  Vision-language models have been widely explored across a wide range of tasks
and achieve satisfactory performance. However, it's under-explored how to
consolidate entity understanding through a varying number of images and to
align it with the pre-trained language models for generative tasks. In this
paper, we propose MIVC, a general multiple instance visual component to bridge
the gap between various image inputs with off-the-shelf vision-language models
by aggregating visual representations in a permutation-invariant fashion
through a neural network. We show that MIVC could be plugged into the
visual-language models to improve the model performance consistently on visual
question answering, classification and captioning tasks on a public available
e-commerce dataset with multiple images per product. Furthermore, we show that
the component provides insight into the contribution of each image to the
downstream tasks.
