Papers Compositional Zero-Shot Learning
“Compositional Zero-Shot Learning” 태그가 달린 논문 81편 · 필터 해제
DIFFCZSL: Compositional Zero-Shot Learning Regularized by Diffusion Representations
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions by leveraging knowledge of primitive concepts learned from seen compositions. Although recent works achieve impressive perfor…
Compositional Zero-Shot LearningProgressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to combine known attributes and objects as primitives for recognizing previously unseen attribute-object pairs. Prior works either predict attributes and objects independently…
Compositional Zero-Shot LearningReinforcement LearningFlowComposer: Composable Flows for Compositional Zero-Shot Learning
Compositional zero-shot learning (CZSL) aims to recognize unseen attribute-object compositions by recombining primitives learned from seen pairs. Recent CZSL methods built on vision-language models (VLMs) typically adopt…
Compositional Zero-Shot Learningparameter-efficient fine-tuningStructure-aware Prompt Adaptation from Seen to Unseen for Open-Vocabulary Compositional Zero-Shot Learning
The goal of Open-Vocabulary Compositional Zero-Shot Learning (OV-CZSL) is to recognize attribute-object compositions in the open-vocabulary setting, where compositions of both seen and unseen attributes and objects are e…
Compositional Zero-Shot LearningWARM-CAT: Warm-Started Test-Time Comprehensive Knowledge Accumulation for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions based on the knowledge learned from seen ones. Existing methods suffer from performance degradation caused by the distribution…
Compositional Zero-Shot LearningRepresentation Learning$\text{H}^2$em: Learning Hierarchical Hyperbolic Embeddings for Compositional Zero-Shot Learning
Compositional zero-shot learning (CZSL) aims to recognize unseen state-object compositions by generalizing from a training set of their primitives (state and object). Current methods often overlook the rich hierarchical …
Compositional Zero-Shot LearningSelf-Attention with State-Object Weighted Combination for Compositional Zero Shot Learning
Object recognition has become prevalent across various industries. However, most existing applications are limited to identifying objects alone, without considering their associated states. The ability to recognize both …
Compositional Zero-Shot LearningObject RecognitionPrompt-Based Continual Compositional Zero-Shot Learning
We tackle continual adaptation of vision-language models to new attributes, objects, and their compositions in Compositional Zero-Shot Learning (CZSL), while preventing forgetting of prior knowledge. Unlike classical con…
Compositional Zero-Shot LearningContinual LearningCAMS: Towards Compositional Zero-Shot Learning via Gated Cross-Attention and Multi-Space Disentanglement
Compositional zero-shot learning (CZSL) aims to learn the concepts of attributes and objects in seen compositions and to recognize their unseen compositions. Most Contrastive Language-Image Pre-training (CLIP)-based CZSL…
Compositional Zero-Shot LearningComposition-Incremental Learning for Compositional Generalization
Compositional generalization has achieved substantial progress in computer vision on pre-collected training data. Nonetheless, real-world data continually emerges, with possible compositions being nearly infinite, long-t…
Compositional Zero-Shot LearningIncremental LearningTOMCAT: Test-time Comprehensive Knowledge Accumulation for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions based on the knowledge learned from seen ones. Existing methods suffer from performance degradation caused by the distribution…
Compositional Zero-Shot LearningRepresentation LearningCompositional Zero-Shot Learning: A Survey
Compositional Zero-Shot Learning (CZSL) is a critical task in computer vision that enables models to recognize unseen combinations of known attributes and objects during inference, addressing the combinatorial challenge …
Compositional Zero-Shot LearningLearning by Imagining: Debiased Feature Augmentation for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions by learning prior knowledge of seen primitives, \textit{i.e.}, attributes and objects. Learning generalizable compositional r…
Compositional Zero-Shot LearningSalientFusion: Context-Aware Compositional Zero-Shot Food Recognition
Food recognition has gained significant attention, but the rapid emergence of new dishes requires methods for recognizing unseen food categories, motivating Zero-Shot Food Learning (ZSFL). We propose the task of Composit…
Compositional Zero-Shot LearningContinual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting
Vision-language models (VLMs), spanning predictive architectures to generative Multimodal Large Language Models (MLLMs), have revolutionized artificial intelligence through powerful cross-modal alignment and zero-shot ge…
Compositional Zero-Shot LearningZero-shot GeneralizationContinual LearningA Conditional Probability Framework for Compositional Zero-shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen combinations of known objects and attributes by leveraging knowledge from previously seen compositions. Traditional approaches primarily focus on disentang…
Compositional Zero-Shot LearningEVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) investigates compositional generalization capacity to recognize unknown state-object pairs based on learned primitive concepts. Existing CZSL methods typically derive primitives fe…
Compositional Zero-Shot LearningMixture-of-ExpertsZero-Shot LearningFeasibility with Language Models for Open-World Compositional Zero-Shot Learning
Humans can easily tell if an attribute (also called state) is realistic, i.e., feasible, for an object, e.g. fire can be hot, but it cannot be wet. In Open-World Compositional Zero-Shot Learning, when all possible state-…
AttributeCompositional Zero-Shot LearningIn-Context LearningLanguage Modeling+3MSCI: Addressing CLIP's Inherent Limitations for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen state-object combinations by leveraging known combinations. Existing studies basically rely on the cross-modal alignment capabilities of CLIP but tend to o…
Compositional Zero-Shot Learningcross-modal alignmentZero-Shot LearningVisual Adaptive Prompting for Compositional Zero-Shot Learning
Vision-Language Models (VLMs) have demonstrated impressive capabilities in learning joint representations of visual and textual data, making them powerful tools for tasks such as Compositional Zero-Shot Learning (CZSL). …
AttributeCompositional Zero-Shot LearningZero-Shot Learning