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M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset

M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dat...

引用

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

作者： Chaney, Kenneth Cladera, Fernando Wang, Ziyun Bisulco, Anthony Hsieh, M. Ani Korpela, Christopher Kumar, Vijay Taylor, Camillo J. Daniilidis, Kostas University of Pennsylvania GRASP Laboratory PA19104 United States Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States

ISBN: (纸本)9798350302493

We present M3ED, the first multi-sensor event camera dataset focused on high-speed dynamic motions in robotics applications. M3ED provides high-quality synchronized and labeled data from multiple platforms, including ground vehicles, legged robots, and aerial robots, operating in challenging conditions such as driving along off-road trails, navigating through dense forests, and performing aggressive flight maneuvers. Our dataset also covers demanding operational scenarios for event cameras, such as scenes with high egomotion and multiple independently moving objects. The sensor suite used to collect M3ED includes high-resolution stereo event cameras (1280×720), grayscale imagers, an RGB imager, a high-quality IMU, a 64-beam LiDAR, and RTK localization. This dataset aims to accelerate the development of event-based algorithms and methods for edge cases encountered by autonomous systems in dynamic environments. © 2023 ieee.

关键词： Cameras

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MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks

MTN: Forensic Analysis of MP4 Video Files Using Graph Neural...

引用

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

作者： Xiang, Ziyue Singh Yadav, Amit Kumar Bestagini, Paolo Tubaro, Stefano Delp, Edward J. School of Electrical and Computer Engineering West LafayetteIN United States Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy

ISBN: (纸本)9798350302493

MP4 video files are stored using a tree data structure. These trees contain rich information that can be used for forensic analysis. In this paper, we propose MP4 Tree Network (MTN), an approach based on an end-to-end Graph Neural Networks (GNNs) that is used for forensic analysis of MP4 trees. MTN does not use any video pixel data. MTN is trained using Self-Supervised Learning (SSL), which generates semantic-preserving node embeddings for the nodes in an MP4 tree. We also propose a data augmentation technique for MP4 trees, which helps train MTN in data-scarce scenarios. MTN achieves good performance across 3 video forensics tasks on the EVA-7K dataset. We show that MTN can gain more comprehensive understanding about the MP4 trees and is more robust to potential attacks compared to existing methods. © 2023 ieee.

关键词： Trees (mathematics)

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Tracked-Vehicle Retrieval by Natural Language Descriptions with Multi-Contextual Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions w...

引用

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

作者： Le, Huy Dinh-Anh Qui-Vinh Nguyen, Quang Luu, Duc Trung Thi-Thanh Chau, Truc Chung, Nhat Minh Viet-Uyen Ha, Synh International University School of Computer Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350302493

This paper introduces our solution for Track 2 in AI City Challenge 2023. The task is tracked-vehicle retrieval by natural language descriptions with a real-world dataset of various scenarios and cameras. Our solution mainly focuses on four points: (1) To address the linguistic ambiguity in the language query, we leverage our proposed standardized version for text descriptions for the domain-adaptive training and post-processing stage. (2) Our baseline vehicle retrieval model utilizes CLIP to extract robust visual and textual feature representations to learn the unified cross-modal representations between textual and visual features. (3) Our proposed semi-supervised domain adaptive (SSDA) training method is leveraged to address the domain gap between the train and test set. (4) Finally, we propose a multi-contextual post-processing technique that prunes out the wrong results based on multi-contextual attributes information that effectively boosts the final retrieval results. Our proposed framework has yielded a competitive performance of 82.63% MRR accuracy on the test set, achieving 1st place in the competition. Codes will be available at https://***/zef1611/AIC23-NLRetrieval-HCMIU-CVIP © 2023 ieee.

关键词： Tracked vehicles

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Efficient CNN Architecture for Multi-modal Aerial View Object Classification

Efficient CNN Architecture for Multi-modal Aerial View Objec...

引用

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

作者： Miron, Casian Pasarica, Alexandru Timofte, Radu Gheorghe Asachi Tech Univ MCC Resources SRL Iasi Romania

ISBN: (纸本)9781665448994

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

关键词： Training computer vision Target recognition conferences Systems architecture Graphics processing units computer architecture

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Hierarchical Clustering and Refinement for Generalized Multi-Camera Person Tracking

Hierarchical Clustering and Refinement for Generalized Multi...

引用

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

作者： Li, Zongyi Wang, Runsheng Li, He Wei, Bohao Shi, Yuxuan Ling, Hefei Chen, Jiazhong Liu, Boyuan Li, Zhongyang Zheng, Hanqing Huazhong University of Science and Technology Department of Computer Science and Technology China

ISBN: (纸本)9798350302493

Multi-camera person tracking has gained significant attention in recent times, owing to its widespread application in surveillance scenarios. However, this task is challenging due to the variance viewpoints, heavy occlusion, and illumination changes. In order to tackle these challenges, we propose a novel Hierarchical Clustering and Refinement framework for Generalized Multi-Camera Person Tracking. Specifically, our framework comprises two main components: hierarchical clustering and hierarchical refinement. Compared with directly clustering tracklets among multiple cameras, our hierarchical clustering strategy can progressively assign tracklets to correct targets. Nevertheless, the clustering and tracking process would inevitably produce incorrect matchings. Therefore, a hierarchical refinement strategy is proposed to reduce these incorrect matches which includes: intra-camera tracklet level refinement, appearance refinement, spatial-temporal refinement, and face refinement. Extensive experiments show the effectiveness of our method, which achieves 92% IDF1 in 2023 AI CITY CHALLENGE track1, ranking 5th on the leaderboard. © 2023 ieee.

