Quantization Errors of fGn and fBm Signals

In this Letter, we show that under the assumption of high resolution, the quantization errors of fGn and fBm signals with uniform quantizer can be treated as uncorrelated white noises

Sustainable Urban Living Environment Through Prefabricated Buildings

The endless destruction of the natural environment and man's unbridled demand for natural resources has led to the spread of a series of environmental problems such as global warming, air pollution and land desertification. These environmental problems not only greatly restrict the development of the economy and society, but also threaten the living environment of human beings. With the change in people's living standards, people's demand for the urban living environment is also updated, and the sustainable development of the urban living environment is also a very important issue today. Some studies show that the energy consumption of building construction accounts for 35%-40% of the total energy consumption, and the high energy consumption of the construction industry is a global problem. The trend in urban construction today is to use prefabricated buildings, also known as assembly buildings, to promote the upgrading and transformation of the construction industry. Energy-saving and emission-reducing prefabricated assembly buildings will also be a trend in the industry in the future, and in the long run, the energy-saving rate of assembled apartments will be evaluated so that the degree of efficiency can be fed back to the relevant authorities to judge whether the assembled homes meet the requirements of sustainable development

Sketch-to-Architecture: Generative AI-aided Architectural Design

Recently, the development of large-scale models has paved the way for various interdisciplinary research, including architecture. By using generative AI, we present a novel workflow that utilizes AI models to generate conceptual floorplans and 3D models from simple sketches, enabling rapid ideation and controlled generation of architectural renderings based on textual descriptions. Our work demonstrates the potential of generative AI in the architectural design process, pointing towards a new direction of computer-aided architectural design. Our project website is available at: https://zrealli.github.io/sketch2arcComment: Pacific Graphics 2023, accepted as Poste

LayerDiffusion: Layered Controlled Image Editing with Diffusion Models

Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining consistency between the subject and the background remains challenging. In this paper, we propose LayerDiffusion, a semantic-based layered controlled image editing method. Our method enables non-rigid editing and attribute modification of specific subjects while preserving their unique characteristics and seamlessly integrating them into new backgrounds. We leverage a large-scale text-to-image model and employ a layered controlled optimization strategy combined with layered diffusion training. During the diffusion process, an iterative guidance strategy is used to generate a final image that aligns with the textual description. Experimental results demonstrate the effectiveness of our method in generating highly coherent images that closely align with the given textual description. The edited images maintain a high similarity to the features of the input image and surpass the performance of current leading image editing methods. LayerDiffusion opens up new possibilities for controllable image editing.Comment: 17 pages, 14 figure

Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer Learning

Large pre-trained vision models achieve impressive success in computer vision. However, fully fine-tuning large models for downstream tasks, particularly in video understanding, can be prohibitively computationally expensive. Recent studies turn their focus towards efficient image-to-video transfer learning. Nevertheless, existing efficient fine-tuning methods lack attention to training memory usage and exploration of transferring a larger model to the video domain. In this paper, we present a novel Spatial-Temporal Side Network for memory-efficient fine-tuning large image models to video understanding, named Side4Video. Specifically, we introduce a lightweight spatial-temporal side network attached to the frozen vision model, which avoids the backpropagation through the heavy pre-trained model and utilizes multi-level spatial features from the original image model. Extremely memory-efficient architecture enables our method to reduce 75% memory usage than previous adapter-based methods. In this way, we can transfer a huge ViT-E (4.4B) for video understanding tasks which is 14x larger than ViT-L (304M). Our approach achieves remarkable performance on various video datasets across unimodal and cross-modal tasks (i.e., action recognition and text-video retrieval), especially in Something-Something V1&V2 (67.3% & 74.6%), Kinetics-400 (88.6%), MSR-VTT (52.3%), MSVD (56.1%) and VATEX (68.8%). We release our code at https://github.com/HJYao00/Side4Video.Comment: Technical repor