Progressive Tree-Structured Prototype Network for End-to-End Image Captioning

• URL: http://arxiv.org/abs/2211.09460v1
• Date: Thu, 17 Nov 2022 11:04:00 GMT
• Title: Progressive Tree-Structured Prototype Network for End-to-End Image Captioning
• Authors: Pengpeng Zeng, Jinkuan Zhu, Jingkuan Song, Lianli Gao
• Abstract summary: We propose a novel Progressive Tree-Structured prototype Network (dubbed PTSN) PTSN is the first attempt to narrow down the scope of prediction words with appropriate semantics by modeling the hierarchical textual semantics. Our method achieves a new state-of-the-art performance with 144.2% (single model) and 146.5% (ensemble of 4 models) CIDEr scores on Karpathy' split and 141.4% (c5) and 143.9% (c40) CIDEr scores on the official online test server.
• Score: 74.8547752611337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Studies of image captioning are shifting towards a trend of a fully end-to-end paradigm by leveraging powerful visual pre-trained models and transformer-based generation architecture for more flexible model training and faster inference speed. State-of-the-art approaches simply extract isolated concepts or attributes to assist description generation. However, such approaches do not consider the hierarchical semantic structure in the textual domain, which leads to an unpredictable mapping between visual representations and concept words. To this end, we propose a novel Progressive Tree-Structured prototype Network (dubbed PTSN), which is the first attempt to narrow down the scope of prediction words with appropriate semantics by modeling the hierarchical textual semantics. Specifically, we design a novel embedding method called tree-structured prototype, producing a set of hierarchical representative embeddings which capture the hierarchical semantic structure in textual space. To utilize such tree-structured prototypes into visual cognition, we also propose a progressive aggregation module to exploit semantic relationships within the image and prototypes. By applying our PTSN to the end-to-end captioning framework, extensive experiments conducted on MSCOCO dataset show that our method achieves a new state-of-the-art performance with 144.2% (single model) and 146.5% (ensemble of 4 models) CIDEr scores on `Karpathy' split and 141.4% (c5) and 143.9% (c40) CIDEr scores on the official online test server. Trained models and source code have been released at: https://github.com/NovaMind-Z/PTSN.

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