Focusing on Valid Search Space in Open-World Compositional Zero-Shot Learning by Leveraging Misleading Answers

Focusing on Valid Search Space in Open-World Compositional Zero-Shot Learning by Leveraging Misleading Answers

Blog Article

The goal of Compositional Zero-Shot Learning (CZSL) is to recognize various compositions of state-object pairs.Because the compositions that need to be considered are only a subset of all combinations of states and objects, it is tough for models to predict unseen compositions.Previous work overlooks the problem of predicting non-sensical compositions such as flying dogs.To address this problem, we introduce a novel method for the model to distinguish between target and non-target composition space to avoid predicting absurd compositions.

More specifically, in the process 3.26/3VR of predicting the states and objects, we train the model to increase the similarity with the label that matches the input image while decreasing the similarity with non-matched labels.Our method calculates the logits for the composition labels by combining the similarities of the image-states and the similarities of image-objects respectively.Then, the combined logits and directly computed composition logits are used to minimize the case of the predicting absurd composition.On three well-known datasets such as MIT-States, UT-Zappos, and C-GQA, various experimental results demonstrate our simple and CBD Vape novel approach significantly improves model performances.

Code is available at: https://github.com/ToBeSuperior/Annotation-embedding.