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

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,LGF,: Local and Global Feature Fusion for Text-Guided Object Detection, leverages the multimodal information of both the image and text. However, classical object detection approaches only utilize the image modality to distinguish objects, neglecting the text modality. While many Vision-Language Models have been explored in the object detection task, they often require

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聋子

octogenarian

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Torrid

ROFusion: Efficient Object Detection Using Hybrid Point-Wise Radar-Optical Fusion, 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

投票

,SDGC-YOLOv5: A More Accurate Model for Small Object Detection,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

MONY

,The Statistical Characteristics of P3a and P3b Subcomponents in Electroencephalography Signals,computer interface (BCI), and cognitive neuroscience research. Comprehensive documentation of P300 is needed to support research growth in those areas, especially for P300 subcomponents, P3a and P3b, which commonly used to quantify EEG characteristic. Therefore, this study aims to explore the quanti

安慰

,Transforming Limitations into Advantages: Improving Small Object Detection Accuracy with SC-Attenti in small object detection needs to be improved. This paper proposes the SC-AttentionIoU loss function to stress the issue. Due to the less features of small objects, SC-AttentionIoU introduces attention within the true bounding box, allowing the existing detection models to focus on the critical fe

CHARM

,Literaturverzeichnis für Verbundbauweise,ove identification accuracy using novel shallow feature extraction strategies. First of all, a detection head corresponding to shallow features is used to replace the original detection head corresponding to the deepest features in YOLOv5. Secondly, an attention module is directly added to the outpu