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Multi Model Ensemble for Compound Expression recognition

Multi Model Ensemble for Compound Expression Recognition

引用

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

作者： Jun Yu Jichao Zhu Wangyuan Zhu Zhongpeng Cai Gongpeng Zhao Zhihong Wei Guochen Xie Zerui Zhang Qingsong Liu Jiaen Liang University of Science and Technology of China Unisound AI Technology Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Compound Expression recognition (CER) plays a crucial role in interpersonal interactions. Due to the complexity of human emotional expressions, which leads to the existence of compound expressions, it is necessary to consider both local and global facial expressions comprehensively for recognition. In this paper, to address this issue, we propose a solution for compound expression recognition based on ensemble learning methods. Specifically, our task is classification. We trained three expression classification models based on convolutional networks (ResNet50), vision Transformers, and multi-scale local attention networks, respectively. Then, by using late fusion, integrated the outputs of three models to predict the final result, leveraging the strengths of different models. Our method achieves high accuracy on RAF-DB and in sixth Affective Behavior Analysis in-the-wild (ABAW) Challenge, achieves an F1 score of 0.224 on the test set of C-EXPR-DB.

关键词： computer vision Emotion recognition Face recognition Predictive models Transformers Feature extraction Compounds

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A Cortically-inspired Architecture for Event-based Visual Motion Processing: From Design Principles to Real-world Applications

A Cortically-inspired Architecture for Event-based Visual Mo...

引用

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

作者： Peveri, Francesca Testa, Simone Sabatini, Silvio P. Univ Genoa Dept Informat Bioengn Robot & Syst Engn Via Opera Pia 11a Genoa Italy

ISBN: (纸本)9781665448994

We developed and tested the architecture of a bio-inspired Spiking Neural Network for motion estimation. The computation performed by the retina is emulated by the neuromorphic event-based image sensor DAVIS346 which constitutes the input of our network. We obtained neurons highly tuned to spatial frequency and orientation of the stimulus through a combination of feed-forward excitatory connections modeled as an elongated Gaussian kernel and recurrent inhibitory connections from two clusters of neurons within the same cortical layers. Sums over adjacent nodes weighted by time-variable synapses are used to attain Gabor-like spatio-temporal V1 receptive fields with selectivity to the stimulus' motion. In order to gain the invariance to the stimulus phase, the two polarities of the events provided by the neuromorphic sensor were exploited, which allowed us to build two pairs of quadrature filters from which we obtain Motion Energy detectors as described in [2]. Finally, a decoding stage allows us to compute optic flow from the Motion Detector layers. We tested the approach proposed with both synthetic and natural stimuli.

关键词： Visualization Neuromorphics Neurons Neural networks computer architecture Detectors Spatial filters

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Detecting and Matching Related Objects with One Proposal Multiple Predictions

Detecting and Matching Related Objects with One Proposal Mul...

引用

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

作者： Liu, Yang Hafemann, Luiz G. Jamieson, Michael Javan, Mehrsan Sportlogiq Montreal PQ Canada

ISBN: (纸本)9781665448994

Tracking players in sports videos is commonly done in a tracking-by-detection framework, first detecting players in each frame, and then performing association over time. While for some sports tracking players is sufficient for game analysis, sports like hockey, tennis and polo may require additional detections, that include the object the player is holding (e.g. racket, stick). The baseline solution for this problem involves detecting these objects as separate classes, and matching them to player detections based on the intersection over union (IoU). This approach, however, leads to poor matching performance in crowded situations, as it does not model the relationship between players and objects. In this paper, we propose a simple yet efficient way to detect and match players and related objects at once without extra cost, by considering an implicit association for prediction of multiple objects through the same proposal box. We evaluate the method on a dataset of broadcast ice hockey videos, and also a new public dataset we introduce called COCO +Torso. On the ice hockey dataset, the proposed method boosts matching performance from 57.1% to 81.4%, while also improving the meanAP of player+stick detections from 68.4% to 88.3%. On the COCO +Torso dataset, we see matching improving from 47.9% to 65.2%. The COCO +Torso dataset, code and pre-trained models will be released at https: //***/foreverYoungGitHub/detectand-match-related-objects.

关键词： computer vision conferences Games Detectors Ice Proposals pattern matching

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Explainable Deep Classification Models for Domain Generalization

Explainable Deep Classification Models for Domain Generaliza...

