Accuracy: 71.4
Papers with Code · CC BY-SA 4.0
Zero-Shot Learning
Zero-shot learning (ZSL) is a model's ability to detect classes never seen during training. The condition is that the classes are not known during supervised learning.
Earlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the computer vision context, more recent advances learn mappings from image feature space to semantic space. Other approaches learn non-linear multimodal embeddings. In the modern NLP context, language models can be evaluated on downstream tasks without fine tuning.
Benchmark datasets for zero-shot learning include aPY, AwA, and CUB, among others.
( Image credit: Prototypical Networks for Few shot Learning in PyTorch )
Further readings:
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Dataset기록된 모델 예시MetricPaperCode
AdaTAD (VideoMAEv2-giant)
mAP IOU@0.5: 80.9mAP IOU@0.3: 89.7mAP IOU@0.4: 86.7mAP IOU@0.6: 71.0
논문
코드
데이터셋
178개논문
Average mAP: 84.30
Accuracy: 21.7
average top-1 classification accuracy: 70.8
A-acc: 36.0
average top-1 classification accuracy: 71.9
Accuracy: 88.98
Accuracy: 0.268
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