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Image Captioning is a traditional vision-and-language task that aims togenerate the language description of an image. Recent studies focus on scalingup the model size and the number of training data, which significantly increasethe cost of model training. Different to these heavy-cost models, we introducea lightweight image captioning framework (I-Tuning), which contains a smallnumber of trainable parameters. We design a novel I-Tuning cross-attentionmodule to connect the non-trainable pre-trained language decoder GPT2 andvision encoder CLIP-ViT. Since most parameters are not required to be updatedduring training, our framework is lightweight and fast. Experimental resultsconducted on three image captioning benchmarks reveal that our frameworkachieves comparable or better performance than the large-scale baselinesystems. But our models contain up to 10 times fewer trainable parameters andrequire much fewer data for training compared with state-of-the-art baselines.

I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning