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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 41–50 of 65 papers

TitleStatusHype
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
Revealing the Proximate Long-Tail Distribution in Compositional Zero-Shot LearningCode0
Compositional Zero-Shot Learning for Attribute-Based Object Reference in Human-Robot Interaction—0
Prompt Tuning for Zero-shot Compositional Learning—0
HOMOE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts—0
Compositional Zero-shot Learning via Progressive Language-based Observations—0
GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot LearningCode0
Hierarchical Visual Primitive Experts for Compositional Zero-Shot LearningCode0
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