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Found 5 papers, 2 papers with code
Word length-aware text spotting: Enhancing detection and recognition in dense text image
Scene text spotting is essential in various computer vision applications, enabling extracting and interpreting textual information from images.
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network
Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e. g., facial parsing maps, facial landmarks) to focus on facial structure, thereby maintaining the consistency of the facial structure while restoring facial details.
Multi-scale Self-calibrated Network for Image Light Source Transfer
However, many problems for scene reconversion and shadow estimation tasks, including uncalibrated feature information and poor semantic information, are still unresolved, thereby resulting in insufficient feature representation.
Face Hallucination via Split-Attention in Split-Attention Network
However, most of them fail to take into account the overall facial profile and fine texture details simultaneously, resulting in reduced naturalness and fidelity of the reconstructed face, and further impairing the performance of downstream tasks (e. g., face detection, facial recognition).
Feature-Driven Super-Resolution for Object Detection
Comparing with some state-of-the-art SR algorithms with 4$\times$ scale factor, FDSR outperforms the detection performance mAP on MS COCO validation, VOC2007 databases with good generalization to other detection networks.
