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An Attention-based Method for Multi-label Facial Action Unit Detection

An Attention-based Method for Multi-label Facial Action Unit...

引用

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

作者： Le Hoai, Duy Lim, Eunchae Choi, Eunbin Kim, Sieun Pant, Sudarshan Lee, Guee-Sang Kim, Soo-Huyng Yang, Hyung-Jeong Chonnam Natl Univ Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial Action Coding System is an approach for modeling the complexity of human emotional expression. Automatic action unit (AU) detection is a crucial research area in human-computer interaction. This paper describes our submission to the third Affective Behavior Analysis in-the-wild (ABAW) competition 2022. We proposed a method for detecting facial action units in the video. In the first stage, a lightweight CNN-based feature extractor is employed to extract the feature map from each video frame. Then, an attention module is applied to refine the attention map. The attention encoded vector is derived using a weighted sum of the feature map and the attention scores later. Finally, the sigmoid function is used at the output layer to make the prediction suitable for multi-label AUs detection. We achieved a macro F1 score of 0.48 on the validation set and 0.4206 on the test set compared to 0.39 and 0.3650 from the ABAW challenge baseline model.

关键词： Human computer interaction Gold computer vision conferences Computational modeling Feature extraction Encoding

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A Watermarking-Based Framework for Protecting Deep Image Classifiers Against Adversarial Attacks

A Watermarking-Based Framework for Protecting Deep Image Cla...

引用

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

作者： Sun, Chen Yang, En-Hui Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781665448994

Although deep learning-based models have achieved tremendous success in image-related tasks, they are known to be vulnerable to adversarial examples-inputs with imperceptible, but subtly crafted perturbation which fool the models to produce incorrect outputs. To distinguish adversarial examples from benign images, in this paper, we propose a novel watermarking-based framework for protecting deep image classifiers against adversarial attacks. The proposed framework consists of a watermark encoder, a possible adversary, and a detector followed by a deep image classifier to be protected. Specific methods of watermarking and detection are also presented. It is shown by experiment on a subset of ImageNet validation dataset that the proposed framework along with the presented methods of watermarking and detection is effective against a wide range of advanced attacks (static and adaptive), achieving a near zero (effective) false negative rate for FGSM and PGD attacks (static and adaptive) with the guaranteed zero false positive rate. In addition, for all tested deep image classifiers (ResNet50V2, MobileNetV2, InceptionV3), the impact of watermarking on classification accuracy is insignificant with, on average, 0.63% and 0.49% degradation in top 1 and top 5 accuracy, respectively.

关键词： Degradation computer vision Perturbation methods conferences Watermarking Detectors pattern recognition

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Building text features for object image classification

Building text features for object image classification

引用

IEEE-computer-Society conference on computer vision and pattern recognition workshops

作者： Wang, Gang Hoiem, Derek Forsyth, David Dept. of Computer Science University of Illinois Urbana-Champaign (UIUC) United States Dept. of Electrical and Computer Engineering University of Illinois Urbana-Champaign (UIUC) United States

ISBN: (纸本)9781424439928

We introduce a text-based image feature and demonstrate that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the internet. We do not inspect or correct the tags and expect that they are noisy We obtain the text feature of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say a new viewing direction) must rely on its visual examples. Our text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy they are more stable when appearance changes. We test the performance of this feature using PASCAL VOC 2006 and 2007 datasets. Our feature performs well, consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small.

关键词： Classification (of information)

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Leveraging Style and Content features for Text Conditioned Image Retrieval

Leveraging Style and Content features for Text Conditioned I...

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

作者： Chawla, Pranit Jandial, Surgan Badjatiya, Pinkesh Chopra, Ayush Sarkar, Mausoom Krishnamurthy, Balaji IIT Kharagpur Kharagpur W Bengal India IIT Hyderabad Kandi Telangana India Adobe Media & Data Sci Res Lab San Jose CA USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665448994

Image Search is a fundamental task playing a significant role in the success of wide variety of frameworks and applications. However, with the increasing sizes of product catalogues and the number of attributes per product, it has become difficult for users to express their needs effectively. Therefore, we focus on the problem of Image Retrieval with Text Feedback, which involves retrieving modified images according to the natural language feedback provided by users. In this work, we hypothesise that since an image can be delineated by its content and style features, modifications to the image can also take place in the two sub spaces respectively. Hence, we decompose an input image into its corresponding style and content features, apply modification of the text feedback individually in both the style and content spaces and finally fuse them for retrieval. Our experiments show that our approach outperforms a recent state of the art method in this task, TIRG, that seeks to use a single vector in contrast to leveraging the modification via text over style and content spaces separately.

关键词： computer vision Fuses conferences Image retrieval Natural languages pattern recognition Task analysis

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An Effective Framework of Multi-Class Product Counting and recognition for Automated Retail Checkout

An Effective Framework of Multi-Class Product Counting and R...

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

作者： Wan, Junfeng Qian, Shuhao Tian, Zihan Zhao, Yanyun Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

As the field of computer vision grows, Automated Retail Checkout has become a highly anticipated development goal. The key of this task is to improve the accuracy rate. If there is an error, it will bring serious losses to the business and awful experience for customers which is not our expected. This competition gives us an opportunity to simulate check-out in a real world scenario, so that we can identify problems and solve them, not only for the competition, but also for the practical application. As one of the participating teams in this task, we pursue the goal of avoiding misdetection and misclassification, and build a complete set of framework to achieve high-precise, high-recall performance. In addition, there is an excessive difference between the training data and test data. How to use limited data to make up for the differences in this part is also one of the highlights of our framework. In general, our framework consists of three main parts. Firstly, the Pre-Processing module to make up for the differences between training and test data. The DTC module completes the overall process of automatic recognition. Finally the MTCR module is proposed to post-process the output of the DTC module. On the TestA data of AICITY2022 Task 4, we have achieved significant result compared to the other teams. Finally, our model is ranked 1st in AICITY2022 Task 4 [17].

