Confidence-aware Non-repetitive Multimodal Transformers for TextCaps
By Zhaokai Wang, Renda Bao, Qi Wu, Si Liu. 2020
28 citations recorded by the source corpus.
When describing an image, reading text in the visual scene is crucial to understand the key information. Recent work explores the TextCaps task, i.e. image captioning with reading Optical Character Recognition (OCR) tokens, which requires models to read text and cover them in generated captions. Existing approaches fail to generate accurate descriptions because of their (1) poor reading ability; (2) inability to choose the crucial words among all extracted OCR tokens; (3) repetition of words in predicted captions. To this end, we propose a Confidence-aware Non-repetitive Multimodal Transformers (CNMT) to tackle the above challenges. Our CNMT consists of a reading, a reasoning and a generation modules, in which Reading Module employs better OCR systems to enhance text reading ability and a confidence embedding to select the most noteworthy tokens. To address the issue of word redundancy in captions, our Generation Module includes a repetition mask to avoid predicting repeated word in captions. Our model outperforms state-of-the-art models on TextCaps dataset, improving from 81.0 to 93.0 in CIDEr. Our source code is publicly available.
Subfields: Computer Vision and Pattern Recognition, Multimodal Learning
How researchers see this paper
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- Hongji Zhu
- Fangxun Shu
- Aixi Zhang
- Yongliang Wang
- Xuchao Gong
- Si Liu
- Biaolong Chen
Citations in the corpus
- Multiple Transformer Mining for VizWiz Image Caption (2021, methodology): “For the samples with a large proportion of visual information, the visual model DLCT [8] and AOA [5]is mainly used, and for samples with rich visual and OCR information, the multi-modal model CNMT[12] is mainly used.”