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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 1–10 of 65 papers

TitleStatusHype
BatchFormer: Learning to Explore Sample Relationships for Robust Representation LearningCode2
Learning Conditional Attributes for Compositional Zero-Shot LearningCode1
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot LearningCode1
Learning Attention as Disentangler for Compositional Zero-shot LearningCode1
Learning Clustering-based Prototypes for Compositional Zero-shot LearningCode1
Disentangling Visual Embeddings for Attributes and ObjectsCode1
A causal view of compositional zero-shot recognitionCode1
CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot LearningCode1
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot LearningCode1
Learning Graph Embeddings for Compositional Zero-shot LearningCode1
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