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MaskSim: Detection of synthetic images by masked spectrum similarity analysis

MaskSim: Detection of synthetic images by masked spectrum si...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yanhao Li Quentin Bammey Marina Gardella Tina Nikoukhah Jean-Michel Morel Miguel Colom Rafael Grompone Von Gioi Université Paris-Saclay ENS Paris-Saclay CNRS Centre Borelli France Instituto de Matemática Pura e Aplicada Rio de Janeiro Brazil Department of Mathematics City University of Hong Kong Kowloon Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic image generation methods have recently revolutionized the way in which visual content is created. This opens up creative opportunities but also presents challenges in preventing misinformation and crime. However, these methods leave traces in the Fourier spectrum that are invisible to humans, but can be detected by specialized tools. This paper describes a semi-white-box method for detecting synthetic images by revealing anomalous patterns in the spectral domain. Specifically, we train a mask to enhance the most discriminative frequencies and simultaneously train a reference pattern that resembles the patterns produced by a given generative method. The proposed method produces explainable results with state-of-the-art performances and highlights cues that can be used as forensic evidence. Code is available at https://***/li-yanhao/masksim.

关键词： Visualization computer vision Image coding Image synthesis Forensics conferences Transform coding

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Look, Listen, and Attack: Backdoor Attacks Against Video Action recognition

Look, Listen, and Attack: Backdoor Attacks Against Video Act...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hasan Abed Al Kader Hammoud Shuming Liu Mohammed Alkhrashi Fahad AlBalawi Bernard Ghanem KAUST SDAIA Taif University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean test images, yet persistently predicts an attacker-defined label for any sample in the presence of the backdoor trigger. Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain. In this work, we revisit the traditional backdoor threat model and incorporate additional video-related aspects to that model. We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically, leading to highly effective attacks in the video domain. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models, where we show that attacking a single modality is enough for achieving a high attack success rate.

关键词： Threat modeling computer vision Accuracy conferences Artificial neural networks pattern recognition

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uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories

uTRAND: Unsupervised Anomaly Detection in Traffic Trajectori...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Giacomo D’Amicantonio Egor Bondarau Peter H.N De With Eindhoven University of Technology Eindhoven Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications. This paper addresses two issues: the lack of labeled data and the difficulty of explaining the predictions of a neural network. To this end, we present a framework called uTRAND, that shifts the problem of anomalous trajectory prediction from the pixel space to a semantic-topological domain. The framework detects and tracks all types of traffic agents in bird’s-eye-view videos of traffic cameras mounted at an intersection. By conceptualizing the intersection as a patch-based graph, it is shown that the framework learns and models the normal behaviour of traffic agents without costly manual labeling. Furthermore, uTRAND allows to formulate simple rules to classify anomalous trajectories in a way suited for human interpretation. We show that uTRAND outperforms other state-of-the-art approaches on a dataset of anomalous trajectories collected in a real-world setting, while producing explainable detection results.

关键词： Accuracy Semantics Neural networks Layout Object detection Manuals Rendering (computer graphics)

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The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition

The 6th Affective Behavior Analysis in-the-wild (ABAW) Compe...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dimitrios Kollias Panagiotis Tzirakis Alan Cowen Stefanos Zafeiriou Irene Kotsia Alice Baird Chris Gagne Chunchang Shao Guanyu Hu Queen Mary University of London UK Hume AI USA Imperial College London UK Cogitat UK Xi’an Jiaotong University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with ieee CVPR 2024. The 6th ABAW Competition addresses contemporary challenges in understanding human emotions and behaviors, crucial for the development of human-centered technologies. In more detail, the Competition focuses on affect related benchmarking tasks and comprises of five sub-challenges: i) Valence-Arousal Estimation (the target is to estimate two continuous affect dimensions, valence and arousal), ii) Expression recognition (the target is to recognise between the mutually exclusive classes of the 7 basic expressions and ’other’), iii) Action Unit Detection (the target is to detect 12 action units), iv) Compound Expression recognition (the target is to recognise between the 7 mutually exclusive compound expression classes), and v) Emotional Mimicry Intensity Estimation (the target is to estimate six continuous emotion dimensions). In the paper, we present these Challenges, describe their respective datasets and challenge protocols (we outline the evaluation metrics) and present the baseline systems and top performing teams’ per Challenge, as well as their obtained performance. More information for the Competition can be found in: https://***/6th.

