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

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

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

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

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

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

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

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

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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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Hierarchical Clustering and Refinement for Generalized Multi-Camera Person Tracking

Hierarchical Clustering and Refinement for Generalized Multi...

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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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ConCon-Chi: Concept-Context Chimera Benchmark for Personalized vision-Language Tasks

ConCon-Chi: Concept-Context Chimera Benchmark for Personaliz...

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2024 ieee/CVF conference on computer vision and pattern recognition, CVPR 2024

作者： Rosasco, Andrea Berti, Stefano Pasquale, Giulia Malafronte, Damiano Sato, Shogo Segawa, Hiroyuki Inada, Tetsugo Natale, Lorenzo University of Genoa Italy Istituto Italiano di Tecnologia Italy Sony Interactive Entertainment Inc. Japan

ISBN: (纸本)9798350353006

While recent vision-Language (VL) models excel at open-vocabulary tasks, it is unclear how to use them with specific or uncommon concepts. Personalized Text-to-Image Retrieval (TIR) or Generation (TIG) are recently introduced tasks that represent this challenge, where the VL model has to learn a concept from few images and respectively discriminate or generate images of the target concept in arbitrary contexts. We identify the ability to learn new meanings and their compositionality with known ones as two key properties of a personalized system. We show that the available benchmarks offer a limited validation of personalized textual concept learning from images with respect to the above properties and introduce ConCon-Chi as a benchmark for both personalized TIR and TIG, designed to fill this gap. We modelled the new-meaning concepts by crafting chimeric objects and formulating a large, varied set of contexts where we photographed each object. To promote the compositionality assessment of the learned concepts with known contexts, we combined different contexts with the same concept, and vice-versa. We carry out a thorough evaluation of state-of-the-art methods on the resulting dataset. Our study suggests that future work on personalized TIR and TIG methods should focus on the above key properties, and we propose principles and a dataset for their performance assessment. Dataset: https://***/10.48557/QJ1166 and code: https://***/hsp-iit/concon-chi-benchmark. © 2024 ieee.

关键词： benchmark chimeric objects compositionality dataset personalization personalized text-to-image generation personalized text-to-image retrieval personalized vision-language models personalized vision-language tasks vision-language models vision-language tasks

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Proceedings - 2023 ieee/CVF conference on computer vision and pattern recognition, CVPR 2023

Proceedings - 2023 IEEE/CVF Conference on Computer Vision an...

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2023 ieee/CVF conference on computer vision and pattern recognition, CVPR 2023

ISBN: (纸本)9798350301298

The proceedings contain 2356 papers. The topics discussed include: exploring discontinuity for video frame interpolation;two-view geometry scoring without correspondences;language-guided audio-visual source separation via trimodal consistency;handwritten text generation from visual archetypes;Bayesian posterior approximation with stochastic ensembles;ERM-KTP: knowledge-level machine unlearning via knowledge transfer;PlenVDB: memory efficient VDB-based radiance fields for fast training and rendering;learning and aggregating lane graphs for urban automated driving;teaching matters: investigating the role of supervision in vision transformers;NeuralField-LDM: scene generation with hierarchical latent diffusion models;cut and learn for unsupervised object detection and instance segmentation;probabilistic debiasing of scene graphs;and unifying layout generation with a decoupled diffusion model.

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NTIRE 2022 Spectral Recovery Challenge and Data Set

NTIRE 2022 Spectral Recovery Challenge and Data Set

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

作者： Arad, Boaz Timofte, Radu Yahel, Rony Morag, Nimrod Bernat, Amir Cai, Yuanhao Lin, Jing Lin, Zudi Wang, Haoqian Zhang, Yulun Pfister, Hanspeter Van Gool, Luc Liu, Shuai Li, Yongqiang Feng, Chaoyu Lei, Lei Li, Jiaojiao Du, Songcheng Wu, Chaoxiong Leng, Yihong Song, Rui Zhang, Mingwei Song, Chongxing Zhao, Shuyi Lang, Zhiqiang Wei, Wei Zhang, Lei Dian, Renwei Shan, Tianci Guo, Anjing Feng, Chengguo Liu, Jinyang Agarla, Mirko Bianco, Simone Buzzelli, Marco Celona, Luigi Schettini, Raimondo He, Jiang Xiao, Yi Xiao, Jiajun Yuan, Qiangqiang Li, Jie Zhang, Liangpei Kwon, Taesung Ryu, Dohoon Bae, Hyokyoung Yang, Hao-Hsiang Chang, Hua-En Huang, Zhi-Kai Chen, Wei-Ting Kuo, Sy-Yen Chen, Junyu Li, Haiwei Liu, Song Uma, Sabarinathan K. Bama, B. Sathya Roomi, S. Mohamed Mansoor Oddity Tech Ltd New York NY USA Voyage81 Ltd New York NY USA Swiss Fed Inst Technol Computat Vis Lab Zurich Switzerland Univ Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Acad Coll Tel Aviv Yaffo Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Harvard Univ Visual Comp Grp Cambridge MA USA JMU Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Xiaomi Inc Beijing Peoples R China Xidian Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China Northwestern Polytech Univ Changan Campus Xian Peoples R China Hunan Univ Changsha Hunan Peoples R China Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Wuhan Univ Sch Geodesy & Geomat Wuhan Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Daejeon South Korea Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Peoples R China Couger Inc Tokyo Japan Thiagarajar Coll Engn Kamarajar Salai India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD 1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated JPEG-compressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams competing in the final testing phase, 12 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in spectral reconstruction from natural RGB images.

关键词： Training computer vision Costs conferences Neural networks Cameras pattern recognition

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Rethinking the Self-Attention in vision Transformers

Rethinking the Self-Attention in Vision Transformers

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

作者： Kim, Kyungmin Wu, Bichen Dai, Xiaoliang Zhang, Peizhao Yan, Zhicheng Vajda, Peter Kim, Seon Yonsei Univ Seoul South Korea Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet-1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while maintaining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.

关键词： computer vision conferences Computational modeling pattern recognition

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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...

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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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