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Leveraging Pre-trained Multi-task Deep Models for Trustworthy Facial Analysis in Affective Behaviour Analysis in-the-Wild

Leveraging Pre-trained Multi-task Deep Models for Trustworth...

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

作者： Saychenko, Andrey V. r Sber AI Lab Moscow Russia HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia

ISBN: (纸本)9798350365474

This article presents our results for the sixth Affective Behavior Analysis in-the-wild (ABAW) competition. To improve the trustworthiness of facial analysis, we study the possibility of using pre-trained deep models that extract reliable emotional features without the need to fine-tune the neural networks for a downstream task. In particular, we introduce several lightweight models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN architectures trained in multi-task scenarios to recognize facial expressions, valence, and arousal on static photos. These neural networks extract frame-level features fed into a simple classifier, e.g., linear feed-forward neural network, to predict emotion intensity, compound expressions, and valence/arousal. Experimental results for three tasks from the sixth ABAW challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble techniques. As a result, our solutions took second place in the compound expression recognition competition.

关键词： Compound facial expressition recognition Emotional Mimicry Intensity estimation HSEmotion library lightweight neural networks Valence-Arousal prediction

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Outsmarting Biometric Imposters: Enhancing Iris-recognition System Security through Physical Adversarial Example Generation and PAD Fine-Tuning

Outsmarting Biometric Imposters: Enhancing Iris-Recognition ...

引用

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

作者： Ogino, Yuka Kakizaki, Kazuya Toizumi, Takahiro Ito, Atsushi NEC Corp Ltd Tokyo Japan

ISBN: (纸本)9798350365474

In this paper, we address the vulnerabilities of iris recognition systems to both image-based impersonation attacks and Presentation Attacks (PAs) in physical environments. While existing Presentation Attack Detection (PAD) methods have been effective against PAs, they remain susceptible to adversarial examples. We propose a combination of physical adversarial attacks tailored to iris recognition and PAD, and also propose a defense method against them. Our attack methods involve a physical impersonation attack using adversarial perturbation on the iris region and a physical PAD evading attack using an adversarial patch on the pupil region. We demonstrate the high transferability and effectiveness of our attacks on multiple PA instruments in digital and distinct physical environments using multiple recognition engines. To counteract these attacks, we develop a defense method for PAD involving adversarial fine-tuning against both the physical attacks. This defense method successfully reduces the PAD evasion attack success rate from 71.5% to 21.0% in physical environments and ultimately lowers the overall physical impersonation success rate from 58.0% to 19.5%. Our proposed method lays the groundwork for developing more robust and secure iris recognition systems with increased protection against sophisticated PAs.

关键词： Optical character recognition

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Exploring Facial Expression recognition through Semi-Supervised Pre-training and Temporal Modeling

Exploring Facial Expression Recognition through Semi-Supervi...

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

作者： Yu, Jun Wei, Zhihong Cai, Zhongpeng Zhao, Gongpeng Zhang, Zerui Wang, Yongqi Xie, Guochen Zhu, Jichao Zhu, Wangyuan Liu, Qingsong Liang, Jiaen Univ Sci & Technol China Hefei Peoples R China Unisound AI Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350365474

Facial Expression recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at cvpr2024. In the facial expression recognition task, the limited size of the FER dataset poses a challenge to the expression recognition model's generalization ability, resulting in subpar recognition performance. To address this problem, we employ a Semi-supervised learning technique to generate expression category pseudo labels for unlabeled face data. At the same time, we uniformly sampled the labeled facial expression samples and implemented a debiased feedback learning strategy to address the problem of category imbalance in the dataset and the possible data bias in semi-supervised learning. Moreover, to further compensate for the limitation and bias of features obtained only from static images, we introduced a Temporal Encoder to capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition, our method achieved the third place in the official test set, a result that fully demonstrates the effectiveness and competitiveness of our proposed method.

关键词： Facial Expression recognition Semi-Supervised Learning Temporal Modeling

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Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling

Label-free Anomaly Detection in Aerial Agricultural Images w...

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

作者： Shikhar, Sambal Sobti, Anupam Plaksha Univ Mohali Punjab India

ISBN: (纸本)9798350365474

Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies, necessitating extensive annotated data. In this work, we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only "normal" data while training, we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating "normal" images for training. Evaluation on the Agriculture-vision data challenge shows a 6.3% mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-vision Challenge Dataset [5].

关键词： Anomaly Detection computer vision Masked Image Modelling Precision Agriculture UAV Images

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Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language

Separating the "Chirp" from the "Chat": Self-supervised Visu...

