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Micro-expression recognition Based on Facial Graph Representation Learning and Facial Action Unit Fusion

Micro-expression Recognition Based on Facial Graph Represent...

引用

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

作者： Lei, Ling Chen, Tong Li, Shigang Li, Jianfeng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China Hiroshima City Univ Grad Sch Informat Sci Hiroshima Japan

ISBN: (纸本)9781665448994

Micro-expressions recognition is a challenge because it involves subtle variations in facial organs. In this paper, first, we propose a novel pipeline to learn a facial graph (nodes and edges) representation to capture these local subtle variations. We express the micro-expressions with multi-patches based on facial landmarks and then stack these patches into channels while using a depthwise convolution (DConv) to learn the features inside the patches, namely, node learning. Then, the encoder of the transformer (ETran) is utilized to learn the relationships between the nodes, namely, edge learning. Based on node and edge learning, a learned facial graph representation is obtained. Second, because the occurrence of an expression is closely bound to action units, we design an AU-GCN to learn the action unit's matrix by embedding and GCN. Finally, we propose a fusion model to introduce the action unit's matrix into the learned facial graph representation. The experiments are comparing with SOTA on various evaluation criteria, including common classifications on CASME II and SAMM datasets, and also conducted following Micro-expression Grand Challenge 2019 protocol.

关键词： computer vision Protocols Convolution Face recognition conferences Pipelines Feature extraction

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Learned Video Compression with Intra-Guided Enhancement and Implicit Motion Information

Learned Video Compression with Intra-Guided Enhancement and ...

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

作者： Zou, Nannan Zhang, Honglei Cricri, Francesco Tavakoli, Hamed R. Lainema, Jani Aksu, Emre Hannuksela, Miska Rahtu, Esa Nokia Technol Tampere Finland Tampere Univ Tampere Finland

ISBN: (纸本)9781665448994

Although learned approaches to video compression have been proposed with promising results, hand-engineered video codecs are still unbeaten. On the other hand, learned image compression has already surpassed traditional image codecs. In this paper, we propose a learned video compression system that mimics part of the pipeline of traditional codecs while leveraging learned image compression. It comprises two main modules: a learned intra-frame compression module, and a learned inter-frame compression module that is conditioned on intra-coded frames. These modules use separate learned probability models for entropy coding. The intra-frame codec uses a variant of nonlocal attention layers. Regarding the inter-frame codec, we propose an implicit motion information mechanism, and an enhancement of the inter-frame predictions by leveraging the high quality information of intra-coded frames. On the learned probability model side, we propose to use the reference frames as additional conditioning information. We used this system as our submitted entry for the 2021 Challenge on Learned Image Compression (CLIC). In our experiments, we show the effectiveness of our system and its components via a set of ablation studies.

关键词： computer vision Image coding conferences Computational modeling Pipelines MIMICs Video compression

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Efficient Skeleton-Based Action recognition for Real-Time Embedded Systems

Efficient Skeleton-Based Action Recognition for Real-Time Em...

引用

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

作者： Nadhira Noor Fabianaugie Jametoni Jinbeom Kim Hyunsu Hong In Kyu Park Dept. of Electrical and Computer Engineering Inha University Incheon Korea Finedigital Inc Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lightweight action recognition on embedded systems by utilizing skeleton-based techniques, which naturally require less computing and memory resources. To achieve this, we propose innovative methodologies and optimizations tailored for embedded deployment, including post-training quantization, optimized model architectures, and efficient resource utilization. By enabling real-time and lightweight action recognition on resource-constrained embedded systems, our research opens up new possibilities for applications in areas like autonomous surveillance, driving, and indoor safety monitoring.

关键词： Embedded systems Accuracy Quantization (signal) Surveillance Memory management Network architecture Real-time systems

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PupilTAN: A Few-Shot Adversarial Pupil Localizer

PupilTAN: A Few-Shot Adversarial Pupil Localizer

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

作者： Poulopoulos, Nikolaos Psarakis, Emmanouil Z. Kosmopoulos, Dimitrios Univ Patras Comp Engn & Informat Patras Greece

ISBN: (纸本)9781665448994

The eye center localization is a challenging problem faced by many computer vision applications. The challenges typically stem from the scene variability, such as, the wide range of shapes, the lighting conditions, the view angles and the occlusions. Nowadays, the increasing interest on deep neural networks requires a large volume of training data. However, a significant issue is the dependency on labeled data, which are expensive to obtain and susceptible to errors. To address these issues, we propose a deep network, dubbed PupilTAN, that performs image-to-heatmap Translation and an Adversarial training framework that solves the eye localization problem in a few-shot unsupervised way. The key idea is to estimate, by using only a few ground-truth shots, the heatmaps centers' pdf and use it as a generator to create random heatmaps that follow the same probability distribution of the real ones. We showcase that training the deep network with these artificial heatmaps in an adversarial framework not only makes us less dependent on labeled data, but also leads to a significant accuracy improvement. The proposed network achieves real-time performance in a general-purpose computer environment and improves the state-of-the-art accuracy for both MUCT and BioID datasets, even compared with supervised techniques. Furthermore, our model is robust even in the case of reducing its size of up to 1/16 of the original network (0.2M parameters), demonstrating comparable accuracy to the state-of-the-art with high practical value to real-time applications.

关键词： Location awareness Heating systems Training computer vision Shape Training data Real-time systems

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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 ...

