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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
EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning—0
Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning—0
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning—0
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning—0
HOMOE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts—0
Learning Attention Propagation for Compositional Zero-Shot Learning—0
Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning—0
On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning—0
ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning—0
Prompt Tuning for Zero-shot Compositional Learning—0
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