引用

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

作者： Zunino, Andrea Bargal, Sarah Adel Volpi, Riccardo Sameki, Mehrnoosh Zhang, Jianming Sclaroff, Stan Murino, Vittorio Saenko, Kate Huawei Ireland Res Ctr Dublin Ireland Boston Univ Dept Comp Sci 111 Cummington St Boston MA 02215 USA Naver Labs Europe Meylan France Microsoft Redmond WA USA Adobe Res San Jose CA USA Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Univ Verona Verona Italy

ISBN: (纸本)9781665448994

Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object classification, but as a consequence, suffers no perceptible accuracy degradation. Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision. This is represented in the form of a saliency map conveying how much each pixel contributed to the network's decision. Our training strategy enforces a periodic saliency-based feedback to encourage the model to focus on the image regions that directly correspond to the ground-truth object. We quantify explainability using an automated metric, and using human judgement. We propose explainability as a means for bridging the visual-semantic gap between different domains where model explanations are used as a means of disentagling domain specific information from otherwise relevant features. We demonstrate that this leads to improved generalization to new domains without hindering performance on the original domain.

关键词： Training Degradation Measurement Visualization computer vision conferences Computational modeling

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EVSRNet: Efficient Video Super-Resolution with Neural Architecture Search

EVSRNet: Efficient Video Super-Resolution with Neural Archit...

引用

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

作者： Liu, Shaoli Zheng, Chengjian Lu, Kaidi Gao, Si Wang, Ning Wang, Bofei Zhang, Diankai Zhang, Xiaofeng Xu, Tianyu ZTE Corp State Key Lab Mobile Network & Mobile Multimedia Shenzhen Peoples R China

ISBN: (纸本)9781665448994

With the development of convolutional neural networks (CNN), the super-resolution results of CNN-based method have far surpassed traditional method. In particular, the CNN-based single image super-resolution method has achieved excellent results. Video sequences contain more abundant information compare with image, but there are few video super-resolution methods that can be applied to mobile devices due to the requirement of heavy computation, which limits the application of video super-resolution. In this work, we propose the Efficient Video Super-Resolution Network (EVSRNet) with neural architecture search for real-time video super-resolution. Extensive experiments show that our method achieves a good balance between quality and efficiency. Finally, we achieve a competitive result of 7.36 where the PSNR is 27.85 dB and the inference time is 11.3 ms/f on the target snapdragon 865 SoC, resulting in a 2nd place in the Mobile AI(MAI) 2021 real-time video super-resolution challenge. It is noteworthy that, our method is the fastest and significantly outperforms other competitors by large margins.

关键词： Visualization Superresolution Video sequences Neural networks computer architecture Streaming media Real-time systems

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Combining Weight Pruning and Knowledge Distillation For CNN Compression

Combining Weight Pruning and Knowledge Distillation For CNN ...

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

作者： Aghli, Nima Ribeiro, Eraldo Florida Inst Technol 150 W Univ Blvd Melbourne FL 32901 USA

ISBN: (纸本)9781665448994

Complex deep convolutional neural networks such as ResNet require expensive hardware such as powerful GPUs to achieve real-time performance. This problem is critical for applications that run on low-end embedded GPU or CPU systems with limited resources. As a result, model compression for deep neural networks becomes an important research topic. Popular compression methods such as weight pruning remove redundant neurons from the CNN without affecting the network's output accuracy. While these pruning methods work well on simple networks such as VGG or AlexNet, they are not suitable for compressing current state-of-the-art networks such as ResNets because of these networks' complex architectures with dimensionality dependencies. This dependency results in filter pruning breaking the structure of ResNets leading to an untrainable network. In this paper, we first use the weight pruning method only on a selective number of layers in the ResNet architecture to avoid breaking the network structure. Second, we introduce a knowledge distillation architecture and a loss function to compress the untouched layers during the pruning. We test our method on both image-based regression and classification networks for head-pose estimation and image classification. Our compression method reduces the models' size significantly while maintaining the accuracy very close to the baseline model.

关键词： Image coding Neurons Estimation Graphics processing units computer architecture Real-time systems Convolutional neural networks

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What is Point Supervision Worth in Video Instance Segmentation?

What is Point Supervision Worth in Video Instance Segmentati...

引用

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

作者： Shuaiyi Huang De-An Huang Zhiding Yu Shiyi Lan Subhashree Radhakrishnan Jose M. Alvarez Abhinav Shrivastava Anima Anandkumar University of Maryland College Park NVIDIA Caltech

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object masks which are expensive. We reduce the human annotations to only one point for each object in a video frame during training, and obtain high-quality mask predictions close to fully supervised models. Our proposed training method consists of a class-agnostic proposal generation module to provide rich negative samples and a spatio-temporal point-based matcher to match the object queries with the provided point annotations. Comprehensive experiments on three VIS benchmarks demonstrate competitive performance of the proposed framework, nearly matching fully supervised methods.

