We present our method for transferring style from any arbitrary image(s) to
object(s) within a 3D scene. Our primary objective is to offer more control in
3D scene stylization, facilitating the creation of customizable and stylized
scene images from arbitrary viewpoints. To achieve this, we propose a novel
approach that incorporates nearest neighborhood-based loss, allowing for
flexible 3D scene reconstruction while effectively capturing intricate style
details and ensuring multi-view consistency.

我们提出了一种从任意图像向 3D 场景中的物体转移风格的方法，旨在通过最近邻损失的运用实现更加可定制和风格化的场景图像创作，并确保多视角一致性。

语义风格化辐射场

S2RF: Semantically Stylized Radiance Fields

This paper introduces a novel optimization framework for deep neural network
(DNN) hardware accelerators, enabling the rapid development of customized and
automated design flows. More specifically, our approach aims to automate the
selection and configuration of low-level optimization techniques, encompassing
DNN and FPGA low-level optimizations. We introduce novel optimization and
transformation tasks for building design-flow architectures, which are highly
customizable and flexible, thereby enhancing the performance and efficiency of
DNN accelerators. Our results demonstrate considerable reductions of up to 92\%
in DSP usage and 89\% in LUT usage for two networks, while maintaining accuracy
and eliminating the need for human effort or domain expertise. In comparison to
state-of-the-art approaches, our design achieves higher accuracy and utilizes
three times fewer DSP resources, underscoring the advantages of our proposed
framework.

本论文介绍了一种新的针对深度神经网络（DNN）硬件加速器的优化框架，它能够快速开发定制化和自动化的设计流程，同时具有高度可定制性和灵活性，通过引入新的优化和转换任务，不需要人类专业知识，可以在保持准确性的同时大幅度降低 DSP 和 LUT 使用，相较于现有技术，具有更高的精度和更少的 DSP 资源使用率。

MetaML：用于深度学习加速的可定制跨阶段设计流程自动化

MetaML: Automating Customizable Cross-Stage Design-Flow for Deep  Learning Acceleration

We introduce Deep500: the first customizable benchmarking infrastructure that
enables fair comparison of the plethora of deep learning frameworks,
algorithms, libraries, and techniques. The key idea behind Deep500 is its
modular design, where deep learning is factorized into four distinct levels:
operators, network processing, training, and distributed training. Our
evaluation illustrates that Deep500 is customizable (enables combining and
benchmarking different deep learning codes) and fair (uses carefully selected
metrics). Moreover, Deep500 is fast (incurs negligible overheads), verifiable
(offers infrastructure to analyze correctness), and reproducible. Finally, as
the first distributed and reproducible benchmarking system for deep learning,
Deep500 provides software infrastructure to utilize the most powerful
supercomputers for extreme-scale workloads.

Deep500 是第一个可定制的基准测试基础架构，能够公平比较深度学习框架、算法、库和技术。它具有可定制性，公平性，速度快，可验证性，可重复性，并提供使用最强大的超级计算机进行极大规模工作负载的软件基础设施。