Smallcap: Lightweight Image Captioning Prompted with Retrieval Augmentation
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Accepted author manuscript, 2.34 MB, PDF document
Recent advances in image captioning have focused on scaling the data and model size, substantially increasing the cost of pretraining 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.11Code: https://github.com/RitaRamo/smallcap.
Original language | English |
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Title of host publication | Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 |
Number of pages | 10 |
Publisher | IEEE Computer Society Press |
Publication date | 2023 |
Pages | 2840-2849 |
ISBN (Electronic) | 9798350301298 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Vancouver, Canada Duration: 18 Jun 2023 → 22 Jun 2023 |
Conference
Conference | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 |
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Land | Canada |
By | Vancouver |
Periode | 18/06/2023 → 22/06/2023 |
Sponsor | Amazon Science, Ant Research, Cruise, et al., Google, Lambda |
Bibliographical note
Publisher Copyright:
© 2023 IEEE.
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Research areas
ID: 371289982