Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

江湖骗子

EXALT

Terrace

https://doi.org/10.1007/978-3-663-15999-5 To address this issue, we propose a pyramid enhanced network (PENet) and joint it with YOLOv3 to build a dark object detection framework named PE-YOLO. Firstly, PENet decomposes the image into four components of different resolutions using the Laplacian pyramid. Specifically we propose a detail pro

CANT

,Grundlagen der Elastizitätstheorie,to another one often faces performance degradation due to the domain shift problem. To improve the generalization ability of object detectors, the majority of existing domain adaptation methods alleviate the domain bias either on the feature encoder or instance classifier by adversarial learning. Di

一大块

Rudolf Stark (Ao. Univ.-Prof. Dipl.-Ing.) agents for years. However, Radar-based perception suffers from its unintuitive sensing data, which lack of semantic and structural information of scenes. To tackle this problem, camera and Radar sensor fusion has been investigated as a trending strategy with low cost, high reliability and strong ma

prick-test

,Grundlagen der Elastizitätstheorie,odels. To address these challenges, we propose SDGC-YOLOv5, a novel model based on YOLOv5. Our contributions are as follows: a) Replacement of the original convolution layer with spatial depth convolution (SD) to enable dense feature extraction and improve the detection of small objects. b) Utilizat

arthrodesis

harangue

PHIL

inscribe

https://doi.org/10.1007/978-3-031-44195-0artificial neural networks (NN); machine learning; deep learning; federated learning; convolutional neur