Titlebook: Image Analysis and Processing – ICIAP 2022; 21st International C Stan Sclaroff,Cosimo Distante,Federico Tombari Conference proceedings 2022

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Computationally Efficient Rehearsal for Online Continual Learningrained environments commonly found in online continual learning for image analysis. This work evaluates several rehearsal training strategies for continual online learning and proposes the combined use of a drift detector that decides on (a) when to train using data from the buffer and the online st

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Recurrent Vision Transformer for Solving Visual Reasoning Problemsobtained. In the end, this study can lay the basis for a deeper understanding of the role of attention and recurrent connections for solving visual abstract reasoning tasks. The code for reproducing our results is publicly available here: ..

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Case Study on the Use of the SafeML Approach in Training Autonomous Driving Vehiclesftware, based on the outdated training data, no longer responds adequately to the current field situation. In a previous research paper, we developed the SafeML approach with colleagues from the University of Hull, where datasets are compared for their statistical distance measures. In doing so, we

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User-Biased Food Recognition for Health Monitoringerapy. The information inferred from the users’ eating habits is then exploited to track and monitor the dietary habits of people involved in a smoke quitting protocol. Experimental results show that the proposed food recognition method outperforms the baseline model results on the FoodRec-50 datase

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Unsupervised Person Re-identification Based on Skeleton Joints Using Graph Convolutional Networkscting P identities and K instances (PK sampling) to generate pseudo-labels for the unlabeled data. By iteratively optimizing these modules, our model extracts robust spatial-temporal information that can alleviate the occlusion problem. We conduct experiments on two benchmarks: MARS and DukeMTMC-Vid

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