Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
Peking University（北京大学）；University of California, Berkeley（加州大学伯克利分校）；South China University of Technology（华南理工大学）；PengCheng Laboratory（鹏程实验室）；Carnegie Mellon University（卡内基梅隆大学）
Yixiong Zou, Shanghang Zhang, Ke Chen, Yonghong Tian, Yaowei Wang, José M. F. Moura
Few-shot learning (FSL) aims at recognizing novel classes given only few training samples, which still remains a great challenge for deep learning. However, humans can easily recognize novel classes with only few samples. A key component of such ability is the compositional recognition that human can perform, which has been well studied in cognitive science but is not well explored in FSL. Inspired by such capability of humans, to imitate humans' ability of learning visual primitives and composing primitives to recognize novel classes, we propose an approach to FSL to learn a feature representation composed of important primitives, which is jointly trained with two parts, i.e. primitive discovery and primitive enhancing. In primitive discovery, we focus on learning primitives related to object parts by self-supervision from the order of image splits, avoiding extra laborious annotations and alleviating the effect of semantic gaps. In primitive enhancing, inspired by current studies on the interpretability of deep networks, we provide our composition view for the FSL baseline model. To modify this model for effective composition, inspired by both mathematical deduction and biological studies (the Hebbian Learning rule and the Winner-Take-All mechanism), we propose a soft composition mechanism by enlarging the activation of important primitives while reducing that of others, so as to enhance the influence of important primitives and better utilize these primitives to compose novel classes. Extensive experiments on public benchmarks are conducted on both the few-shot image classification and video recognition tasks. Our method achieves the state-of-the-art performance on all these datasets and shows better interpretability.
ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；The Hong Kong University of Science and Technology（香港科技大学）；Huazhong University of Science and Technology（华中科技大学）
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning
Jiangnan University（江南大学）；University of New South Wales（新南威尔士大学）；Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；Zhejiang University（浙江大学）
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；School of Computer Science, Peking University（北京大学计算机学院）
EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家级重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究所）；Xi’an Jiaotong University（西安交通大学）
Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning
School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Key Laboratory of Big Data & Artificial Intelligence in Transportation (Ministry of Education)（交通运输大数据与人工智能教育部重点实验室）
Zero-shot Compositional Action Recognition with Neural Logic Constraints
College of Computer Science and Technology（计算机科学与技术学院）；Zhejiang University（浙江大学）；AI Chip Center for Emerging Smart Systems（新兴智能系统人工智能芯片中心）；The Hong Kong University of Science and Technology（香港科学与技术大学）
Multi-Sourced Compositional Generalization in Visual Question Answering
Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science & Technology, Beijing Institute of Technology, China（北京智能信息科技重点实验室，计算机科学与技术学院，北京理工大学，中国）；Guangdong Laboratory of Machine Perception and Intelligent Computing, Shenzhen MSU-BIT University, China（广东机器感知与智能计算实验室，深圳MSU-BIT大学，中国）
From Model Patterns to Abstract Semantics in Compositional Zero-Shot Learning
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Key Laboratory of Network System and Network Culture（网络系统与网络文化北京市重点实验室）；Key Laboratory of Interactive Technology and Experience System（交互技术与体验系统重点实验室）
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning
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 Engineering, Newcastle University（纽卡斯尔大学工程学院）；Department of Computer Science, Durham University（杜伦大学计算机科学系）；Harbin Institute of Technology（哈尔滨工业大学）；School of Artificial Intelligence, Nanjing University of Science and Technology（南京理工大学人工智能学院）
Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning
PowerChina Huadong Engineering Corporation Limited（国家电力投资集团华东工程公司）；Hangzhou Dianzi University（杭州电子科技大学）；The University of Manchester（曼彻斯特大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Zhejiang University（浙江大学）
Compositional Zero-shot Learning via Progressive Language-based Observations
AI Chip Center for Emerging Smart Systems（新兴智能系统人工智能芯片中心）；The Hong Kong University of Science（香港科学大学）；Zhejiang University College of Computer Science（浙江大学计算机科学学院）
Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality
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，教育部脑启发智能感知与认知重点实验室）；Independent Researcher（独立研究员）
SuperGaussians: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors
The University of Hong Kong（香港大学）；Shenzhen Graduate School of Peking University（北京大学深圳研究生院）；Nanyang Technological University（南洋理工大学）；Hong Kong University of Science and Technology（香港科技大学）；Macau University of Science and Technology（澳门科技大学）；Shandong University（山东大学）；Texas A&M University（德克萨斯农工大学）
Semantically Guided Dynamic Visual Prototype Refinement for Compositional Zero-Shot Learning
National Key Laboratory of Radar Signal Processing, Xidian University（雷达信号处理国家级重点实验室，西安电子科技大学）；Research Institute of Systems Engineering, Academy of Military Science（系统工程研究所，军事科学院）
TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learning
The Hong Kong Polytechnic University（香港理工大学）；Hong Kong Baptist University（香港浸会大学）；Shenzhen University（深圳大学）；Jilin University（吉林大学）；Hong Kong University of Science and Technology（香港科技大学）
On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning
Technische Universität München（慕尼黑工业大学）；Mercateo AG（Mercateo股份公司）
人类能够识别和分类已知概念的新组合。组合零镜头学习（Compositional Zero-Shot learning，CNOML）的任务是学习原始概念（即对象和状态）的组合，即使是它们的新组合也可以被zero-shot分类。在这项工作中，我们不假设任何先验知识的可行性，即开放世界的设置，其中不可行的组合物占主导地位的搜索空间的新组合物。我们提出了一种组合变分图自动编码器（CVGAE）的方法来学习原始概念（节点）的变分嵌入以及它们的组合（通过边缘）的可行性。这样的建模使得CVGAE可扩展到现实世界的应用场景。这与SOTA方法CGE相反，SOTA方法CGE在计算上非常昂贵。例如，对于基准C-GQA数据集，CGE需要3.94 x 10^5个节点，而CVGAE只需要1323个节点。我们学习将图和图像嵌入映射到公共嵌入空间。CVGAE采用深度度量学习方法，并通过投影图和图像嵌入之间的双向对比损失来学习该空间中的相似性度量。我们在三个基准数据集上验证了该方法的有效性，并通过一个图像检索任务证明了CVGAE学习的表示更适合于组合泛化。
CardioComposer: Leveraging Differentiable Geometry for Compositional Control of Anatomical Diffusion Models
Massachusetts Institute of Technology（麻省理工学院）；Brigham and Women’s Hospital（布里奇沃特医院）；American University in Cairo（开罗美国大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning
Tongji University（同济大学）；The Ohio State University（俄亥俄州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Shandong Academy of Sciences（山东省科学院）；Macquarie University（麦考瑞大学）
Learning Graph Embeddings for Open World Compositional Zero-Shot Learning
University of Tübingen（蒂宾根大学）；ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute (MPI) for Informatics（马克斯·普朗克信息学研究所）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）
