6 papers
Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution
Sichen Guo, Wenjie Li, Yuanyang Liu +3
Recently, Mamba-based super-resolution (SR) methods have demonstrated the ability to capture global receptive fields with linear complexity, addressing the quadratic computational…
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution
Siyu Xu, Wenjie Li, Guangwei Gao +3
Face super-resolution (FSR) under limited computational budgets remains challenging. Existing methods often treat all facial pixels equally, leading to suboptimal resource allocati…
DAWA: Dynamic Ambiguity-Wise Adaptation for Real-Time Domain Adaptive Semantic Segmentation
Taorong Liu, Zhen Zhang, Liang Liao +2
Test-time domain adaption (TTDA) for semantic segmentation aims to adapt a segmentation model trained on a source domain to a target domain for inference on-the-fly, where both eff…
WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition
Guoan Xu, Yang Xiao, Wenjing Jia +3
While recent semantic segmentation networks heavily rely on powerful pretrained encoders, most employ simplistic decoders, leading to suboptimal trade-offs between semantic context…
Attention-Guided Multi-scale Interaction Network for Face Super-Resolution
Xujie Wan, Wenjie Li, Guangwei Gao +3
Recently, CNN and Transformer hybrid networks demonstrated excellent performance in face super-resolution (FSR) tasks. Since numerous features at different scales in hybrid network…
Efficient Semantic Segmentation via Lightweight Multiple-Information Interaction Network
Yangyang Qiu, Guoan Xu, Guangwei Gao +3
Recently, integrating the local modeling capabilities of Convolutional Neural Networks (CNNs) with the global dependency strengths of Transformers has created a sensation in the se…