While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existing FSOD systems follow FSC approaches, ignoring critical issues such as spatial variability and uncertain representations, and consequently result in low performance. Observing this, we propose a novel \textbf{Dual-Awareness Attention (DAnA)} mechanism that enables networks to adaptively interpret the given support images. DAnA transforms support images into \textbf{query-position-aware} (QPA) features, guiding detection networks precisely by assigning customized support information to each local region of the query. In addition, the proposed DAnA component is flexible and adaptable to multiple existing object detection frameworks. By adopting DAnA, conventional object detection networks, Faster R-CNN and RetinaNet, which are not designed explicitly for few-shot learning, reach state-of-the-art performance in FSOD tasks. In comparison with previous methods, our model significantly increases the performance by 47\% (+6.9 AP), showing remarkable ability under various evaluation settings.
A Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness
School of Information Science and Technology, Southwest Jiaotong University（信息科学与技术学院，西南交通大学）；School of Computer Science and Technology, Xidian University（计算机科学与技术学院，西安电子科技大学）
Boosting Quantitive and Spatial Awareness for Zero-Shot Object Counting
Northwestern Polytechnical University（西北工业大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院）；University of Science and Technology of China（中国科学技术大学）；Fudan University（复旦大学）
Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
MAGIC: Few-Shot Mask-Guided Anomaly Inpainting with Prompt Perturbation, Spatially Adaptive Guidance, and Context Awareness
Department of AI Semiconductor Engineering, Hanyang University, Seoul, Korea（汉阳大学人工智能半导体工程系）；Department of Electronic Engineering, Hanyang University, Seoul, Korea（汉阳大学电子工程系）
FAMSeC: A Few-shot-sample-based General AI-generated Image Detection Method
Anhui University（安徽大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）
Dual-channel Prototype Network for few-shot Classification of Pathological Images
College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；School of Computer, National University of Defense Technology（国防科技大学计算机学院）
DSFC-Net: A Dual-Encoder Spatial and Frequency Co-Awareness Network for Rural Road Extraction
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（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）；Department of Earth System Science, Tsinghua University（清华大学地球系统科学系）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）
Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction
Institute of Information Science, Academia Sinica（中央研究院信息科学研究所）；Department of Computer Science, National Tsing Hua University（国立清华大学计算机科学系）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）
Dual Variational Generation for Low-Shot Heterogeneous Face Recognition
NLPR & CRIPAC, CASIA（中国科学院自动化研究所 国家模式识别实验室与模式识别国家重点实验室与智能感知与计算研究中心）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Excellence in Brain Science and Intelligence Technology, CAS（中国科学院脑科学与智能技术卓越创新中心）
Mutually-Aware Feature Learning for Few-Shot Object Counting
Department of Artificial Intelligence, Sungkyunkwan University（人工智能系，全州大学）；Department of Computer Science and Engineering, Sungkyunkwan University（计算机科学与工程系，全州大学）
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；Fudan University（复旦大学）；University of Piraeus（比雷埃夫斯大学）
Self-Supervised Dual-Frequency Phase Decomposition for Single-Shot Composite Fringe Projection Profilometry
Department of Mechanical Engineering, Yonsei University（延世大学机械工程系）；Yonsei Institute for Embodied Intelligence, Yonsei University（延世大学具身智能研究所）；Meta Reality Labs
Dual-path Frequency Discriminators for Few-shot Anomaly Detection
Zhejiang University（浙江大学）；Polytechnic Institute, Zhejiang University（浙江大学理工学院）；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Tencent（腾讯）；China Tower (Hangzhou) Science and Technology Innovation Center（中国铁塔（杭州）科技创新中心）；School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）
Text-promptable Object Counting via Quantity Awareness Enhancement
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息学院）；College of Computer Science, Wuhan University（武汉大学计算机学院）；College of Computer Science, Tongji University（同济大学计算机学院）；State Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）
Dual Expert Distillation Network for Generalized Zero-Shot Learning
The Hong Kong Polytechnic University（香港理工大学）；PolyU Shenzhen Research Institute（香港理工大学深圳研究院）；Hong Kong University of Science and Technology（香港科技大学）；University of Iowa（爱荷华大学）；Huazhong University of Science and Technology（华中科技大学）；Jilin University（吉林大学）
