Real-world object detection must operate in evolving environments where new classes emerge, domains shift, and unseen objects must be identified as "unknown": all without accessing prior data. We introduce Evolving World Object Detection (EWOD), a paradigm coupling incremental learning, domain adaptation, and unknown detection under exemplar-free constraints. To tackle EWOD, we propose EW-DETR framework that augments DETR-based detectors with three synergistic modules: Incremental LoRA Adapters for exemplar-free incremental learning under evolving domains; a Query-Norm Objectness Adapter that decouples objectness-aware features from DETR decoder queries; and Entropy-Aware Unknown Mixing for calibrated unknown detection. This framework generalises across DETR-based detectors, enabling state-of-the-art RF-DETR to operate effectively in evolving-world settings. We also introduce FOGS (Forgetting, Openness, Generalisation Score) to holistically evaluate performance across these dimensions. Extensive experiments on Pascal Series and Diverse Weather benchmarks show EW-DETR outperforms other methods, improving FOGS by 57.24%.
National University of Singapore（新加坡国立大学）；Harbin Institute of Technology（哈尔滨工业大学）
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes. Most related prior works are on incremental object detection that rely on the availability of abundant training samples per novel class that substantially limits the scalability to real-world setting where novel data can be scarce. In this paper, we propose the Incremental-DETR that does incremental few-shot object detection via fine-tuning and self-supervised learning on the DETR object detector. To alleviate sever…
Inception Institute of Artificial Intelligence（Inception人工智能研究院）；IIT Hyderabad（印度理工学院海得拉巴分校）；Australian National University（澳大利亚国立大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Linköping University（林雪平大学）；University of Central Florida（中佛罗里达大学）
Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-…
National University of Singapore（新加坡国立大学）；Harbin Institute of Technology（哈尔滨工业大学）
Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision. Furthermore, the exact classes of the unknown objects must be identified without catastrophic forgetting of the previous known classes when the corresponding annotations of unknown objects are given incrementally. In this paper, we propose a two-stage training approach named Open World DETR for open world object detection based on Deformable DETR. In the first stage, we pre-train a model on the current annotated data to detect objects…
Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection
Institute of Automation, Chinese Academy of Sciences (CAS)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Science, UCAS（中国科学院大学人工智能学院）；Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属第六人民医院）
RT-DETRv4: Painlessly Furthering Real-Time Object Detection with Vision Foundation Models
School of Electronic and Computer Engineering, Peking University, Shenzhen, China（北京大学电子与计算机工程学院，深圳，中国）；Department of Automation and BNRist, Tsinghua University, Beijing, China（清华大学自动化系及BNRist，北京，中国）
Exploiting Stability-Plasticity Asymmetry in Pretrained Detectors for Incremental Object Detection
Harbin Institute of Technology（哈尔滨工业大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）
OV-DEIM: Real-time DETR-Style Open-Vocabulary Object Detection with GridSynthetic Augmentation
Intellindust AI Lab（Intellindust AI实验室）；Guangdong Laboratory of Artificial Intelligence（广东人工智能实验室）；College of Computer Science（计算机科学学院）；Software Engineering, Shenzhen University, China（软件工程，深圳大学，中国）；School of Information Technology, Carleton University, Canada（信息科技学院，卡尔顿大学，加拿大）；Institute for Research in Biomedicine, Bellinzona, Switzerland（生物医学研究 institute，贝尔林扎，瑞士）
LP-DETR: Layer-wise Progressive Relations for Object Detection
New York University（纽约大学）；University of Pittsburgh（匹兹堡大学）；Fordham University（福特汉姆大学）；Georgia Institute of Technology（佐治亚理工学院）；California Institute of Technology（加州理工学院）
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉成像工程技术研究中心）
Incremental learning of semantic segmentation has emerged as a promising strategy for visual scene interpretation in the open- world setting. However, it remains challenging to acquire novel classes in an online fashion for the segmentation task, mainly due to its continuously-evolving semantic label space, partial pixelwise ground-truth annotations, and constrained data availability. To ad- dress this, we propose an incremental learning strategy that can fast adapt deep segmentation models without catastrophic forgetting, using a streaming input data with pixel annotations on the novel classe…
Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation
Department of Computer Science, University of Freiburg（弗赖堡大学计算机科学系）；Zuse School ELIZA（Zuse学校ELIZA）；NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal（神经Poly实验室，生物医学工程学院，蒙特利尔理工学院）；Mila - Québec AI Institute（魁北克人工智能研究所）；Collaborative Research Institute Intelligent Oncology (CRIION)（智能肿瘤学协作研究机构）
PIGEONS: Incremental Long-Video Captioning with Persistent Identity Gallery and Evolving Overarching Narrative Summary
Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；The Hong Kong University of Science and Technology（香港科技大学）；Peking University（北京大学）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Nanjing University（南京大学）
DFKI – German Research Center for Artificial Intelligence（德国人工智能研究中心）；ZF Friedrichshafen AG（采埃孚腓特烈港股份公司）；RPTU – University of Kaiserslautern-Landau（凯泽斯劳滕-兰道莱茵兰-普法尔茨技术大学）
Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization
Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed大学人工智能学院）；University of Trento（特伦托大学）；Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；South China University of Technology（华南理工大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Institute of Big Data, Fudan University（复旦大学大数据研究院）；Shanghai Jiao Tong University（上海交通大学）
Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for many real-world applications. In this work, we shed light on the causes of this well-known yet unsolved phenomenon - often referred to as catastrophic forgetting - in a class-incremental setup. We show that a combination of simple components and a loss that balances intra-task and inter-task learning can already resolve forgetting to the same extent as more complex measures propose…
Fudan University（复旦大学）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高等研究院）；University of Washington（华盛顿大学）
We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition…
InfoDense: Density-Aware Regional Decisive Replay for Memory-Efficient Incremental Face Forgery Detection
Huazhong Agricultural University（华中农业大学）；Peking University（北京大学）；National University of Singapore（新加坡国立大学）；Nantong University（南通大学）；Wuhan University（武汉大学）
Multi-Scale Deformable Transformers for Student Learning Behavior Detection in Smart Classroom
Central China Normal University（华中师范大学）；CCNU Wollongong Joint Institute, Central China Normal University（华中师范大学伍伦贡联合研究院）；Hubei University of Technology（湖北工业大学）
Steering Vision-Language Pre-trained Models for Incremental Face Presentation Attack Detection
School of Computer Science, China University of Geosciences（中国地质大学（北京）计算机科学学院）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences (CAS)（中国科学院人工智能安全重点实验室）；University of China Academy of Sciences（中国科学院大学）；Center for Machine Vision and Signal Analysis, University of Oulu（奥卢大学机器视觉与信号分析中心）；Microsoft Research Asia（微软亚洲研究院）
An Incremental Unified Framework for Small Defect Inspection
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Chinese University of Hong Kong（香港中文大学）；SmartMore Corporation（思谋科技公司）；Hong Kong Industrial Artificial Intelligence & Robotics Centre（香港工业人工智能及机器人中心）；Hong Kong Productivity Council（香港生产力促进局）
Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
College of Artificial Intelligence, Xi’an Jiaotong University（西安交通大学人工智能学院）；Shenzhen University of Advanced Technology（深圳先进技术大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）
