DEVICE: DEpth and VIsual ConcEpts Aware Transformer for TextCaps

• URL: http://arxiv.org/abs/2302.01540v1
• Date: Fri, 3 Feb 2023 04:31:13 GMT
• Title: DEVICE: DEpth and VIsual ConcEpts Aware Transformer for TextCaps
• Authors: Dongsheng Xu, Qingbao Huang, Yi Cai
• Abstract summary: We propose a DEpth and VIsual ConcEpts Aware Transformer (DEVICE) for TextCaps. Our DEVICE is capable of generalizing scenes more comprehensively and boosting the accuracy of described visual entities.
• Score: 10.87327544629769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-based image captioning is an important but under-explored task, aiming to generate descriptions containing visual objects and scene text. Recent studies have made encouraging progress, but they are still suffering from a lack of overall understanding of scenes and generating inaccurate captions. One possible reason is that current studies mainly focus on constructing the plane-level geometric relationship of scene text without depth information. This leads to insufficient scene text relational reasoning so that models may describe scene text inaccurately. The other possible reason is that existing methods fail to generate fine-grained descriptions of some visual objects. In addition, they may ignore essential visual objects, leading to the scene text belonging to these ignored objects not being utilized. To address the above issues, we propose a DEpth and VIsual ConcEpts Aware Transformer (DEVICE) for TextCaps. Concretely, to construct three-dimensional geometric relations, we introduce depth information and propose a depth-enhanced feature updating module to ameliorate OCR token features. To generate more precise and comprehensive captions, we introduce semantic features of detected visual object concepts as auxiliary information. Our DEVICE is capable of generalizing scenes more comprehensively and boosting the accuracy of described visual entities. Sufficient experiments demonstrate the effectiveness of our proposed DEVICE, which outperforms state-of-the-art models on the TextCaps test set. Our code will be publicly available.

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