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

作者： Hamilton, Mark Zisserman, Andrew Hershey, John R. Freeman, William T. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Microsoft Redmond WA 98052 USA Google Oxford England

ISBN: (纸本)9798350353006

We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visual aligned features solely through watching videos. We show that DenseAV can discover the "meaning" of words and the "location" of sounds without explicit localization supervision. Furthermore, it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast, many other systems that learn "global" audio and video representations cannot localize words and sound. Finally, we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the current state-of-the-art, ImageBind, on cross-modal retrieval using fewer than half of the parameters. Project Page: https://***/denseav

关键词： audio audio-visual contrastive learning language multimodal object discovery semantic segmentation sound unsupervised learning vision visual grounding

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Coarse or Fine? Recognising Action End States without Labels

Coarse or Fine? Recognising Action End States without Labels

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

作者： Moltisanti, Davide Bilen, Hakan Sevilla-Lara, Laura Keller, Frank Univ Bath Bath Avon England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9798350365474

We focus on the problem of recognising the end state of an action in an image, which is critical for understanding what action is performed and in which manner. We study this focusing on the task of predicting the coarseness of a cut, i.e., deciding whether an object was cut "coarsely" or "finely". No dataset with these annotated end states is available, so we propose an augmentation method to synthesise training data. We apply this method to cutting actions extracted from an existing action recognition dataset. Our method is object agnostic, i.e., it presupposes the location of the object but not its identity. Starting from less than a hundred images of a whole object, we can generate several thousands images simulating visually diverse cuts of different coarseness. We use our synthetic data to train a model based on UNet and test it on real images showing coarsely/finely cut objects. Results demonstrate that the model successfully recognises the end state of the cutting action despite the domain gap between training and testing, and that the model generalises well to unseen objects.

关键词： adverb recognition fine-grained recognition object end-state recognition

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Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models

Strategies to Improve Real-World Applicability of Laparoscop...

引用

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

作者： Kolbinger, Fiona R. He, Jiangpeng Ma, Jinge Zhu, Fengqing Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support. Segmentation performance of such models is traditionally reported using metrics of overlap such as IoU. However, imbalanced and unrealistic representation of classes in the training data and suboptimal selection of reported metrics have the potential to skew nominal segmentation performance and thereby ultimately limit clinical translation. In this work, we systematically analyze the impact of class characteristics (i.e., organ size differences), training and test data composition (i.e., representation of positive and negative examples), and modeling parameters (i.e., foreground-to-background class weight) on eight segmentation metrics: accuracy, precision, recall, IoU, F1 score (Dice Similarity Coefficient), specificity, Hausdorff Distance, and Average Symmetric Surface Distance. Our findings support two adjustments to account for data biases in surgical data science: First, training on datasets that are similar to the clinical real-world scenarios in terms of class distribution, and second, class weight adjustments to optimize segmentation model performance with regard to metrics of particular relevance in the respective clinical setting.

关键词： Class Imbalance computer-Assisted Surgery Laparoscopic Surgery Semantic Segmentation Surgical Data Science

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Emotion recognition Using Transformers with Random Masking

Emotion Recognition Using Transformers with Random Masking

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

作者： Min, Seongjae Yang, Junseok Lim, Sejoon Kookmin Univ Seoul South Korea

ISBN: (纸本)9798350365474

In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW) competition have been particularly instrumental in driving research in this area by providing diverse and challenging datasets that enable precise evaluation of complex emotional states. This study leverages the vision Transformer (ViT) and Transformer models to focus on the estimation of Valence-Arousal (VA), which signifies the positivity and intensity of emotions, recognition of various facial expressions, and detection of Action Units (AU) representing fundamental muscle movements. This approach transcends traditional Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) based methods, proposing a new Transformer-based framework that maximizes the understanding of temporal and spatial features. The core contributions of this research include the introduction of a learning technique through random frame masking and the application of Focal loss adapted for imbalanced data, enhancing the accuracy and applicability of emotion and behavior analysis in real-world settings. This approach is expected to contribute to the advancement of emotional computing and deep learning methodologies.

关键词： Convolutional neural networks

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A Dual-Mode Approach for vision-Based Navigation in a Lunar Landing Scenario

A Dual-Mode Approach for Vision-Based Navigation in a Lunar ...

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

作者： Ostrogovich, Luca Del Prete, Roberto Tomasicchio, Giuseppe Longepe, Nicolas Renga, Alfredo Univ Naples Federico II Dept Ind Engn Naples Italy Telespazio SRL Rome Italy ESA ESRIN Lab Frascati Italy

ISBN: (纸本)9798350365474

In this research, a novel approach for autonomous spacecraft navigation, particularly in lunar contexts, is presented, focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation, replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms, resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position, with an average accuracy of approximately 270 m for the absolute navigation mode, while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems-akin to those on-board spacecraft-were conducted. These tests are pivotal in demonstrating the system's operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions, enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.

关键词： Lunar Navigation Onboard AI Visual Based Navigation

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FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model...

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

作者： Yang, Julia Barnett, Alina Jade Donnelly, Jon Kishore, Satvik Fang, Jerry Schwartz, Fides Regina Chen, Chaofan Lo, Joseph Y. Rudin, Cynthia Duke Univ Durham NC 27708 USA Brigham & Womens Hosp 75 Francis St Boston MA 02115 USA Univ Maine Orono ME USA

ISBN: (纸本)9798350365474

Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse-to fine-grained prototypes.

关键词： breast cancer cancer computer vision deep learning interpretability interpretable machine learning machine learning mammography medical imaging neural networks

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