Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

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Yuemin Zheng,Jin Tao,Qinglin Sun,Jinshan Yang,Hao Sun,Mingwei Sun,Zengqiang Chenia.Provides workable ideas to attain goals of sustainable soThis curated book addresses, in the scholarly realm, the problems of soil degradation and provides some practical solutions for them to save soil life. It comprises ten specially invited chapters that address the global soil framework, soil

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莎草

Xiangrui Su,Qi Zhang,Chongyang Shi,Jiachang Liu,Liang Huo create even one inch of top soil. Due to anthropogenic and climatic factors, soils, especially in dryland regions (40% globally and 70% of India), are under serious threat of accelerated degradation and desertification. In India, 80% of the land mass is considered highly vulnerable to drought, flo

停止償付

Ao Jin,Zhichao Wu,Li Zhu,Qianchen Xia,Xin Yang, of the fundamental principles of soil mechanics. The understanding of these principles is considered to be an essential foundation upon which future practical experience in soils engineering can be built. The choice of material involves an element of personal opinion but the contents of this book

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intricacy

Text to?Image Generation with?Conformer-GANugh natural language text descriptions. Existing T2I models are mostly based on generative adversarial networks, but it is still very challenging to guarantee the semantic consistency between a given textual description and generated natural images. To address this problem, we propose a concise and

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MGFNet: A Multi-granularity Feature Fusion and?Mining Network for?Visible-Infrared Person Re-identif Existing works on retrieving pedestrians focus on mining the shared feature representations by the deep convolutional neural networks. However, there are limitations of single-granularity for identifying target pedestrians in complex VI-ReID tasks. In this study, we propose a new Multi-Granularity

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Carbon-Monoxide

Hi-Stega: A Hierarchical Linguistic Steganography Framework Combining Retrieval and?Generationf secret message, has been widely studied and applied. However, existing linguistic steganography methods ignore the correlation between social network texts, resulting in steganographic texts that are isolated units and prone to breakdowns in cognitive-imperceptibility. Moreover, the embedding capa

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Effi-Seg: Rethinking EfficientNet Architecture for?Real-Time Semantic Segmentations a feature extractor and replace the classification head with a decoder to generate segmented outputs. The advantage of this strategy is the ability to obtain a ready-made backbone with additional knowledge. However, there are several disadvantages, such as a lack of architectural knowledge, a sign