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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 31–40 of 65 papers

TitleStatusHype
Compositional Zero-shot Learning via Progressive Language-based Observations—0
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition—0
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training—0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
Cross-composition Feature Disentanglement for Compositional Zero-shot Learning—0
CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning—0
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning—0
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning—0
Dual-Modal Prototype Joint Learning for Compositional Zero-Shot Learning—0
Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning—0
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