Paper Reading AI Learner

MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot Learning

2024-06-18 16:24:48
Shuo Xu, Sai Wang, Xinyue Hu, Yutian Lin, Bo Du, Yu Wu


Compositional Zero-Shot Learning (CZSL) aims to learn semantic primitives (attributes and objects) from seen compositions and recognize unseen attribute-object compositions. Existing CZSL datasets focus on single attributes, neglecting the fact that objects naturally exhibit multiple interrelated attributes. Real-world objects often possess multiple interrelated attributes, and current datasets' narrow attribute scope and single attribute labeling introduce annotation biases, undermining model performance and evaluation. To address these limitations, we introduce the Multi-Attribute Composition (MAC) dataset, encompassing 18,217 images and 11,067 compositions with comprehensive, representative, and diverse attribute annotations. MAC includes an average of 30.2 attributes per object and 65.4 objects per attribute, facilitating better multi-attribute composition predictions. Our dataset supports deeper semantic understanding and higher-order attribute associations, providing a more realistic and challenging benchmark for the CZSL task. We also develop solutions for multi-attribute compositional learning and propose the MM-encoder to disentangling the attributes and objects.

Abstract (translated)

组合零 shot 学习(CZSL)旨在通过观察到的作品学习语义原型(属性)并识别未见到的属性-对象组合。现有的 CZSL 数据集集中只关注单个属性,忽视了对象自然表现出多种相关属性的事实。现实世界的物体通常具有多个相关属性,而当前数据集的狭窄属性范围和单属性标注导致标注偏差,削弱了模型的性能和评估。为了克服这些限制,我们引入了多属性组合(MAC)数据集,包括18,217个图像和11,067个组合,具有全面的、代表性的、多样性的属性注释。MAC 包括每个对象的平均30.2个属性以及每个属性的65.4个对象,从而促进更好的多属性组合预测。我们的数据集支持更深的语义理解和高阶属性关联,为 CZSL 任务提供了一个更真实和具有挑战性的基准。我们还开发了多属性组合学习解决方案,并提出 MM-编码器来解离属性和对象。



3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot