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Micro-Expression Classification based on Landmark Relations with Graph Attention Convolutional Network

Micro-Expression Classification based on Landmark Relations ...

引用

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

作者： Kumar, Ankith Jain Rakesh Bhanu, Bir Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

ISBN: (纸本)9781665448994

Facial micro-expressions are brief, rapid, spontaneous gestures of the facial muscles that express an individual's genuine emotions. Because of their short duration and subtlety, detecting and classifying these micro-expressions by humans and machines is difficult. In this paper, a novel approach is proposed that exploits relationships between landmark points and the optical flow patch for the given landmark points. It consists of a two-stream graph attention convolutional network that extracts the relationships between the landmark points and local texture using an optical flow patch. A graph structure is built to draw-out temporal information using the triplet of frames. One stream is for node feature location, and the other one is for a patch of optical-flow information. These two streams (node location stream and optical flow stream) are fused for classification. The results are shown on, CASME II and SAMM, publicly available datasets, for three classes and five classes of micro-expressions. The proposed approach outperforms the state-of-the-art methods for 3 and 5 categories of expressions.

关键词： computer vision conferences Feature extraction Facial muscles pattern recognition Optical flow

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Cross-modal Speaker Verification and recognition: A Multilingual Perspective

Cross-modal Speaker Verification and Recognition: A Multilin...

引用

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

作者： Nawaz, Shah Saeed, Muhammad Saad Morerio, Pietro Mahmood, Arif Gallo, Ignazio Yousaf, Muhammad Haroon Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol IIT Visual Geometry & Modelling VGM Genoa Italy Univ Insubria Varese VA Italy Univ Engn & Technol Taxila Rawalpindi Punjab India Informat Technol Univ Lahore Pakistan

ISBN: (纸本)9781665448994

Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition. Inspired from this, we introduce a challenging task in establishing association between faces and voices across multiple languages spoken by the same set of persons. The aim of this paper is to answer two closely related questions: "Is face-voice association language independent?" and "Can a speaker be recognized irrespective of the spoken language?". These two questions are important to understand effectiveness and to boost development of multilingual biometric systems. To answer these, we collected a Multilingual Audio-Visual dataset, containing human speech clips of 154 identities with 3 language annotations extracted from various videos uploaded online. Extensive experiments on the two splits of the proposed dataset have been performed to investigate and answer these novel research questions that clearly point out the relevance of the multilingual problem.

关键词： Training Annotations Face recognition Pipelines Speech recognition Speaker recognition Task analysis

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MTUNet: Few-shot Image Classification with Visual Explanations

MTUNet: Few-shot Image Classification with Visual Explanatio...

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

作者： Wang, Bowen Li, Liangzhi Verma, Manisha Nakashima, Yuta Kawasaki, Ryo Nagahara, Hajime Osaka Univ Inst Databil Sci IDS Suita Osaka Japan Osaka Univ Grad Sch Med Suita Osaka Japan

ISBN: (纸本)9781665448994

Few-shot learning (FSL) approaches, mostly neural network-based, are assuming that the pre-trained knowledge can be obtained from base (seen) categories and transferred to novel (unseen) categories. However, the black-box nature of neural networks makes it difficult to understand what is actually transferred, which may hamper its application in some risk-sensitive areas. In this paper, we reveal a new way to perform explainable FSL for image classification, using discriminative patterns and pairwise matching. Experimental results prove that the proposed method can achieve satisfactory explainability on two mainstream datasets. Code is available*.

关键词： Knowledge engineering Visualization computer vision conferences Computational modeling Neural networks Benchmark testing

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A Deep Adversarial Framework for Visually Explainable Periocular recognition

A Deep Adversarial Framework for Visually Explainable Perioc...

