Large language models~(LLMs) are instruction followers, but it can be challenging to find
the best instruction for different situations, especially for black-box LLMs on which …

Compositional Zero-Shot Learning (CZSL) aims to recognize novel\textit {state-object}
compositions by leveraging the shared knowledge of their primitive components. Despite …

Compositional zero-shot learning (CZSL) aims to recognize unseen attribute-object
compositions by learning from seen compositions. Composing the learned knowledge of …

Compositional zero-shot learning aims to recognize unseen state-object compositions by
leveraging known primitives (state and object) during training. However, effectively modeling …

Compositional Zero-Shot Learning (CZSL) aims to learn visual concepts (ie, attributes and
objects) from seen compositions and combine them to predict unseen compositions. Existing …

Recent studies show that prompt tuning can better leverage the power of large language
models than fine-tuning on downstream natural language understanding tasks. However …

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions of seen
primitives. Prior studies have attempted to either learn primitives individually (non …

Abstract Compositional Zero-Shot Learning (CZSL) aims to classify unseen state-object
compositions using seen primitives. Previous methods commonly map an identical primitive …

The integration of Artificial Intelligence (AI) into biodiversity research and conservation is
growing rapidly, demonstrating great potential in reducing the intensive human labor …

Learning primitive (ie, attribute and object) concepts from seen compositions is the primary
challenge of Compositional Zero-Shot Learning (CZSL). Existing CZSL solutions typically …