Feasibility with Language Models for Open-World Compositional Zero-Shot Learning

• URL: http://arxiv.org/abs/2505.11181v1
• Date: Fri, 16 May 2025 12:37:08 GMT
• Title: Feasibility with Language Models for Open-World Compositional Zero-Shot Learning
• Authors: Jae Myung Kim, Stephan Alaniz, Cordelia Schmid, Zeynep Akata,
• Abstract summary: In Open-World Compositional Zero-Shot Learning, all possible state-object combinations are considered as unseen classes.<n>Our work focuses on using external auxiliary knowledge to determine the feasibility of state-object combinations.
• Score: 96.6544564242316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans can easily tell if an attribute (also called state) is realistic, i.e., feasible, for an object, e.g. fire can be hot, but it cannot be wet. In Open-World Compositional Zero-Shot Learning, when all possible state-object combinations are considered as unseen classes, zero-shot predictors tend to perform poorly. Our work focuses on using external auxiliary knowledge to determine the feasibility of state-object combinations. Our Feasibility with Language Model (FLM) is a simple and effective approach that leverages Large Language Models (LLMs) to better comprehend the semantic relationships between states and objects. FLM involves querying an LLM about the feasibility of a given pair and retrieving the output logit for the positive answer. To mitigate potential misguidance of the LLM given that many of the state-object compositions are rare or completely infeasible, we observe that the in-context learning ability of LLMs is essential. We present an extensive study identifying Vicuna and ChatGPT as best performing, and we demonstrate that our FLM consistently improves OW-CZSL performance across all three benchmarks.

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