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

作者： Brito, Joao Proenca, Hugo Univ Beira Interior IT Inst Telecomunicacoes P-6200001 Covilha Portugal

ISBN: (纸本)9781665448994

In the biometrics context, the ability to provide the reasoning behind a decision has been at the core of major research efforts. Explanations serve not only to increase the trust amongst the users of a system, but also to augment the system's overall accountability and transparency. In this work, we describe a periocular recognition framework that not only performs biometric recognition, but also provides visual representations of the features/regions that supported a decision. Being particularly designed to explain non-match ("impostors") decisions, our solution uses adversarial generative techniques to synthesise a large set of "genuine" image pairs, from where the most similar elements with respect to a query are retrieved. Then, assuming the alignment between the query/retrieved pairs, the element-wise differences between the query and a weighted average of the retrieved elements yields a visual explanation of the regions in the query pair that would have to be different to transform it into a "genuine" pair. Our quantitative and qualitative experiments validate the proposed solution, yielding recognition rates that are similar to the state-of-the-art, but - most importantly - also providing the visual explanations for every decision.

关键词： Visualization computer vision Biometrics (access control) conferences Transforms Cognition pattern recognition

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Assistive Signals for Deep Neural Network Classifiers

Assistive Signals for Deep Neural Network Classifiers

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

作者： Pestana, Camilo Liu, Wei Glance, David Owens, Robyn Mian, Ajmal Univ Western Australia 35 Stirling Hwy Crawley WA 6009 Australia

ISBN: (纸本)9781665448994

Deep Neural Networks are brittle in that small changes in the input can drastically affect their prediction outcome and confidence. Consequently, research in this area mainly focus on adversarial attacks and defenses. In this paper, we take an alternative stance and introduce the concept of Assistive Signals, which are perturbations optimized to improve a model's confidence score regardless if it's under attack or not. We analyze some interesting properties of these assistive perturbations and extend the idea to optimize them in the 3D space simulating different lighting conditions and viewing angles. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models more than those generated by conventional methods that work in the 2D space. 'Assistive Signals' also illustrate bias of ML models towards certain patterns in real-life objects.

关键词： Deep learning computer vision Three-dimensional displays Perturbation methods conferences Optimization methods Lighting

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Learning to predict crop type from heterogeneous sparse labels using meta-learning

Learning to predict crop type from heterogeneous sparse labe...

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

作者： Tseng, Gabriel Kerner, Hannah Nakalembe, Catherine Becker-Reshef, Inbal NASA Harvest College Pk MD 20742 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665448994

There are many labelled datasets relating to land cover and crop type mapping that cover diverse geographies, agroecologies and land uses. However, these labels are often extremely sparse, particularly in low- and middle-income regions, with as few as tens of examples for certain crop types. This makes it challenging to train supervised machine learning models to detect specific crops in satellite observations of these regions. We investigate the utility of model-agnostic meta-learning (MAML) to learn from diverse global datasets and improve performance in data-sparse regions. We find that in a variety of countries (Togo, Kenya and Brazil) and across a variety of tasks (crop type mapping, crop vs. non-crop mapping), MAML improves performance compared to pretrained and random initial weights. We also investigate the utility of MAML for different target data-size regimes. We find MAML outperforms other methods for a wide range of training set sizes and positive to negative label ratios, indicating its general suitability for land use and crop type mapping.

关键词： Training Geography computer vision Satellites conferences Machine learning Agriculture

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Layer Importance Estimation with Imprinting for Neural Network Quantization

Layer Importance Estimation with Imprinting for Neural Netwo...

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

作者： Liu, Hongyang Elkerdawy, Sara Ray, Nilanjan Elhoushi, Mostafa Univ Alberta Edmonton AB Canada Huawei Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Neural network quantization has achieved a high compression rate using fixed low bit-width representation of weights and activations while maintaining the accuracy of the high-precision original network. However, mixed precision (per-layer bit-width precision) quantization requires careful tuning to maintain accuracy while achieving further compression and higher granularity than fixed-precision quantization. We propose an accuracy-aware criterion to quantify the layer's importance rank. Our method applies imprinting per layer which acts as a proxy module for accuracy estimation in an efficient way. We rank the layers based on the accuracy gain from previous modules and iteratively quantize first those with less accuracy gain. Previous mixed-precision methods either rely on expensive search techniques such as reinforcement learning (RL) or end-to-end optimization with a lack of interpretation to the quantization configuration scheme. Our method is a one-shot, efficient, accuracy-aware information estimation and thus draws better interpretability to the selected bit-width configuration.

