18 citations · 83 across the 22 of their papers we have counts for
20 papers
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolution
Yutao Yuan, Chun Yuan
Image super-resolution is a fundamentally ill-posed problem because multiple valid high-resolution images exist for one low-resolution image. Super-resolution methods based on diff…
Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition
Feng Lu, Lijun Zhang, Xiangyuan Lan +3
Recent studies show that vision models pre-trained in generic visual learning tasks with large-scale data can provide useful feature representations for a wide range of visual perc…
Distilling Semantic Priors from SAM to Efficient Image Restoration Models
Quan Zhang, Xiaoyu Liu, Wei Li +6
In image restoration (IR), leveraging semantic priors from segmentation models has been a common approach to improve performance. The recent segment anything model (SAM) has emerge…
CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition
Feng Lu, Xiangyuan Lan, Lijun Zhang +3
Over the past decade, most methods in visual place recognition (VPR) have used neural networks to produce feature representations. These networks typically produce a global represe…
Deep Homography Estimation for Visual Place Recognition
Feng Lu, Shuting Dong, Lijun Zhang +4
Visual place recognition (VPR) is a fundamental task for many applications such as robot localization and augmented reality. Recently, the hierarchical VPR methods have received co…
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Zihan Zhong, Zhiqiang Tang, Tong He +2
The Segment Anything Model (SAM) stands as a foundational framework for image segmentation. While it exhibits remarkable zero-shot generalization in typical scenarios, its advantag…