1 citations · 1 across the 6 of their papers we have counts for
6 papers
A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios
Zhuo Song, Ye Zhang, Kunhong Li +2
Cross-view geo-localization is a promising solution for large-scale localization problems, requiring the sequential execution of retrieval and metric localization tasks to achieve…
Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction
Changjun Li, Runqing Jiang, Zhuo Song +3
Post-training quantization (PTQ) has evolved as a prominent solution for compressing complex models, which advocates a small calibration dataset and avoids end-to-end retraining. H…
Pluggable Style Representation Learning for Multi-Style Transfer
Hongda Liu, Longguang Wang, Weijun Guan +2
Due to the high diversity of image styles, the scalability to various styles plays a critical role in real-world applications. To accommodate a large amount of styles, previous mul…
SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer
Hongda Liu, Longguang Wang, Ye Zhang +2
Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transfor…
Progressive Correspondence Regenerator for Robust 3D Registration
Guiyu Zhao, Sheng Ao, Ye Zhang +2
Obtaining enough high-quality correspondences is crucial for robust registration. Existing correspondence refinement methods mostly follow the paradigm of outlier removal, which ei…
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers
Runqing Jiang, Ye Zhang, Longguang Wang +2
Post-training quantization (PTQ) has emerged as a promising solution for reducing the storage and computational cost of vision transformers (ViTs). Recent advances primarily target…