Paper Reading AI Learner

Efficient scene text image super-resolution with semantic guidance

2024-03-20 06:20:54
LeoWu TomyEnrique, Xiangcheng Du, Kangliang Liu, Han Yuan, Zhao Zhou, Cheng Jin

Abstract

Scene text image super-resolution has significantly improved the accuracy of scene text recognition. However, many existing methods emphasize performance over efficiency and ignore the practical need for lightweight solutions in deployment scenarios. Faced with the issues, our work proposes an efficient framework called SGENet to facilitate deployment on resource-limited platforms. SGENet contains two branches: super-resolution branch and semantic guidance branch. We apply a lightweight pre-trained recognizer as a semantic extractor to enhance the understanding of text information. Meanwhile, we design the visual-semantic alignment module to achieve bidirectional alignment between image features and semantics, resulting in the generation of highquality prior guidance. We conduct extensive experiments on benchmark dataset, and the proposed SGENet achieves excellent performance with fewer computational costs. Code is available at this https URL

Abstract (translated)

场景文本图像超分辨率显著提高了场景文本识别的准确性。然而,许多现有方法强调性能而非效率,并忽略了在部署场景中实现轻量级解决方案的实际需求。面对这些问题,我们的工作提出了一种高效的框架SGENet,以促进在资源受限平台上部署。SGENet包含两个分支:超分辨率分支和语义引导分支。我们使用轻量预训练识别器作为语义提取器来增强文本信息的理解。同时,我们设计了一个视觉语义对齐模块,以实现图像特征和语义之间的双向对齐,从而生成高质量的先验指导。我们在基准数据集上进行广泛的实验,与提出的SGENet相比,具有卓越的性能,但计算成本较低。代码可在此处下载:https://url

URL

https://arxiv.org/abs/2403.13330

PDF

https://arxiv.org/pdf/2403.13330.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot