Open World Compositional Zero-Shot Learning (OW-CZSL) is known to be an extremely challenging task, which aims to recognize unseen compositions formed from seen attributes and objects without any prior assumption of the output space. In order to achieve this goal, a model has to be "smart" and "knowledgeable". To be smart, a model should be good at reasoning the interactions between attributes and objects from the seen compositions. While "knowledgeable" means the model owns "common sense" to the open world that can "foresee" some features of the unseen compositions. Most previous work focuses on the "smart" part, while few of them provided an effective solution to achieve the "knowledgeable" goal. In this paper, we proposed a framework named Multi-Modal Prompt Tuning (MMPT) to inherit the "knowledgeable" property from the large pre-trained vision-language model. Extensive experiments show that our proposed MMPT obtains new state-of-the-art results in OW-CZSL task. On the UT-Zappos dataset, MMPT pushes the AUC score to $29.8$, while the previous best score is $26.5$. On the more challenging MIT-States dataset, the AUC score of MMPT is 1.5 times better than the current state-of-the-art.
Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection
Zhejiang University（浙江大学）；The Hong Kong University of Science and Technology（香港科技大学）；Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）
Weave Forcing: Compositional Memory Routing for Interactive Long Video Generation
University of Electronic Science and Technology of China (UESTC)（电子科技大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
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）
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）
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）
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）
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning
University of Tübingen（图宾根大学）；Helmholtz Munich（海德堡-慕尼黑研究所）；Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Inria, Ecole normale supérieure, CNRS, PSL Research University（Inria、巴黎高等师范学校、国家科学研究中心、PSL研究大学）
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）
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
East China Normal University（华东师范大学）；Chongqing Institute of East China Normal University（华东师范大学重庆研究院）；The Navy Military Medical University（海军军医大学）；Xiamen University（厦门大学）
PRECEPT: Planning Resilience via Experience, Context Engineering & Probing Trajectories A Unified Framework for Test-Time Adaptation with Compositional Rule Learning and Pareto-Guided Prompt Evolution
Evaluating Prompts Across Multiple Choice Tasks In a Zero-Shot Setting
大语言模型已经表明，通过自然语言提示可以实现令人印象深刻的zero-shot性能（Radford et al.，2019; Brown等人，2020; Sanh等人，2021年）。然而，创建一个有效的提示需要大量的尝试和错误。这就向\textit{prompts}提出了一个问题：提示的质量如何影响它的表现？为此，我们从各种任务中收集并标准化提示，以用于它们不是为之设计的任务。然后，我们在固定的多项选择数据集上评估这些提示，以定量分析提示的某些属性如何影响性能。我们发现，包括选择和使用提示不使用在预培训提供了显着的改善。所有的实验和代码都可以在https://github.com/gabeorlanski/zero-shot-cross-task上找到。
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）
Information Technology University（信息科技大学）；University of Ontario Institute of Technology（安大略理工大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