关键词： Training Quantization (signal) Search methods Neural networks Estimation Reinforcement learning pattern recognition

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S3Net: A Single Stream Structure for Depth Guided Image Relighting

S3Net: A Single Stream Structure for Depth Guided Image Reli...

引用

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

作者： Yang, Hao-Hsiang Chen, Wei-Ting Kuo, Sy-Yen Asustek Comp Inc ASUS Intelligent Cloud Serv Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781665448994

Depth guided any-to-any image relighting aims to generate a relit image from the original image and corresponding depth maps to match the illumination setting of the given guided image and its depth map. To the best of our knowledge, this task is a new challenge that has not been addressed in the previous literature. To address this issue, we propose a deep learning-based neural Single Stream Structure network called S3Net for depth guided image relighting. This network is an encoder-decoder model. We concatenate all images and corresponding depth maps as the input and feed them into the model. The decoder part contains the attention module and the enhanced module to focus on the relighting-related regions in the guided images. Experiments performed on challenging benchmark show that the proposed model achieves the 3rd highest SSIM in the NTIRE 2021 Depth Guided Any-to-any Relighting Challenge.

关键词： Training Fuses Lighting Streaming media Feature extraction Decoding pattern recognition

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SRKTDN: Applying Super Resolution Method to Dehazing Task

SRKTDN: Applying Super Resolution Method to Dehazing Task

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

作者： Chen, Tianyi Fu, Jiahui Jiang, Wentao Gao, Chen Liu, Si Beihang Univ Shenyuan Honors Coll Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781665448994

Nonhomogeneous haze removal is a challenging problem, which does not follow the physical scattering model of haze. Numerous existing methods focus on homogeneous haze removal by generating transmission map of the image, which is not suitable for nonhomogeneous dehazing tasks. Some methods use end-to-end model but are also designed for homogeneous haze. Inspired by Knowledge Transfer Dehazing Network and Trident Dehazing Network, we propose a model with super-resolution method and knowledge transfer method. Our model consists of a teacher network, a dehaze network and a super-resolution network. The teacher network provides the dehaze network with reliable prior, the dehaze network focuses primarily on haze removal, and the super-resolution network is used to capture details in the hazy image. Ablation study shows that the super-resolution network has significant benefit to image quality. And comparison shows that our model outperforms previous state-of-the-art methods in terms of perceptual quality on NTIRE2021 NonHomogeneous Dehazing Challenge dataset, and also performs well on other datasets.

关键词： Knowledge engineering Image quality Computational modeling conferences Superresolution Scattering pattern recognition

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Multi-Scale Selective Residual Learning for Non-Homogeneous Dehazing

Multi-Scale Selective Residual Learning for Non-Homogeneous ...

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

作者： Jo, Eunsung Sim, Jae-Young Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan South Korea Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan South Korea

ISBN: (纸本)9781665448994

As the particles in hazy medium cause the absorption and scattering of light, the images captured under such environment suffer from quality degradation such as low contrast and color distortion. While numerous single image dehazing methods have been proposed to reconstruct clean images from hazy images, non-homogeneous dehazing has been rarely studied. In this paper, we design an end-to-end network to remove non-homogeneous dense haze. We employ the selective residual blocks to adaptively improve the visibility of resulting images, where the input feature and the residual feature are combined with fully trainable weights. Experimental results including the ablation study demonstrate that the proposed method is a promising tool for non-homogeneous dehazing that enhances the contrast of hazy images effectively while restoring colorful appearance faithfully.

关键词： Degradation computer vision Image color analysis conferences Scattering Tools Distortion

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