Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing最新文献_第3页

Analyzing EEG Data with Machine and Deep Learning: A Benchmark 用机器和深度学习分析脑电图数据:一个基准

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-03-18 DOI: 10.48550/arXiv.2203.10009

D. Avola, Marco Cascio, L. Cinque, Alessio Fagioli, G. Foresti, Marco Raoul Marini, D. Pannone

引用次数: 0

Learning video retrieval models with relevance-aware online mining 基于相关性感知在线挖掘的学习视频检索模型

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-03-16 DOI: 10.48550/arXiv.2203.08688

Alex Falcon, G. Serra, O. Lanz

{"title":"Learning video retrieval models with relevance-aware online mining","authors":"Alex Falcon, G. Serra, O. Lanz","doi":"10.48550/arXiv.2203.08688","DOIUrl":"https://doi.org/10.48550/arXiv.2203.08688","url":null,"abstract":"Due to the amount of videos and related captions uploaded every hour, deep learning-based solutions for cross-modal video retrieval are attracting more and more attention. A typical approach consists in learning a joint text-video embedding space, where the similarity of a video and its associated caption is maximized, whereas a lower similarity is enforced with all the other captions, called negatives. This approach assumes that only the video and caption pairs in the dataset are valid, but different captions positives may also describe its visual contents, hence some of them may be wrongly penalized. To address this shortcoming, we propose the Relevance-Aware Negatives and Positives mining (RANP) which, based on the semantics of the negatives, improves their selection while also increasing the similarity of other valid positives. We explore the influence of these techniques on two videotext datasets: EPIC-Kitchens-100 and MSR-VTT. By using the proposed techniques, we achieve considerable improvements in terms of nDCG and mAP, leading to state-of-the-art results, e.g. +5.3% nDCG and +3.0% mAP on EPIC-Kitchens-100. We share code and pretrained models at https://github.com/aranciokov/ranp.","PeriodicalId":74527,"journal":{"name":"Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing","volume":"26 1","pages":"182-194"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87366829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

MOBDrone: a Drone Video Dataset for Man OverBoard Rescue MOBDrone:用于人落水救援的无人机视频数据集

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-03-15 DOI: 10.48550/arXiv.2203.07973

Donato Cafarelli, Luca Ciampi, Lucia Vadicamo, C. Gennaro, A. Berton, M. Paterni, C. Benvenuti, M. Passera, F. Falchi

{"title":"MOBDrone: a Drone Video Dataset for Man OverBoard Rescue","authors":"Donato Cafarelli, Luca Ciampi, Lucia Vadicamo, C. Gennaro, A. Berton, M. Paterni, C. Benvenuti, M. Passera, F. Falchi","doi":"10.48550/arXiv.2203.07973","DOIUrl":"https://doi.org/10.48550/arXiv.2203.07973","url":null,"abstract":"Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.","PeriodicalId":74527,"journal":{"name":"Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing","volume":"49 1","pages":"633-644"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73565925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Decontextualized I3D ConvNet for ultra-distance runners performance analysis at a glance 去语境化的I3D ConvNet超长跑运动员的表现分析一目了然

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-03-13 DOI: 10.1007/978-3-031-06433-3_21

David Freire-Obregón, J. Lorenzo-Navarro, M. C. Santana

引用次数: 3

Improve Convolutional Neural Network Pruning by Maximizing Filter Variety 通过最大化滤波器种类来改进卷积神经网络剪枝

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-03-11 DOI: 10.48550/arXiv.2203.05807

Nathan Hubens, M. Mancas, B. Gosselin, Marius Preda, T. Zaharia

{"title":"Improve Convolutional Neural Network Pruning by Maximizing Filter Variety","authors":"Nathan Hubens, M. Mancas, B. Gosselin, Marius Preda, T. Zaharia","doi":"10.48550/arXiv.2203.05807","DOIUrl":"https://doi.org/10.48550/arXiv.2203.05807","url":null,"abstract":"Neural network pruning is a widely used strategy for reducing model storage and computing requirements. It allows to lower the complexity of the network by introducing sparsity in the weights. Because taking advantage of sparse matrices is still challenging, pruning is often performed in a structured way, i.e. removing entire convolution filters in the case of ConvNets, according to a chosen pruning criteria. Common pruning criteria, such as l1-norm or movement, usually do not consider the individual utility of filters, which may lead to: (1) the removal of filters exhibiting rare, thus important and discriminative behaviour, and (2) the retaining of filters with redundant information. In this paper, we present a technique solving those two issues, and which can be appended to any pruning criteria. This technique ensures that the criteria of selection focuses on redundant filters, while retaining the rare ones, thus maximizing the variety of remaining filters. The experimental results, carried out on different datasets (CIFAR-10, CIFAR-100 and CALTECH-101) and using different architectures (VGG-16 and ResNet-18) demonstrate that it is possible to achieve similar sparsity levels while maintaining a higher performance when appending our filter selection technique to pruning criteria. Moreover, we assess the quality of the found sparse sub-networks by applying the Lottery Ticket Hypothesis and find that the addition of our method allows to discover better performing tickets in most cases","PeriodicalId":74527,"journal":{"name":"Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing","volume":"73 1","pages":"379-390"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85743937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Avalanche RL: a Continual Reinforcement Learning Library Avalanche RL:一个持续强化学习库

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-02-28 DOI: 10.1007/978-3-031-06427-2_44

Nicolo Lucchesi, Antonio Carta, Vincenzo Lomonaco

引用次数: 4

StandardSim: A Synthetic Dataset For Retail Environments StandardSim:零售环境的合成数据集

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-02-04 DOI: 10.1007/978-3-031-06430-2_6

Cristina Mata, Nick Locascio, Mohammed Azeem Sheikh, Kenny Kihara, Daniel L. Fischetti

引用次数: 5

Learning Semantics for Visual Place Recognition through Multi-Scale Attention 通过多尺度注意学习视觉位置识别的语义

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-01-24 DOI: 10.1007/978-3-031-06430-2_38

Valerio Paolicelli, A. Tavera, Gabriele Berton, C. Masone, Barbara Caputo

引用次数: 8

A Robust and Efficient Overhead People Counting System for Retail Applications 一种稳健、高效的零售管理人员计数系统

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-06430-2_12

Antonio Greco, A. Saggese, Bruno Vento

引用次数: 0

3D Key-Points Estimation from Single-View RGB Images 单视图RGB图像的3D关键点估计

Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-06430-2_3

M. Zohaib, M. Taiana, Milind G. Padalkar, A. D. Bue

引用次数: 3