Explicitly-Decoupled Text Transfer With Minimized Background Reconstruction for Scene Text Editing.

Scene text editing aims to replace the source text with the target text while preserving the original background. Its practical applications span various domains, such as data generation and privacy protection, highlighting its increasing importance in recent years. In this study, we propose a novel Scene Text Editing network with Explicitly-decoupled text transfer and Minimized background reconstruction, called STEEM.

Cognition Guided Human-Object Relationship Detection.

Human-object relationship detection reveals the fine-grained relationship between humans and objects, helping the comprehensive understanding of videos. Previous human-object relationship detection approaches are mainly developed with object features and relation features without exploring the specific information of humans. In this paper, we propose a novel Relation-Pose Transformer (RPT) for human-object relationship detection.

ACE: Anchor-Free Corner Evolution for Real-Time Arbitrarily-Oriented Object Detection.

Objects with different orientations are ubiquitous in the real world (e.g., texts/hands in the scene image, objects in the aerial image, etc.

SLOAN: Scale-Adaptive Orientation Attention Network for Scene Text Recognition.

Scene text recognition, the final step of the scene text reading system, has made impressive progress based on deep neural networks. However, existing recognition methods devote to dealing with the geometrically regular or irregular scene text. They are limited to the semantically arbitrary-orientation scene text.