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20192026
most citedMultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning

13 citations · 31 across the 11 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CV2023★ 13 cited

MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning

Bang Yang, Fenglin Liu, Xian Wu +3

Supervised visual captioning models typically require a large scale of images or videos paired with descriptions in a specific language (i.e., the vision-caption pairs) for trainin…

cs.CV2023

Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels

Bang Yang, Fenglin Liu, Zheng Li +4

Generating an informative and attractive title for the product is a crucial task for e-commerce. Most existing works follow the standard multimodal natural language generation appr…

cs.CV2023★ 4 cited

Customizing General-Purpose Foundation Models for Medical Report Generation

Bang Yang, Asif Raza, Yuexian Zou +1

Medical caption prediction which can be regarded as a task of medical report generation (MRG), requires the automatic generation of coherent and accurate captions for the given med…

cs.CV2023★ 3 cited

Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generation

Yaowei Li, Bang Yang, Xuxin Cheng +3

Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists. However, simultaneously establis…

cs.CL2023★ 2 cited

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation

Bang Yang, Fenglin Liu, Yuexian Zou +3

Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly…