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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 61–65 of 65 papers

TitleStatusHype
ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning—0
Prompt Tuning for Zero-shot Compositional Learning—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—0
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning—0
TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learning—0
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