关键词： Instance segmentation Training computer vision Annotations Shape Object segmentation Object detection

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An Improved Attention for Visual Question Answering

An Improved Attention for Visual Question Answering

引用

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

作者： Rahman, Tanzila Chou, Shih-Han Sigal, Leonid Carenini, Giuseppe Univ British Columbia Dept Comp Sci Vancouver BC Canada Vector Inst AI Toronto ON Canada Canada CIFAR AI Chair Toronto ON Canada

ISBN: (纸本)9781665448994

We consider the problem of Visual Question Answering (VQA). Given an image and a free-form, open-ended, question, expressed in natural language, the goal of VQA system is to provide accurate answer to this question with respect to the image. The task is challenging because it requires simultaneous and intricate understanding of both visual and textual information. Attention, which captures intra- and inter-modal dependencies, has emerged as perhaps the most widely used mechanism for addressing these challenges. In this paper, we propose an improved attention-based architecture to solve VQA. We incorporate an Attention on Attention (AoA) module within encoder-decoder framework, which is able to determine the relation between attention results and queries. Attention module generates weighted average for each query. On the other hand, AoA module first generates an information vector and an attention gate using attention results and current context;and then adds another attention to generate final attended information by multiplying the two. We also propose multimodal fusion module to combine both visual and textual information. The goal of this fusion module is to dynamically decide how much information should be considered from each modality. Extensive experiments on VQA-v2 benchmark dataset show that our method achieves better performance than the baseline method.

关键词： Visualization computer vision conferences Computational modeling Natural languages computer architecture Logic gates

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Indirect synthetic attack on thermal face biometric systems via visible-to-thermal spectrum conversion

Indirect synthetic attack on thermal face biometric systems ...

引用

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

作者： Mallat, Khawla Dugelay, Jean-Luc EURECOM Sophia Antipolis France

ISBN: (纸本)9781665448994

The growing interest in exploring thermal face biometrics is mostly due to the robustness of thermal imagery to face spoofing attacks. However, this robustness lies in the acquisition of thermal properties by the thermal sensor and is limited to presentation attacks. In this paper, we propose a new type of attack on thermal face recognition systems, performed at the post-sensor level. In the visible spectrum, this attack would be carried out by simply injecting a face image of the claimed identity into the communication channel right after the sensor. However, unlike visible face images that are abundantly available on the web, thermal face images are not easy to obtain. Therefore, we propose to generate synthetic thermal attacks by converting visible face images into the thermal spectrum. To perform visible-to-thermal spectrum conversion, we use a cascaded refinement network trained using contextual loss. In a scenario where the attacker has prior knowledge about the spoofing countermeasure of the system, we introduce a new loss computed at the local binary pattern (LBP) maps level to fool an LBP-based spoofing attack detection algorithm. The vulnerability of thermal face biometric systems to the proposed attack is then assessed using two existing baselines of spoofing attack detection. When compared to the challenging presentation attack using silicone masks, the equal error rate has increased from 0.20% to 11.60% and from 2.28% to 58.54% when exposed to the proposed synthetic attack, using the two spoofing attack detection baselines.

关键词： computer vision Error analysis Social networking (online) Face recognition conferences Communication channels Thermal sensors

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Evaluating the Impact of Wide-Angle Lens Distortion on Learning-based Depth Estimation

Evaluating the Impact of Wide-Angle Lens Distortion on Learn...

引用

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

作者： Buquet, Julie Zhang, Jinsong Roulet, Patrice Thibault, Simon Lalonde, Jean-Francois Univ Laval Quebec City PQ Canada Immervision Montreal PQ Canada

ISBN: (纸本)9781665448994

Most computer vision research focuses on narrow angle lenses and is not adapted to super-wide-angle (aka spherical) lenses. This is mainly because current neural networks are not designed or trained to interpret the significant barrel distortion that is introduced in the captured image by such wide angle *** these lenses capture a half-sphere or a section of sphere on the object space, barrel distortion appears when the image is projected on a 2D flat image sensor. By controlling this distortion at the lens design stage, camera designers can create some areas with augmented resolution [26]. In this work, we present an analysis of the impact of such augmented resolution on computer vision algorithm accuracy, using the problem of single image depth estimation as a case study. To this end, 360 degrees panorama datasets are warped to simulate different wideangle lens datasets, which are then used to train identical neural networks. Each lens presents specific areas of the image with augmented resolution using spatially-varying non-linear distortion. We show that this property leads to better local accuracy in depth estimation. We also demonstrate that considering lens manufacturing improves performance when tested on realistic lenses, especially in the area of augmented resolution. We further show that this property helps to locally come closer to performances obtained on perspective images without cropping the field of view.

关键词： Training computer vision Nonlinear distortion Neural networks Estimation Tools Cameras

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