关键词： Training computer vision Codes conferences Training data Trajectory pattern recognition

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QFabric: Multi-Task Change Detection Dataset

QFabric: Multi-Task Change Detection Dataset

引用

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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Improving In-field Cassava Whitefly Pest Surveillance with Machine Learning

Improving In-field Cassava Whitefly Pest Surveillance with M...

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

作者： Tusubira, Jeremy Francis Nsumba, Solomon Ninsiima, Flavia Akera, Benjamin Acellam, Guy Nakatumba, Joyce Mwebaze, Ernest Quinn, John Oyana, Tonny Makerere Univ Artificial Intelligence Lab Kampala Uganda Google Res Mountain View CA USA Makerere Univ Geospatial Data & Computat Intelligence Lab Kampala Uganda

ISBN: (纸本)9781728193601

Whiteflies are the major vector responsible for the transmission of cassava related diseases in tropical environments, and knowing the numbers of whiteflies is key in detecting and identifying their spread and prevention. However, the current approach for counting whiteflies is a simple visual inspection, where a cassava leaf is turned upside down to reveal the underside where the whiteflies reside to enable a manual count. Repeated across many cassava farms, this task is quite tedious and time-consuming. In this paper, we propose a method to automatically count whiteflies using computer vision techniques. To implement this approach, we collected images of infested cassava leaves and trained a computer vision detector using Haar Cascade and Deep Learning techniques. The two techniques were used to identify the pest in images and return a count. Our results show that this novel method produces a white-fly count with high precision. This method could be applied to similar object detection scenarios similar to the whitefly problem with minor adjustments.

关键词： computer vision

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A Neural-network Enhanced Video Coding Framework beyond VVC

A Neural-network Enhanced Video Coding Framework beyond VVC

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

作者： Li, Junru Li, Yue Lin, Chaoyi Zhang, Kai Zhang, Li Bytedance Inc Multimedia Lab San Diego CA 92122 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a hybrid video compression framework, aiming at providing a demonstration of applying deep learning-based approaches beyond conventional coding framework. The proposed hybrid framework is established over the Enhanced Compression Model (ECM) of which the core is the Versatile Video Coding (VVC) standard. We propose to integrate a series of enhanced coding tools, such as block partitioning, intra prediction, and inter prediction to further remove the spatial and temporal redundancy. Moreover, deep learning-based technologies including loop filter and super resolution are involved to restore the compression distortion. Compared with the VVC software VTM-11.0, experimental results demonstrate the effectiveness of the proposed learning-based framework, leading to 25.81%, 35.08%, and 37.54% bit-rate savings for Y, Cb and Cr components, respectively under random access configuration. In addition, the proposed framework achieves 39.313 and 32.050 PSNRs in the test set under 1 Mbps and 0.1 Mbps video compression tracks of CLIC-2022. 33.522, 30.758, and 28.300 in terms of PSNR are obtained in 0.3 bpp, 0.15 bpp, and 0.075 bpp image compression tracks.

关键词： Video coding Image coding Filtering Redundancy Video compression Software pattern recognition

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Unpaired Real-World Super-Resolution with Pseudo Controllable Restoration

Unpaired Real-World Super-Resolution with Pseudo Controllabl...

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

作者： Romero, Andres Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven Leuven Belgium Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9781665487399

Current super-resolution methods rely on the bicubic down-sampling assumption in order to develop the ill-posed reconstruction of the low-resolution image. Not surprisingly, these approaches fail when using real-world low-resolution images due to the presence of artifacts and intrinsic noise absent in the bicubic setup. Consequently, attention is increasingly paid to techniques that alleviate this problem and super-resolve real-world images. As acquiring paired real-world datasets is a challenging problem, real-world super-resolution solutions are traditionally tackled as a blind problem or as an unpaired data-driven problem. The former makes assumptions about the down-sampling operations, the latter uses unpaired training to learn the real distributions. Recently, blind approaches have dominated this problem by assuming a diverse bank of degradations, whereas the unpaired solutions have shown under-performance due to the two-staged training. In this paper, we propose an unpaired real-world super-resolution method that performs on par, or even better than blind paired approaches by introducing a pseudo-controllable restoration module in a fully end-to-end system.

关键词： Training Degradation computer vision conferences Superresolution Image restoration pattern recognition

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GRAFIQS: Face Image Quality Assessment Using Gradient Magnitudes

GRAFIQS: Face Image Quality Assessment Using Gradient Magnit...

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

作者： Kolf, Jan Niklas Damer, Naser Boutros, Fadi Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350365474

Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset. To achieve that, we propose quantifying the discrepancy in Batch Normalization statistics (BNS), including mean and variance, between those recorded during FR training and those obtained by processing testing samples through the pretrained FR model. We then generate gradient magnitudes of pretrained FR weights by backpropagating the BNS through the pretrained model. The cumulative absolute sum of these gradient magnitudes serves as the FIQ for our approach. Through comprehensive experimentation, we demonstrate the effectiveness of our training-free and quality labeling-free approach, achieving competitive performance to recent state-of-the-art FIQA approaches without relying on quality labeling, the need to train regression networks, specialized architectures, or designing and optimizing specific loss functions.

关键词： Biometrics computer vision Face Image Quality Assessment Face recognition

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