  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
https://github.com/Harryqu123/LMC