关键词： Security systems

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FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

FewSOME: One-Class Few Shot Anomaly Detection with Siamese N...

引用

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

作者： Belton, Niamh Hagos, Misgina Tsighe Lawlor, Aonghus Curran, Kathleen M. Science Foundation Ireland Centre for Research Training in Machine Learning Ireland University College Dublin School of Medicine Ireland University College Dublin School of Computer Science Ireland University College Dublin Insight Centre for Data Analytics Dublin Ireland

ISBN: (纸本)9798350302493

Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples are available for training. We propose 'Few Shot anOMaly dEtection' (FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to accurately detect anomalies having trained on 'few' examples of the normal class and no examples of the anomalous class. We describe FewSOME to be of low complexity given its low data requirement and short training time. FewSOME is aided by pretrained weights with an architecture based on Siamese Networks. By means of an ablation study, we demonstrate how our proposed loss, 'Stop Loss', improves the robustness of FewSOME. Our experiments demonstrate that FewSOME performs at stateof-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on. Moreover, our experiments show FewSOME to be robust to contaminated datasets. We also report F1 score and balanced accuracy in addition to AUC as a benchmark for future techniques to be compared against. Code available;https://***/niamhbelton/FewSOME. © 2023 ieee.

关键词： Anomaly detection

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An Investigation into the Impact of AI-Powered Image Enhancement on Forensic Facial recognition

An Investigation into the Impact of AI-Powered Image Enhance...

引用

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

作者： Justin Norman Hany Farid University of California Berkeley Berkeley CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Advances in machine learning and computer vision have led to significant improvements in automated facial recognition. Many real-world forensic settings, however, are confronted with challenging low-quality and low-resolution images that often confound even state-of-the art facial recognition. We investigate if and when advances in neural-based image enhancement and restoration can be used to restore degraded images while preserving facial identity for use in forensic facial recognition.

关键词： computer vision Art Face recognition Forensics conferences Machine learning Image restoration

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Tilted Cross-Entropy (TCE): Promoting Fairness in Semantic Segmentation

Tilted Cross-Entropy (TCE): Promoting Fairness in Semantic S...

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

作者： Szabo, Attila Jamali-Rad, Hadi Mannava, Siva-Datta Shell Global Solut Int BV Amsterdam Netherlands Delft Univ Technol TU Delft Delft Netherlands

ISBN: (纸本)9781665448994

Traditional empirical risk minimization (ERM) for semantic segmentation can disproportionately advantage or disadvantage certain target classes in favor of an (unfair but) improved overall performance. Inspired by the recently introduced tilted ERM (TERM), we propose tilted cross-entropy (TCE) loss and adapt it to the semantic segmentation setting to minimize performance disparity among target classes and promote fairness. Through quantitative and qualitative performance analyses, we demonstrate that the proposed Stochastic TCE for semantic segmentation can offer improved overall fairness by efficiently minimizing the performance disparity among the target classes of Cityscapes.

关键词： computer vision conferences Semantics Performance analysis pattern recognition Risk management

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Explainable Noisy Label Flipping for Multi-Label Fashion Image Classification

Explainable Noisy Label Flipping for Multi-Label Fashion Ima...

引用

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

作者： Ferreira, Beatriz Quintino Costeira, Joao P. Gomes, Joao P. Univ Lisbon ISR IST Lisbon Portugal

ISBN: (纸本)9781665448994

In online shopping applications, the daily insertion of new products requires an overwhelming annotation effort. Usually done by humans, it comes at a huge cost and yet generates high rates of noisy/missing labels that seriously hinder the effectiveness of CNNs in multi-label classification. We propose SELF-ML, a classification framework that exploits the relation between visual attributes and appearance together with the "low-rank" nature of the feature space. It learns a sparse reconstruction of image features as a convex combination of very few images - a basis - that are correctly annotated. Building on this representation, SELF-ML has a module that relabels noisy annotations from the derived combination of the clean data. Due to such structured reconstruction, SELF-ML gives an explanation of its label-flipping decisions. Experiments on a real-world shopping dataset show that SELF-ML significantly increases the number of correct labels even with few clean annotations.

关键词： Visualization computer vision Annotations conferences Buildings pattern recognition Noise measurement

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I Find Your Lack of Uncertainty in computer vision Disturbing

I Find Your Lack of Uncertainty in Computer Vision Disturbin...

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

作者： Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence Robert Hooke Str 1 D-28359 Bremen Germany

ISBN: (纸本)9781665448994

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

关键词： computer vision Uncertainty Computational modeling conferences Neural networks pattern recognition

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