SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentation
This addresses the computational burden of image captioning for researchers and practitioners, though it is incremental as it builds on existing CLIP and GPT-2 components.
The authors tackled the high computational cost of large-scale image captioning models by developing SmallCap, a lightweight model that generates captions using retrieved examples from a datastore. Their approach achieved competitive performance on COCO and transferred to other domains without retraining, with further improvements from training-free use of diverse data.
Recent advances in image captioning have focused on scaling the data and model size, substantially increasing the cost of pre-training and finetuning. As an alternative to large models, we present SmallCap, which generates a caption conditioned on an input image and related captions retrieved from a datastore. Our model is lightweight and fast to train, as the only learned parameters are in newly introduced cross-attention layers between a pre-trained CLIP encoder and GPT-2 decoder. SmallCap can transfer to new domains without additional finetuning and can exploit large-scale data in a training-free fashion since the contents of the datastore can be readily replaced. Our experiments show that SmallCap, trained only on COCO, has competitive performance on this benchmark, and also transfers to other domains without retraining, solely through retrieval from target-domain data. Further improvement is achieved through the training-free exploitation of diverse human-labeled and web data, which proves to be effective for a range of domains, including the nocaps benchmark, designed to test generalization to unseen visual concepts.