Select the model who knows the image best: a multi-model method

In the current popular visual tasks, one single model is usually used to output the final results. But no model is perfect, in this paper, we propose a simple and general multi-model method. To combine the advantages of multiple models, we design a familiarity prediction network to output the model's familiarity of images, then select the optimal model based on the familiarity value. Since the loss value is a single value, the familiarity value of any task can be reflected in the loss value, so the output of the familiarity prediction network can be regarded as an estimate of the loss value. The accuracy of the multi-model exceeds any single model that composes the multi-model. By limiting the number of feature layers input into the familiarity network, the sacrifice of computation and detection speed is acceptable. Our method is general and task-agnostic, it not only performs well on classification tasks but also on object detection tasks and other vision tasks.