Despite outstanding performance on public benchmarks, face recognition still suffers due to domain mismatch between training (source) and testing (target) data. Furthermore, these domains are not shared classes, which complicates domain adaptation. Since this is also a fine-grained classification problem which does not strictly follow the low-density separation principle, conventional domain adaptation approaches do not resolve these problems. In this paper, we formulate domain mismatch in face recognition as a style mismatch problem for which we propose two methods. First, we design a domain discriminator with human-level judgment to mine target-like images in the training data to mitigate the domain gap. Second, we extract style representations in low-level feature maps of the backbone model, and match the style distributions of the two domains to find a common style representation. Evaluations on verification and open-set and closed-set identification protocols show that both methods yield good improvements, and that performance is more robust if they are combined. Our approach is competitive with related work, and its effectiveness is verified in a practical application.
DSKC: Domain Style Modeling with Adaptive Knowledge Consolidation for Exemplar-free Lifelong Person Re-Identification
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences（机器人与智能系统国家重点实验室，沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Manufacturing Industrial Integrated Automation, Shenyang University（制造工业集成自动化重点实验室，沈阳大学）；Department of Statistics and Actuarial Science and the Institute of Data Science, The University of Hong Kong（统计与精算系及数据科学研究所，香港大学）
SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revision
ROSE Lab, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore（南洋理工大学罗思实验室，跨学科研究生项目，新加坡）；ROSE Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学罗思实验室，电子与电气工程学院，新加坡）；City University of Hong Kong, Hong Kong SAR（香港城市大学，香港特别行政区）；Shanghai Jiao Tong University, China（上海交通大学，中国）；Singapore University of Technology and Design, Singapore（新加坡科技设计大学，新加坡）；VinUniversity, Hanoi, Vietnam（越南文大学，河内，越南）
Beijing Institute of Technology（北京理工大学）；Baidu VIS（百度视觉技术部）
DETR-style detectors stand out amongst in-domain scenarios, but their properties in domain shift settings are under-explored. This paper aims to build a simple but effective baseline with a DETR-style detector on domain shift settings based on two findings. For one, mitigating the domain shift on the backbone and the decoder output features excels in getting favorable results. For another, advanced domain alignment methods in both parts further enhance the performance. Thus, we propose the Object-Aware Alignment (OAA) module and the Optimal Transport based Alignment (OTA) module to achieve com…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Domain generalization (DG) aims to learn a model on several source domains, hoping that the model can generalize well to unseen target domains. The distribution shift between domains contains the covariate shift and conditional shift, both of which the model must be able to handle for better generalizability. In this paper, a novel DG method is proposed to deal with the distribution shift via Visual Alignment and Uncertainty-guided belief Ensemble (VAUE). Specifically, for the covariate shift, a visual alignment module is designed to align the distribution of image style to a common empirical …
ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
School of Computer Science and Engineering, Northeastern University, Shenyang, China（计算机科学与工程学院，东北大学，沈阳，中国）；Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China（教育部医学图像智能计算重点实验室，东北大学，沈阳，中国）；National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China（工业智能与系统优化国家级前沿科学中心，沈阳，中国）；Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada（阿尔伯塔人工智能研究所，阿尔伯塔大学，埃德蒙顿，加拿大）
MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation
School of Computer Science, Wuhan University（武汉大学计算机学院）；University of Louisville（路易斯维尔大学）；School of Computer Science, Central China Normal University（华中师范大学计算机学院）
The Ohio State University（俄亥俄州立大学）；University of Vienna（维也纳大学）；Université Paris-Saclay（巴黎萨克雷大学）；University of Stuttgart（斯图加特大学）；University of Nottingham（诺丁汉大学）；Old Dominion University（老 dominion 大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation. DDR renders pseudo-document pages by modeling randomized textual and non-textual contents of interest, with user-defined layout and font styles to support joint learning of fine-grained classes. We demonstrate competitive results using our DDR approach to extract nine document classes from the benchmark CS-150 and papers published in two domains, namely annual meetings of Association fo…
