Text detection in scenes based on deep neural networks have shown promising results. Instead of using word bounding box regression, recent state-of-the-art methods have started focusing on character bounding box and pixel-level prediction. This necessitates the need to link adjacent characters, which we propose in this paper using a novel Graph Neural Network (GNN) architecture that allows us to learn both node and edge features as opposed to only the node features under the typical GNN. The main advantage of using GNN for link prediction lies in its ability to connect characters which are spatially separated and have an arbitrary orientation. We show our concept on the well known SynthText dataset, achieving top results as compared to state-of-the-art methods.
The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment
National Engineering Research Center of Software Engineering, Peking University（北京大学软件工程国家工程研究中心）；School of Computer Science, Peking University（北京大学计算机学院）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（教育部高可信软件技术重点实验室）；GRG Banking Equipment Co., Ltd.（广电运通金融电子股份有限公司）；Center on Frontiers of Computing Studies, Peking University（北京大学计算前沿研究中心）；Peking University Information Technology Institute (Tianjin Binhai)（北京大学（天津滨海）信息技术研究院）
Learning 3D Scene Analogies with Neural Contextual Scene Maps
Dept. of Electrical and Computer Engineering, Seoul National University（电子与计算机工程系，首尔国立大学）；Interdisciplinary Program in Artificial Intelligence and INMC, Seoul National University（人工智能跨学科项目及INMC，首尔国立大学）
