Search Results for author:
Found 8 papers, 3 papers with code
Efficient Decoder for End-to-End Oriented Object Detection in Remote Sensing Images
To address these limitations, we propose an end-to-end oriented detector equipped with an efficient decoder, which incorporates two technologies, Rotated RoI attention (RRoI attention) and Selective Distinct Queries (SDQ).
A unified framework for STAR-RIS coefficients optimization
Due to the lack of a unified comparison of communication systems equipped with different modes of STAR-RIS and the performance degradation caused by the constraints involving discrete selection, this paper proposes a unified optimization framework for handling the STAR-RIS operating mode and discrete phase constraints.
CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer
Contour based scene text detection methods have rapidly developed recently, but still suffer from inaccurate frontend contour initialization, multi-stage error accumulation, or deficient local information aggregation.
TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask
Arbitrary-shaped scene text detection is a challenging task due to the variety of text changes in font, size, color, and orientation.
Generalizable No-Reference Image Quality Assessment via Deep Meta-learning
Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions.
Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization
Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an ``average'' user.
MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment
The underlying idea is to learn the meta-knowledge shared by human when evaluating the quality of images with various distortions, which can then be adapted to unknown distortions easily.
Facial Action Unit Detection via Adaptive Attention and Relation
Specifically, we propose an adaptive attention regression network to regress the global attention map of each AU under the constraint of attention predefinition and the guidance of AU detection, which is beneficial for capturing both specified dependencies by landmarks in strongly correlated regions and facial globally distributed dependencies in weakly correlated regions.
