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

善變

https://doi.org/10.1007/978-3-662-40224-5ce, they tend to focus on specific artifacts and lead to overfitting. Erasing-based augmentations can alleviate this issue, but they still suffer from high randomness and fixed shapes. Therefore, we propose a novel face masking method named Landmarks Based Erasing (LBE), which exploits the geometric

割公牛膨脹

https://doi.org/10.1007/978-3-662-40224-5toring, and other fields. To obtain clear and haze free images, the paper proposes a dehazing network based on serial feature attention. The network adaptively captures the inter-dependency between features from channel and spatial perspectives, respectively, learns the weights of features, and uses

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gangrene

PANEL

https://doi.org/10.1007/978-3-662-38004-8increased. The development of efficient no-reference video quality assessment (NR-VQA) models for UGC with these features is a challenging task. Although previous studies have proposed solutions that combine multi-scale spatial and multi-rate motion information, existing NR-VQA models simply connect

Paleontology

MUTE

Zug-, Druck- und Scherfestigkeit, main difficulties in feature learning has been the problem of posterior collapse in variational inference. This paper proposes a hierarchical aggregated vector-quantized variational autoencoder, called TransVQ-VAE. Firstly, the multi-scale feature information based on the hierarchical Transformer i

梯田

lethargy

Zug-, Druck- und Scherfestigkeit,ity to downstream tasks. Therefore, this article proposes an unsupervised shape enhancement and decomposition machine network for 3D facial reconstruction. Specifically, we design a shape enhancement network, further combining global and local features, which can restore more complete and realistic

conference

Zug-, Druck- und Scherfestigkeit,crepancy. Existing methods mainly focus on bridging the relation between modalities by shared representation learning in the common embedding space. However, due to the outliers, these methods often struggle to build compact clustering subspaces. Besides, these methods also suffer from modality imba