关键词： Measurement Emotion recognition computer vision Protocols Target recognition Databases conferences

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Learning Optimized Low-Light Image Enhancement for Edge vision Tasks

Learning Optimized Low-Light Image Enhancement for Edge Visi...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S M A Sharif Azamat Myrzabekov Nodirkhuja Khujaev Roman Tsoy Seongwan Kim Jaeho Lee Opt-AI Inc. LG Sciencepark Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-light image enhancement (LLIE) has a significant role in edge vision applications (EVA). Despite its widespread practicability, the existing LLIE methods are impractical due to their high computational costs. This study proposed a framework to learn optimized low-light image enhancement to tackle the limitations of existing enhancement methods for accelerating EVA. The proposed framework incorporates a lightweight and mobile-friendly deep network. We optimized our proposed model with INT8 precision with a post-training quantization strategy and deployed it on an edge device. The LLIE model has achieved over 199 frames per second (FPS) on a low-power edge board. Additionally, we evaluated the practicability of an optimized model for accelerating the vision application of an edge environment. The experimental results illustrate that our optimized method can significantly accelerate the performance of SOTA vision algorithms in challenging low-light conditions for numerous everyday vision tasks, including object detection and image registration.

关键词： Performance evaluation YOLO Training Image registration Quantization (signal) Image edge detection Hardware

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Complex Style Image Transformations for Domain Generalization in Medical Images

Complex Style Image Transformations for Domain Generalizatio...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nikolaos Spanos Anastasios Arsenos Paraskevi-Antonia Theofilou Paraskevi Tzouveli Athanasios Voulodimos Stefanos Kollias National Technical University of Athens Greece

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The absence of well-structured large datasets in medical computer vision results in decreased performance of automated systems and, especially, of deep learning models. Domain generalization techniques aim to approach unknown domains from a single data source. In this paper we introduce a novel framework, named CompStyle, which leverages style transfer and adversarial training, along with high-level input complexity augmentation to effectively expand the domain space and address unknown distributions. State-of-the-art style transfer methods depend on the existence of subdomains within the source dataset. However, this can lead to an inherent dataset bias in the image creation. Input-level augmentation can provide a solution to this problem by widening the domain space in the source dataset and boost performance on out-of-domain distributions. We provide results from experiments on semantic segmentation on prostate data and corruption robustness on cardiac data which demonstrate the effectiveness of our approach. Our method increases performance in both tasks, without added cost to training time or resources.

关键词： Training computer vision Soft sensors Semantics Transformers Robustness Complexity theory

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GazeCaps: Gaze Estimation with Self-Attention-Routed Capsules

GazeCaps: Gaze Estimation with Self-Attention-Routed Capsule...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Hengfei Oh, Jun O Jin Chang, Hyung Na, Jin Hee Tae, Minwoo Zhang, Zhongqun Choi, Sang-Il University of Birmingham United Kingdom Dankook University Korea Republic of VTouch Inc

ISBN: (纸本)9798350302493

Gaze estimation is the task of estimating eye gaze from facial features. People tend to infer gaze by considering different facial properties from the whole image and their relations. However, existing methods rarely consider these various properties. In this paper, we propose a novel GazeCaps framework that represents various facial properties as different capsules. The capsules respond sensitively to transforms of facial properties by vectorial expression, which is effective for gaze estimation in which many facial components are nonlinearly transformed according to the direction of the head in addition to the perspective. Furthermore, we propose a Self-Attention Routing (SAR) module which can dynamically allocate attention to different capsules that contain important information and can be optimized as a single process without iterations. Through rigorous experiments, we confirm that the proposed method achieves state-of-the-art performance on various benchmarks. We also detail the generalization performance of the proposed model through a cross-dataset evaluation. © 2023 ieee.

