Object detection, particularly open-vocabulary object detection, plays a crucial role in Earth sciences, such as environmental monitoring, natural disaster assessment, and land-use planning. However, existing open-vocabulary detectors, primarily trained on natural-world images, struggle to generalize to remote sensing images due to a significant data domain gap. Thus, this paper aims to advance the development of open-vocabulary object detection in remote sensing community. To achieve this, we first reformulate the task as Locate Anything on Earth (LAE) with the goal of detecting any novel concepts on Earth. We then developed the LAE-Label Engine which collects, auto-annotates, and unifies up to 10 remote sensing datasets creating the LAE-1M - the first large-scale remote sensing object detection dataset with broad category coverage. Using the LAE-1M, we further propose and train the novel LAE-DINO Model, the first open-vocabulary foundation object detector for the LAE task, featuring Dynamic Vocabulary Construction (DVC) and Visual-Guided Text Prompt Learning (VisGT) modules. DVC dynamically constructs vocabulary for each training batch, while VisGT maps visual features to semantic space, enhancing text features. We comprehensively conduct experiments on established remote sensing benchmark DIOR, DOTAv2.0, as well as our newly introduced 80-class LAE-80C benchmark. Results demonstrate the advantages of the LAE-1M dataset and the effectiveness of the LAE-DINO method.
USE: Universal Segment Embeddings for Open-Vocabulary Image Segmentation
Bosch Research North America（博世北美研究院）；Bosch Center for Artificial Intelligence (BCAI)（博世人工智能中心）；The Ohio State University（俄亥俄州立大学）；University of California Davis（加州大学戴维斯分校）
SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Information and Communications Engineering, Xi’an Jiaotong University（西安交通大学信息与通信工程学院）；Ministry of Education Key Lab of Intelligent Network Security, Xi’an, China（教育部智能网络安全重点实验室）；Pazhou Laboratory (Huangpu), Radarweg 29, Guangzhou, China（琶洲实验室（黄埔），雷达路29号，广州，中国）
SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)（多模态人工智能系统国家重点实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
University of Alberta（阿尔伯塔大学）；Tencent WeChat（腾讯微信）；Northwestern University（西北大学）；SUSTech（四川大学）；Yale University（耶鲁大学）；Utrecht University（乌得勒支大学）；Dalian University of Technology（大连理工大学）；SIAT, Chinese Academy of Sciences（中科院深圳先进技术研究院）
Open-Vocabulary High-Resolution 3D (OVHR3D) Data Segmentation and Annotation Framework
USC Institute for Creative Technologies（南加州大学创意技术研究所）；Arizona State University（亚利桑那州立大学）；Colorado School of Mines（科罗拉多矿业大学）；University of Southern California（南加州大学）
Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation
Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；Technical University of Munich (TUM)（慕尼黑工业大学）；Aalto University（阿尔托大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）
SparseSAM: Structured Sparsification of Activations in Segment Anything Models
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；College of Engineering & Computer Science, VinUniversity（Vin大学工程与计算机科学学院）；VinUni-Illinois Smart Health Center, VinUniversity（Vin大学-伊利诺伊智能健康中心）；DFKI；Max Planck Research School for Intelligent Systems (IMPRS-IS)（马克斯·普朗克智能系统研究学校）；University of Stuttgart（斯图加特大学）
The Hong Kong University of Science and Technology（香港科技大学）；International Digital Economy Academy (IDEA)（国际数字经济研究院（IDEA））；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Tsinghua University（清华大学）；Microsoft Research at Redmond（微软雷德蒙德研究院）
We present OpenSeeD, a simple Open-vocabulary Segmentation and Detection framework that jointly learns from different segmentation and detection datasets. To bridge the gap of vocabulary and annotation granularity, we first introduce a pre-trained text encoder to encode all the visual concepts in two tasks and learn a common semantic space for them. This gives us reasonably good results compared with the counterparts trained on segmentation task only. To further reconcile them, we locate two discrepancies: $i$) task discrepancy -- segmentation requires extracting masks for both foreground obje…
ZS-VCOS: Zero-Shot Video Camouflaged Object Segmentation By Optical Flow and Open Vocabulary Object Detection
Department of CMPS, University of British Columbia（计算机科学与软件工程系，不列颠哥伦比亚大学）；Group of Methane Emission Observation & Warning (MEOW) , Weathon Software（甲烷排放观测与预警小组，Weathon软件）
OVS-DINO: Open-Vocabulary Segmentation via Structure-Aligned SAM-DINO with Language Guidance
Tongji University（同济大学）；Hong Kong Polytechnic University（香港理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Chongqing University（重庆大学）
Domain Adaptation for Large-Vocabulary Object Detectors
Xidian University（西安电子科技大学）；Nanyang Technological University（南洋理工大学）；University of Technology Sydney（悉尼科技大学）；University of Chinese Academy of Sciences（中国科学院大学）
We present a novel approach to weakly supervised object detection. Instead of annotated images, our method only requires two short videos to learn to detect a new object: 1) a video of a moving object and 2) one or more "negative" videos of the scene without the object. The key idea of our algorithm is to train the object detector to produce physically plausible object motion when applied to the first video and to not detect anything in the second video. With this approach, our method learns to locate objects without any object location annotations. Once the model is trained, it performs objec…
A Training-Free Guess What Vision Language Model from Snippets to Open-Vocabulary Object Detection
Aerospace and Informatics Domain（航空航天与信息领域）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间智能信息处理国家级重点实验室）；School of Electronic（电子学院）
Frequency-Guided Spatial Adaptation for Camouflaged Object Detection
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（空天地一体化大数据应用技术国家工程实验室）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；School of Information and Technology, Northwest University（西北大学信息与技术学院）；Xi’an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation, Xijing University（西京学院智能康复人机融合控制技术西安市重点实验室）
Learn from Foundation Model: Fruit Detection Model without Manual Annotation
College of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学杭州全球科技创新中心）；Faculty of Science, National University of Singapore（新加坡国立大学科学学院）
