H-Mean: 16.1Seen accuracy: 24.4Test AUC top 1: 3.0Test AUC top 2: 7.6
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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).
<span style="color:grey; opacity: 0.6">( Image credit: Heosuab )</span>
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Dataset기록된 모델 예시MetricPaperCode
데이터셋
4개논문
Attribute accuracy: 30.2Object accuracy: 32.6Seen accuracy: 29Unseen accuracy: 26.2
H-Mean: 39.9Seen accuracy: 51.0Test AUC top 1: 23.4Test AUC top 2: -
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