Texture exists in lots of the products, such as wood, beef and compression tea. These abundant and stochastic texture patterns are significantly different between any two products. Unlike the traditional digital ID tracking, in this paper, we propose a novel approach for product traceability, which directly uses the natural texture of the product itself as the unique identifier. A texture identification based traceability system for Pu'er compression tea is developed to demonstrate the feasibility of the proposed solution. With tea-brick images collected from manufactures and individual users, a large-scale dataset has been formed to evaluate the performance of tea-brick texture verification and searching algorithm. The texture similarity approach with local feature extraction and matching achieves the verification accuracy of 99.6% and the top-1 searching accuracy of 98.9%, respectively.
Fine-Grained Face Swapping via Regional GAN Inversion
Tencent AI Lab（腾讯人工智能实验室）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；South China University of Technology（华南理工大学）
我们提出了一个新的范例高保真人脸交换，忠实地保留所需的微妙的几何形状和纹理细节。我们重新思考人脸交换的角度来看，细粒度的人脸编辑，\textit{i.e., ``editing for swapping'' (E4S)}，并提出了一个框架，是基于明确的解开的形状和纹理的面部组件。遵循E4S原则，我们的框架可以实现面部特征的全局和局部交换，以及控制用户指定的部分交换量。此外，E4S范例固有地能够通过面罩来处理面部遮挡。在我们的系统的核心在于一个新的区域GAN反演（RGI）方法，它允许明确的形状和纹理的解纠缠。它还允许在StyleGAN的潜在空间中执行人脸交换。具体来说，我们设计了一个多尺度的面具引导编码器，每个面部组件的纹理投影到区域风格代码。我们还设计了一个掩码引导的注入模块来操纵的特征图与样式代码。在此基础上，将人脸交换转化为一个简化的风格和掩码交换问题。与当前最先进的方法进行了大量的实验和比较，证明了我们的方法在保留纹理和形状细节以及处理高分辨率图像方面的优越性。项目页面为http://e4s2022.github.io
Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）
RFHNet: Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval
College of Computer Science and Artificial Intelligence, Ludong University（鲁东大学计算机科学与人工智能学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；South China University of Technology（华南理工大学）；The University of Hong Kong（香港大学）；Tencent AI Lab（腾讯AI实验室）
我们提出了一个简单而有效的架构，细粒度的视觉识别称为双线性卷积神经网络（B-CNN）。这些网络将图像表示为从两个CNN导出的特征的池化外积，并以非线性不变的方式捕获局部特征相互作用。B-CNN属于无序纹理表示类，但与之前的工作不同，它们可以以端到端的方式进行训练。我们最精确的模型在Caltech-UCSD鸟类[67]、NABirds [64]、FGVC飞机[42]和斯坦福汽车[33]数据集上分别获得了84.1%、79.4%、86.9%和91.3%的每图像准确度，并在NVIDIA Titan X GPU上以每秒30帧的速度运行。然后，我们对这些网络进行了系统的分析，并表明：（1）双线性特征是高度冗余的，可以在尺寸上减少一个数量级，而不会显着降低准确性;（2）对于其他图像分类任务也是有效的，如纹理和场景识别;（3）可以在ImageNet数据集上从头开始训练，从而在基线架构上提供一致的改进。最后，我们使用神经单元的顶部激活和基于梯度的反演技术在各种数据集上呈现这些模型的可视化。完整系统的源代码可在http://vis-www.cs.umass.edu/bcnn上获得。
FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models
Stanford University（斯坦福大学）；The Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）；S-Lab, NTU（南洋理工大学S-Lab实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；CPII under InnoHK（InnoHK下属CPII机构）
Southeast University（东南大学）；Advanced Ocean Institute of Southeast University（东南大学先进海洋研究院）；Northwestern Polytechnical University（西北工业大学）；Binjiang Institute of Artificial Intelligence, ZJUT（浙江工业大学滨江人工智能研究院）；Alibaba Group（阿里巴巴集团）
Mutual Modality Trust with Lightweight Reconstruction Regularization for Fine-grained Tire Pattern Recognition
School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications（西安邮电大学通信与信息工程学院）；School of Artificial Intelligence, Hainan Normal University（海南师范大学人工智能学院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能与光电学院）
DSAA: Dual-Stage Attribute Activation for Fine-grained Open Vocabulary Detection
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing E-Hualu Information Technology Co., Ltd.（北京亿华鲁信息技术有限公司）；State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China（通用人工智能国家重点实验室，BIGAI，北京，中国）
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
RWTH Aachen University（亚琛工业大学）；Iran University of Science and Technology（伊朗科技大学）；Universiti Kebangsaan（马来西亚国民大学）；University of Regensburg（雷根斯堡大学）；Shahid Beheshti University（沙希德·贝赫什提大学）
BeautyGRPO: Aesthetic Alignment for Face Retouching via Dynamic Path Guidance and Fine-Grained Preference Modeling
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, China（中山大学计算机科学与技术学院，深圳校区）；vivo BlueImage Lab, vivo Mobile Communication Co., Ltd., China（vivo蓝影实验室，vivo移动通信有限公司）
Structure-Aware Fine-Grained Gaussian Splatting for Expressive Avatar Reconstruction
School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（教育部新一代人工智能技术及其跨学科应用重点实验室（东南大学））；Purple Mountain Laboratories（紫金山实验室）；Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education（教育部区块链应用监管工程研究中心（东南大学））；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)（多模态人工智能系统全国重点实验室）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
HieraSurg: Hierarchy-Aware Diffusion Model for Surgical Video Generation
Chair for Computer Aided Medical Procedures (CAMP), TU Munich, Germany（计算机辅助医疗程序研究所（CAMP），慕尼黑技术大学，德国）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
DeCO: Discriminative Evidence Composition for Fine-Grained Dataset Distillation
University of Science and Technology of China（中国科学技术大学）；Hokkaido University（北海道大学）；The University of Queensland（昆士兰大学）；Shanghai AI Laboratory（上海人工智能实验室）；Dalian University of Technology（大连理工大学）
3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects
Roblox Corporation（罗布乐思公司）；Berkeley AI Research Lab & Department of Industrial Engineering and Operations Research, University of California, Berkeley（加州大学伯克利分校伯克利人工智能研究实验室及工业工程与运筹学系）；Computer Science Department, Stanford University（斯坦福大学计算机科学系）；Division of Biostatistics, University of California, Berkeley（加州大学伯克利分校生物统计学系）
