Open-set object recognition aims to identify if an object is from a class that has been encountered during training or not. To perform open-set object recognition accurately, a key challenge is how to reduce the reliance on spurious-discriminative features. In this paper, motivated by that different large models pre-trained through different paradigms can possess very rich while distinct implicit knowledge, we propose a novel framework named Large Model Collaboration (LMC) to tackle the above challenge via collaborating different off-the-shelf large models in a training-free manner. Moreover, we also incorporate the proposed framework with several novel designs to effectively extract implicit knowledge from large models. Extensive experiments demonstrate the efficacy of our proposed framework. Code is available \href{https://github.com/Harryqu123/LMC}{here}.

翻译：开放集物体识别旨在判断一个物体是否属于训练阶段见过的类别。为实现准确的开放集物体识别，关键挑战在于如何减少对伪鉴别性特征的依赖。本文受不同预训练范式下的大模型蕴含丰富且差异显著的隐式知识这一事实启发，提出了一种名为大模型协作（LMC）的新型框架，通过以免训练方式协作多个现成大模型来应对上述挑战。此外，我们还为该框架设计了多项创新机制以有效提取大模型的隐式知识。大量实验验证了所提框架的有效性。代码已开源至\href{https://github.com/Harryqu123/LMC}{此处}。