Text Data-Centric Image Captioning with Interactive Prompts

• URL: http://arxiv.org/abs/2403.19193v1
• Date: Thu, 28 Mar 2024 07:43:49 GMT
• Title: Text Data-Centric Image Captioning with Interactive Prompts
• Authors: Yiyu Wang, Hao Luo, Jungang Xu, Yingfei Sun, Fan Wang,
• Abstract summary: Supervised image captioning approaches have made great progress, but it is challenging to collect high-quality human-annotated image-text data. This paper proposes a new Text data-centric approach with Interactive Prompts for image Captioning, named TIPCap.
• Score: 20.48013600818985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervised image captioning approaches have made great progress, but it is challenging to collect high-quality human-annotated image-text data. Recently, large-scale vision and language models (e.g., CLIP) and large-scale generative language models (e.g., GPT-2) have shown strong performances in various tasks, which also provide some new solutions for image captioning with web paired data, unpaired data or even text-only data. Among them, the mainstream solution is to project image embeddings into the text embedding space with the assistance of consistent representations between image-text pairs from the CLIP model. However, the current methods still face several challenges in adapting to the diversity of data configurations in a unified solution, accurately estimating image-text embedding bias, and correcting unsatisfactory prediction results in the inference stage. This paper proposes a new Text data-centric approach with Interactive Prompts for image Captioning, named TIPCap. 1) We consider four different settings which gradually reduce the dependence on paired data. 2) We construct a mapping module driven by multivariate Gaussian distribution to mitigate the modality gap, which is applicable to the above four different settings. 3) We propose a prompt interaction module that can incorporate optional prompt information before generating captions. Extensive experiments show that our TIPCap outperforms other weakly or unsupervised image captioning methods and achieves a new state-of-the-art performance on two widely used datasets, i.e., MS-COCO and Flickr30K.

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