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Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) is a computer vision task in which the goal is to recognize unseen compositions fromed from seen state and object during training. The key challenge in CZSL is the inherent entanglement between the state and object within the context of an image. Some example benchmarks for this task are MIT-states, UT-Zappos, and C-GQA. Models are usually evaluated with the Accuracy for both seen and unseen compositions, as well as their Harmonic Mean(HM).

( Image credit: Heosuab )

Papers

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Showing 1–25 of 65 papers

TitleStatusHype
EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning—0
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning—0
MSCI: Addressing CLIP's Inherent Limitations for Compositional Zero-Shot LearningCode1
Visual Adaptive Prompting for Compositional Zero-Shot Learning—0
Learning Clustering-based Prototypes for Compositional Zero-shot LearningCode1
Learning Primitive Relations for Compositional Zero-Shot Learning—0
Dual-Modal Prototype Joint Learning for Compositional Zero-Shot Learning—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—0
Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning—0
Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning—0
Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning—0
LOGICZSL: Exploring Logic-induced Representation for Compositional Zero-shot Learning—0
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training—0
Unified Framework for Open-World Compositional Zero-shot LearningCode0
Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot LearningCode1
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning—0
Cross-composition Feature Disentanglement for Compositional Zero-shot Learning—0
TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learning—0
Attention Based Simple Primitives for Open World Compositional Zero-Shot LearningCode0
Anticipating Future Object Compositions without Forgetting—0
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot LearningCode0
MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot Learning—0
CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning—0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
Beyond Seen Primitive Concepts and Attribute-Object Compositional Learning—0
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