ReformulatingZero-shotActionRecognitionfor Multi-labelActions (SupplementaryMaterial)

This poor performance is due to the nearest neighbor classification which does not allow semantically dissimilar classes to be predicted confidently. It is meant to be "an evaluation dataset notably meant to be used to evaluate models trained on the HowTo100M dataset" [38]. Table 3: Tenclasses which PS-ZSAR performs worstonintheUCF-101 dataset. A shared multi-attention framework for multi-label zeroshotlearning.