SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering

• URL: http://arxiv.org/abs/2212.08283v3
• Date: Mon, 7 Aug 2023 08:32:54 GMT
• Title: SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering
• Authors: Feiqi Cao, Siwen Luo, Felipe Nunez, Zean Wen, Josiah Poon, Caren Han
• Abstract summary: The paper proposes a Scene Graph based co-Attention Network (SceneGATE) for TextVQA. It reveals the semantic relations among the objects, Optical Character Recognition (OCR) tokens and the question words. It is achieved by a TextVQA-based scene graph that discovers the underlying semantics of an image.
• Score: 2.8974040580489198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most TextVQA approaches focus on the integration of objects, scene texts and question words by a simple transformer encoder. But this fails to capture the semantic relations between different modalities. The paper proposes a Scene Graph based co-Attention Network (SceneGATE) for TextVQA, which reveals the semantic relations among the objects, Optical Character Recognition (OCR) tokens and the question words. It is achieved by a TextVQA-based scene graph that discovers the underlying semantics of an image. We created a guided-attention module to capture the intra-modal interplay between the language and the vision as a guidance for inter-modal interactions. To make explicit teaching of the relations between the two modalities, we proposed and integrated two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention. We conducted extensive experiments on two benchmark datasets, Text-VQA and ST-VQA. It is shown that our SceneGATE method outperformed existing ones because of the scene graph and its attention modules.

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