HardBoost: Boosting Zero-Shot Learning with Hard Classes
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；School of Computer Science and Technology, Taiyuan University of Science and Technology（太原科技大学计算机科学与技术学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
This work is a systematical analysis on the so-called hard class problem in zero-shot learning (ZSL), that is, some unseen classes disproportionally affect the ZSL performances than others, as well as how to remedy the problem by detecting and exploiting hard classes. At first, we report our empirical finding that the hard class problem is a ubiquitous phenomenon and persists regardless of used specific methods in ZSL. Then, we find that high semantic affinity among unseen classes is a plausible underlying cause of hardness and design two metrics to detect hard classes. Finally, two frameworks are proposed to remedy the problem by detecting and exploiting hard classes, one under inductive setting, the other under transductive setting. The proposed frameworks could accommodate most existing ZSL methods to further significantly boost their performances with little efforts. Extensive experiments on three popular benchmarks demonstrate the benefits by identifying and exploiting the hard classes in ZSL.
MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation
AiV Research Group（AiV研究组）
Few-Shot分割的目标是在仅有少量标记密集的样本的情况下分割出不可见类对象。原型学习是一种通过对全局和局部对象信息进行平均来产生单个或多个原型的学习方法，在自由选择系统中得到了广泛的应用。然而，仅利用原型向量可能不足以表示所有训练数据的特征。为了提取丰富的特征并进行更精确的预测，我们提出了一个多相似性和注意力网络（MSANet），包括两个新的模块，一个多相似性模块和一个注意力模块。多相似度模块利用支持图像和查询图像的多个特征图来估计准确的语义关系。注意力模块指示网络集中于与类相关的信息。该网络在标准FSS数据集PASCAL-5i 1-shot、PASCAL-5i 5-shot、COCO-20 i 1-shot和COCO-20 i 5-shot上进行了测试。以ResNet-101为主干的MSANet在所有4个基准数据集上实现了最先进的性能，平均交并比（mIoU）分别为69.13%，73.99%，51.09%，56.80%。代码可在https://github.com/AIVResearch/MSANet上获得
Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation
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）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；School of Engineering, Newcastle University（纽卡斯尔大学工程学院）
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement
Harbin Institute of Technology Shenzhen（哈尔滨工业大学深圳校区）；SmartMore（思谋科技）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing
Dalian University of Technology（大连理工大学）；Peking University（北京大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；Zhejiang Lab（之江实验室）
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）
Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Intelligent Computing Technology, CAS（中国科学院智能计算技术研究所）；Microsoft Research Asia（微软亚洲研究院）
Group-On: Boosting One-Shot Segmentation with Supportive Query
Zhejiang University（浙江大学）；University of Notre Dame（诺丁汉大学）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）；HKUST(Guangzhou)（香港科技大学（广州））
Boosting Few-Shot Learning via Attentive Feature Regularization
University of Science and Technology of China（中国科学技术大学）；MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China（中国科学技术大学类脑智能技术与应用国家工程实验室）；Brain-Inspired Technology Co., Ltd.（类脑智能科技有限公司）
Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation
Huawei London Research（华为伦敦研究院）；King’s College London（伦敦国王学院）；College of Electronic and Information Engineering（电子与信息工程学院）；Tongji University（同济大学）；Imperial College London（帝国理工学院）
Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations
Tsinghua University（清华大学）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Microsoft Research Asia（微软亚洲研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask
Chongqing University of Technology（重庆理工大学）；Chongqing University（重庆大学）；The Chinese University of Hong Kong（香港中文大学）；SmartMore（智谱AI）；University of Macau（澳门大学）
Boosting Vision-Language Models for Histopathology Classification: Predict all at once
Université Catholique de Louvain (UCLouvain)（天主教鲁汶大学（UCLouvain））；Université de Mons (UMons)（蒙斯大学（UMons））；École de Technologie Supérieure (ÉTS)（高等技术学院（ÉTS））；Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM)（蒙特利尔大学中心医院研究中心（CRCHUM））
Boosting Few-Shot Open-Set Object Detection via Prompt Learning and Robust Decision Boundary
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China（虚拟现实技术与系统国家重点实验室，北京航空航天大学）；The School of Artificial Intelligence and Data Science, Hebei University of Technology, China（人工智能与数据科学学院，河北工业大学）；China Xiongan Group Digital City Technology Co., Ltd.（雄安集团数字城市技术有限公司）；Information Engineering College, Capital Normal University, China（信息工程学院，首都师范大学）；Institute of Information Engineering, Chinese Academy of Sciences（信息工程研究所，中国科学院）；Zhongguancun Laboratory, Beijing, China（中关村实验室，北京）
Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Department of Oral Pathology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院第九人民医院口腔病学部）；Department of Pathology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院新华医院病理科）；Department of Pediatric Hematology/Oncology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院新华医院儿童血液肿瘤科）；Clinical Research and Innovation Unit, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院新华医院临床研究与创新单元）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
Institute of Information Science and Technologies, National Research Council（国家研究委员会信息科学与技术研究所）；Department of Information Engineering, University of Pisa（比萨大学信息工程系）；School of Engineering, University of Edinburgh（爱丁堡大学工程学院）
Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios
Unmanned System Research Institute at Northwestern Polytechnical University（西北工业大学无人系统研究院）；School of Electronic, Electrical, and Communication Engineering, University of Chinese Academic of Sciences（中国科学院大学电子电气与通信工程学院）；Max Planck Institute for Informatics (MPI-INF)（马克斯·普朗克研究所（信息研究所））；University of Science and Technology of China（中国科学技术大学）
Boosting Medical Vision-Language Pretraining via Momentum Self-Distillation under Limited Computing Resources
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam（信息科技学院，科学大学，胡志明市，越南）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学，胡志明市，越南）
Hard Sample Matters a Lot in Zero-Shot Quantization
South China University of Technology（华南理工大学）；Changsha Hisense Intelligent System Research Institute Co., Ltd（长沙海信智能系统研究院有限公司）；Hong Kong Baptist University（香港浸会大学）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Key Laboratory of Big Data and Intelligent Robot, Ministry of Education（教育部大数据与智能机器人重点实验室）；PengCheng Laboratory（鹏城实验室）；Information Technology R&D Innovation Center of Peking University（北京大学信息技术研发创新中心）
