Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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Bottom-Up Transformer Reasoning Network for Text-Image Retrieval researches process images and text in one pipeline or are highly entangled, which is not practical and human-friendly in the real world. Moreover, the image regions extracted by Faster-RCNN are highly over-sampled in the image pipeline, which causes ambiguities for the extracted visual embeddings.

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Graph Attention Mixup Transformer for?Graph Classificationraph learning models have the challenge of overfitting because of the huge number of parameters compared to graph neural networks (GNNs). To address this issue, we propose an end-to-end regularized training scheme based on Mixup for graph Transformer models called Graph Attention Mixup Transformer (

Palter

Frequency Spectrum with?Multi-head Attention for?Face Forgery Detectionarious popular heavy-weight convolutional neural networks (CNNs) and vision transformers (ViTs) are developed with high accuracy. These methods have high computational costs and require large datasets for training. Their accuracy drops when tested on unseen datasets. To overcome these limitations, w

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Lihua Lu,Ruyang Li,Hui Wei,Yaqian Zhao,Rengang Li,Binqiang Wanggen Aspekte entlang des Software-Lebenszyklus.Stellt neben d.Lernen Sie mit diesem Buch, wie Sie Software erfolgreich als Medizinprodukt?entwickeln und zulassen.Die zunehmende Digitalisierung bringt enorme Fortschritte in der Medizin. Softwarel?sungen sind l?ngst ein integraler Bestandteil moderner

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Haoyang Ma,Hongyu Guoschritte in der Medizin. Softwarel?sungen sind l?ngst ein integraler Bestandteil moderner Medizintechnik. Gleichzeitig versch?rft sich die Gesetzgebung für die Entwicklung medizinischer Software zunehmend. Viele Software-Hersteller stehen deshalb vor der Herausforderung, die schnellen Entwicklungen