7 accepted papers
Text recognition in complex scenes is a challenging task in Computer Vision. In this paper, we propose an innovative framework for scene text recognition, ASANet, which features an Alternate Attention Enhancement Encoder and a Masked Dual-modal Decoder. The encoder incorporates a 12-layer Alternatin…
Low-light enhancement has wide applications in autonomous driving, 3D reconstruction, remote sensing, surveillance, and so on, which can significantly improve information utilization. However, most existing methods lack generalization and are limited to specific tasks such as image recovery. To addr…
Vision Transformer, with the distinctive architecture and self-attention mechanisms, had profoundly influenced the field of computer vision, establishing Transformer-based models as benchmarks for semantic segmentation. In this study, we propose a pioneering hybrid model that fuses Kolmogorov-Arnold…
Oracle bone inscriptions, which are among the oldest known hieroglyphics in China, encompass rich historical and cultural information. However, the automatic recognition of oracle characters faces substantial challenges due to issues with data quality and long-tail distribution. This study introduce…
In recent years, significant progress has been made in the field of handwritten signature verification through methods based on deep learning. However, due to the high intra-class variability and high inter-class similarity of signature samples, achieving high accuracy and security in handwritten si…
Lip reading refers to the recognition of speech solely based on the subtle movements of the lips without audio information. Extracting temporal information in lip reading has always been a challenge in this field. In this work, we propose an effective method for extracting temporal information. Spec…
Lipreading refers to recognizing the speaker's speech content through the image sequence of lip movement without the speech signal. Currently, most models use a spatiotemporal (3D) convolutional layer combined with 2D CNN to extract spatial and temporal features from image sequences. However, compar…