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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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QFabric: Multi-Task Change Detection Dataset

QFabric: Multi-Task Change Detection Dataset

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

作者： Verma, Sagar Panigrahi, Akash Gupta, Siddharth Univ Paris Saclay INRIA Cent Supelec Ctr Vis Numer Paris France Granular AI Somerville NJ USA

ISBN: (纸本)9781665448994

Detecting change through multi-image, multi-date remote sensing is essential to developing and understanding of global conditions. Despite recent advancements in remote sensing realized through deep learning, novel methods for accurate multi-image change detection remain unrealized. Recently, several promising methods have been proposed to address this topic, but a paucity of publicly available data limits the methods that can be assessed. In particular, there exists limited work on categorizing the nature and status of change across an observation period. This paper introduces the first labeled dataset available for such a task. We present an open-source change detection dataset, termed QFabric, with 450,000 change polygons annotated across 504 locations in 100 different cities covering a wide range of geographies and urban fabrics. QFabric is a temporal multi-task dataset with 6 change types and 9 change status classes. The geography and environment metadata around each polygon provides context that can be leveraged to build robust deep neural networks. We apply multiple benchmarks on our dataset for change detection, change type and status classification tasks. Project page: https://***/qfabric

关键词： Deep learning Urban areas Predictive models Benchmark testing Metadata Fabrics pattern recognition

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An Empirical Study of Vehicle Re-Identification on the AI City Challenge

An Empirical Study of Vehicle Re-Identification on the AI Ci...

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

作者： Luo, Hao Chen, Weihua Xu, Xianzhe Gu, Jianyang Zhang, Yuqi Liu, Chong Jiang, Yiqi He, Shuting Wang, Fan Li, Hao Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, i.e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge. (1) Both cropping training data and using synthetic data can help the model learn more discriminative features. (2) Since there is a new scenario in the test set that dose not appear in the training set, UDA methods perform well in the challenge. (3) Post-processing techniques including re-ranking, image-to-track retrieval, inter-camera fusion, etc, significantly improve final performance. (4) We ensemble CNN-based models and transformer-based models which provide different representation diversity. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition. Codes are available at https://***/michuanhaohao/AICITY2021_Track2_DMT.

关键词： Training computer vision conferences Computational modeling Urban areas Training data Data models

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Learning Contextual Causality between Daily Events from Time-consecutive Images

Learning Contextual Causality between Daily Events from Time...

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

作者： Zhang, Hongming Huo, Yintong Zhao, Xinran Song, Yangqiu Roth, Dan HKUST Hong Kong Peoples R China CUHK Hong Kong Peoples R China UPenn Philadelphia PA 19104 USA

ISBN: (纸本)9781665448994

Conventional textual-based causal knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causal knowledge records (e.g., ConceptNet) typically do not consider the context. In this paper, we move out of the textual domain to explore a more scalable way of acquiring causal knowledge and investigate the possibility of learning contextual causality from the visual signal. Specifically, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representations, it is possible to discover meaningful causal knowledge from the videos. Further analysis also shows that the contextual property of causal relations indeed exists and considering the contextual property can help better predict the causal relation between events. The Vis-Causal dataset and experiment code are available at https://***/HKUST-KnowComp/Vis_Causal.

关键词： Training Visualization computer vision Annotations Knowledge acquisition conferences pattern recognition

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Instance Segmentation-based Identification of Pelagic Species in Acoustic Backscatter Data

Instance Segmentation-based Identification of Pelagic Specie...

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

作者： Marques, Tunai Porto Cote, Melissa Rezvanifar, Alireza Albu, Alexandra Branzan Ersahin, Kaan Mudge, Todd Gauthier, Stephane Univ Victoria Elect & Comp Engn Victoria BC Canada ASL Environm Sci Victoria BC Canada Fisheries & Oceans Canada Sidney BC Canada

ISBN: (纸本)9781665448994

This paper addresses the automatic identification of pelagic species in acoustic backscatter data. Large quantities of data acquired during underwater acoustic surveys for environmental monitoring and resources management, visualized as echograms, are typically analyzed manually or semi-automatically by marine biologists, which is time-consuming and prone to errors and inter-expert disagreements. In this paper, we propose to detect pelagic species (schools of herring and of juvenile salmon) from echograms with a deep learning (DL) framework based on instance segmentation, allowing us to carefully study the acoustic properties of the targets and to address specific challenges such as close proximity between schools and varying size. Experimental results demonstrate our system's ability to correctly detect pelagic species from echograms and to outperform an existing object detection framework designed for schools of herring in terms of detection performance and computational resources utilization. Our pixel-level detection method has the advantage of generating a precise identification of the pixel groups forming each detection, opening up many possibilities for automatic biological analyses.

关键词： Training Image segmentation Semantics Object detection Standardization Biology pattern recognition

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Adversarial Threats to DeepFake Detection: A Practical Perspective

Adversarial Threats to DeepFake Detection: A Practical Persp...

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

作者： Neekhara, Paarth Dolhansky, Brian Bitton, Joanna Ferrer, Cristian Canton Univ Calif San Diego La Jolla CA 92093 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665448994

Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the credibility of social media platforms and other media sharing web sites. State-of-the art DeepFake detection techniques rely on neural network based classification models which are known to be vulnerable to adversarial examples. In this work, we study the vulnerabilities of state-of-the-art DeepFake detection methods from a practical stand point. We perform adversarial attacks on DeepFake detectors in a black box setting where the adversary does not have complete knowledge of the classification models. We study the extent to which adversarial perturbations transfer across different models and propose techniques to improve the transferability of adversarial examples. We also create more accessible attacks using Universal Adversarial Perturbations which pose a very feasible attack scenario since they can be easily shared amongst attackers. We perform our evaluations on the winning entries of the DeepFake Detection Challenge (DFDC) and demonstrate that they can be easily bypassed in a practical attack scenario by designing transferable and accessible adversarial attacks.(1)

关键词： Social networking (online) Perturbation methods Neural networks Multimedia Web sites Detectors pattern recognition Security

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