关键词： computer vision

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Online Multi-camera People Tracking with Spatial-temporal Mechanism and Anchor-feature Hierarchical Clustering

Online Multi-camera People Tracking with Spatial-temporal Me...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Riu Cherdchusakulchai Sasin Phimsiri Visarut Trairattanapa Suchat Tungjitnob Wasu Kudisthalert Pornprom Kiawjak Ek Thamwiwatthana Phawat Borisuitsawat Teepakorn Tosawadi Pakcheera Choppradit Kasisdis Mahakijdechachai Supawit Vatathanavaro Worawit Saetan Vasin Suttichaya AI and Robotics Ventures (ARV) Thailand

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-camera Multi-object tracking (MTMC) surpasses conventional single-camera tracking by enabling seamless object tracking across multiple camera views. This capability is critical for security systems and improving situational awareness in various environments. This paper proposes a novel MTMC framework designed for online operation. The framework employs a three-stage pipeline: Multiobject Tracking (MOT), Multi-target Multi-camera Tracking (MTMC), and Cross Interval Synchronization (CIS). In the MOT stage, ReID features are extracted and localized tracklets are created. MTMC links these tracklets across cameras using spatial-temporal constraints and constraint hierarchical clustering with anchor features for improved inter-camera association. Finally, CIS ensures the temporal coherence of tracklets across time intervals. The proposed framework achieves robust tracking performance, validated on the challenging 2024 AI City Challenge with a HOTA score of 51.0556%, ranking sixth. The code is available at: https://***/ARV-MLCORE/AIC2024Track1ARV

关键词： Urban areas Pipelines Cameras Feature extraction Real-time systems pattern recognition Synchronization

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Thermal Image Super-Resolution Challenge Results - PBVS 2024

Thermal Image Super-Resolution Challenge Results - PBVS 2024

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Rafael E. Rivadeneira Angel D. Sappa Chenyang Wang Junjun Jiang Zhiwei Zhong Peilin Chen Shiqi Wang Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper outlines the advancements and results of the Fifth Thermal Image Super-Resolution challenge, hosted at the Perception Beyond the Visible Spectrum CVPR 2024 workshop. The challenge employed a novel benchmark cross-spectral dataset consisting of 1000 thermal images, each paired with its corresponding registered RGB image. The challenge featured two tracks: Track-1 focused on Single Thermal Image Super-Resolution with an ×8 upscale factor, while Track-2 extended its evaluation to include both ×8 and ×16 scaling factors, utilizing high-resolution RGB images to guide the super-resolution process for low-resolution thermal images. The participation of over 175 teams highlights the research community’s strong engagement and dedication to enhancing image resolution techniques across both single and cross-spectral methodologies. This year’s challenge sets new benchmarks and provides valuable insights into future directions for research in thermal image super-resolution.

关键词： Image quality computer vision Thermal factors conferences Superresolution Benchmark testing Transformers

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Lifting Multi-View Detection and Tracking to the Bird’s Eye View

Lifting Multi-View Detection and Tracking to the Bird’s Eye...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Torben Teepe Philipp Wolters Johannes Gilg Fabian Herzog Gerhard Rigoll Technical University of Munich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Taking advantage of multi-view aggregation presents a promising solution to tackle challenges such as occlusion and missed detection in multi-object tracking and detection. Recent advancements in multi-view detection and 3D object recognition have significantly improved performance by strategically projecting all views onto the ground plane and conducting detection analysis from a Bird’s Eye View (BEV). In this paper, we compare modern lifting methods, both parameter-free and parameterized, to multi-view aggregation. Additionally, we present an architecture that aggregates the features of multiple times steps to learn robust detection and combines appearance-and motion-based cues for tracking. Most current tracking approaches either focus on pedestrians or vehicles. In our work, we combine both branches and add new challenges to multi-view detection with cross-scene setups. Our method generalizes to three public datasets across two domains: (1) pedestrian: Wildtrack and MultiviewX, and (2) roadside perception: Synthehicle, achieving state-of-the-art performance in detection and tracking. https://***/tteepe/TrackTacular.

关键词： computer vision Pedestrians Three-dimensional displays Tracking conferences computer architecture Feature extraction

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