Deep neural networks are typically trained in a single shot for a specific task and data distribution, but in real world settings both the task and the domain of application can change. The problem becomes even more challenging in dense predictive tasks, such as semantic segmentation, and furthermore most approaches tackle the two problems separately. In this paper we introduce the novel task of coarse-to-fine learning of semantic segmentation architectures in presence of domain shift. We consider subsequent learning stages progressively refining the task at the semantic level; i.e., the finer set of semantic labels at each learning step is hierarchically derived from the coarser set of the previous step. We propose a new approach (CCDA) to tackle this scenario. First, we employ the maximum squares loss to align source and target domains and, at the same time, to balance the gradients between well-classified and harder samples. Second, we introduce a novel coarse-to-fine knowledge distillation constraint to transfer network capabilities acquired on a coarser set of labels to a set of finer labels. Finally, we design a coarse-to-fine weight initialization rule to spread the importance from each coarse class to the respective finer classes. To evaluate our approach, we design two benchmarks where source knowledge is extracted from the GTA5 dataset and it is transferred to either the Cityscapes or the IDD datasets, and we show how it outperforms the main competitors.
Continual Model Merging with Test-Time Adaptation for Whole-Slide Image Analysis
University of Information Technology（信息技术大学）；Viet Nam National University Ho Chi Minh City（胡志明市越南国立大学）；ETIS (UMR 8051), CY Cergy Paris University, ENSEA, CNRS, France（法国赛尔齐巴黎大学、ENSEA、法国国家科学研究中心的ETIS（UMR 8051））
Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation
National Key Laboratory for Multimedia Information Processing（多媒体信息处理国家重点实验室）；School of Computer Science（计算机学院）；Peking University（北京大学）；CUHK（香港中文大学）；University of Science Technology of China（中国科学技术大学）
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation
Peking University（北京大学）；South China University of Technology（华南理工大学）；National University of Singapore（新加坡国立大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；LIVIA ETS Montreal, ILLS International Laboratory on Learning Systems (ILLS)（蒙特利尔LIVIA ETS，学习系统国际实验室（ILLS））；Tulane University（路易斯安那州立大学）；Nanyang Technological University（南洋理工大学）；Hanyang University（翰阳大学）
ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation
National Key Laboratory for Multimedia Information Processing（多媒体信息处理国家重点实验室）；The Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology（香港科技大学）；School of Computer Science Peking University（北京大学计算机学院）；AIRobotics
Enhancing Plasticity for First Session Adaptation Continual Learning
LTCI, Télécom-Paris, Institut Polytechnique de Paris（Télécom-Paris 工程院）；University of Bergamo（贝加莫大学）；Inria at University Grenoble Alpes, LJK（格勒诺布尔阿尔卑斯大学）
Continual Learning for Segment Anything Model Adaptation
Southern University of Science and Technology（南方科技大学）；City University of Hong Kong（香港城市大学）；Hong Kong University of Science and Technology（香港科技大学）；Tencent YouTu Lab（腾讯优图实验室）
针对动态流式数据场景，提出持续 SAM 适应基准 CoSAM 和域适配器混合算法 MoDA，通过全局特征与辅助令牌提取分离特征，实现持续分割并保持知识，优于现有方法。
Adaptive and Balanced Re-initialization for Long-timescale Continual Test-time Domain Adaptation
Hong Kong Polytechnic University（香港理工大学）；Eastern Institute for Advanced Study（东方高级研究所）；Ant Group（蚂蚁集团）；Australian National University（澳大利亚国立大学）；CSIRO（澳大利亚科学院）；The University of Hong Kong（香港大学）
Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery
IDEAS NCBR；Warsaw University of Technology（华沙理工大学）；Gdańsk University of Technology（格但斯克理工大学）；Tooploox；Autonomous University of Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）
Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation
National Key Laboratory for Multimedia Information Processing, School of CS, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；University of Washington（华盛顿大学）；Baidu Inc.（百度公司）
Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation
Peking University（北京大学）；Huawei Technologies（华为技术有限公司）；Wuhan University（武汉大学）；The Chinese University of Hong Kong（香港中文大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation
Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；Shenzhen University of Advanced Technology（深圳先进技术学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Science（中国科学院深圳先进技术研究院）
