrack is first employed to track human poses by viewing the whole scene, and then the proposed local segmentation network leverages the pose information as a powerful query to carry out high-quality segmentation. Furthermore, we adopt a highly light-weight MLP-Mixer layer within the segmentation network that can efficiently propagate the query pose throughout the region of interest with minimal overhead. For the evaluation, we collect a new benchmark called KineMask which includes various appearances and actions. The experimental results demonstrate that our method has superior fine-grained segmentation performance. Moreover, it runs at 33 fps, achieving a great balance of speed and accuracy compared to the prevailing online Video Instance Segmentation methods.

关键词： computer vision conferences Computational modeling Pipelines Benchmark testing pattern recognition

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Age and Gender Classification using Convolutional Neural Networks

Age and Gender Classification using Convolutional Neural Net...

引用

IEEE conference on computer vision and pattern recognition (CVPR)

作者： Levi, Gil Hassner, Tal Open Univ Israel Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781467367592

Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.

关键词： Benchmark testing computer architecture Estimation Face Face recognition Neurons Training

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SaR: Self-adaptive Refinement on Pseudo Labels for Multiclass-Imbalanced Semi-supervised Learning

SaR: Self-adaptive Refinement on Pseudo Labels for Multiclas...

引用

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

作者： Lai, Zhengfeng Wang, Chao Cheung, Sen-ching Chuah, Chen-Nee Univ Calif Davis Davis CA 95616 USA Southern Univ Sci & Technol Shenzhen Peoples R China Univ Kentucky Lexington KY 40506 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Class-imbalanced datasets can severely deteriorate the performance of semi-supervised learning (SSL). This is due to the confirmation bias especially when the pseudo labels are highly biased towards the majority classes. Traditional resampling or reweighting techniques may not be directly applicable when the unlabeled data distribution is unknown. Inspired by the threshold-moving method that performs well in supervised learning-based binary classification tasks, we provide a simple yet effective scheme to address the multiclass imbalance issue of SSL. This scheme, named SaR, is a Self-adaptive Refinement of soft labels before generating pseudo labels. The pseudo labels generated post-SaR will be less biased, resulting in higher quality data for training the classifier. We show that SaR can consistently improve recent consistency-based SSL algorithms on various image classification problems across different imbalanced ratios. We also show that SaR is robust to the situations where unlabeled data have different distributions as labeled data. Hence, SaR does not rely on the assumptions that unlabeled data share the same distribution as the labeled data.

关键词： Training computer vision conferences Semisupervised learning pattern recognition Classification algorithms Task analysis

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On the Robustness of Monte Carlo Dropout Trained with Noisy Labels

On the Robustness of Monte Carlo Dropout Trained with Noisy ...

引用

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

作者： Goel, Purvi Li Chen Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

The memorization effect of deep learning hinders its performance to effectively generalize on test set when learning with noisy labels. Prior study has discovered that epistemic uncertainty techniques are robust when trained with noisy labels compared with neural networks without uncertainty estimation. They obtain prolonged memorization effect and better generalization performance under the adversarial setting of noisy labels. Due to its superior performance amongst other selected epistemic uncertainty methods under noisy labels, we focus on Monte Carlo Dropout (MCDropout) and investigate why it is robust when trained with noisy labels. Through empirical studies on datasets MNIST, CIFAR-10, Animal-10n, we deep dive into three aspects of MCDropout under noisy label setting: 1. efficacy: understanding the learning behavior and test accuracy of MCDropout when training set contains artificially generated or naturally embedded label noise;2. representation volatility: studying the responsiveness of neurons by examining the mean and standard deviation on each neuron's activation;3. network sparsity: investigating the network support of MCDropout in comparison with deterministic neural networks. Our findings suggest that MCDropout further sparsifies and regularizes the deterministic neural networks and thus provides higher robustness against noisy labels.

关键词： Training Uncertainty Monte Carlo methods Neurons Estimation Robustness pattern recognition

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