善变
发表于 2025-3-28 14:59:25
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
割公牛膨胀
发表于 2025-3-28 19:57:20
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
Circumscribe
发表于 2025-3-29 00:46:01
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gangrene
发表于 2025-3-29 06:10:46
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PANEL
发表于 2025-3-29 07:28:44
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
发表于 2025-3-29 11:58:40
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MUTE
发表于 2025-3-29 16:12:49
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
发表于 2025-3-29 23:06:47
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lethargy
发表于 2025-3-30 02:21:27
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
发表于 2025-3-30 04:23:12
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