5 citations · 8 across the 12 of their papers we have counts for
13 papers
Fibre-to-Section Lifting over Measure-Preserving Dynamics: Variational Reciprocity and Memory-Reversal Rigidity
Lei Luo
Many nonlinear and variational problems are posed fibrewise over a probability space, whereas the dynamically relevant objects are measurable sections coupled by measure-preserving…
PhysFlow: Frequency Decoupled with Dual-Field Rectified Flow for Remote Photoplethysmography
Zixu Li, jianjun Qian, Hang Shao +2
Remote Photoplethysmography (rPPG) enables contactless pulse estimation from facial videos, serving as a vital tool for health monitoring. However, current deep learning methods of…
Self-Consistent Generative Paths via Admissible Random Variational Transport
Lei Luo, Yingzhen Zhang, Jian Yang
Modern generative models often define an entire probability path from a simple prior to the data law, rather than only an endpoint map. Diffusion models follow stochastic denoising…
Conditional Random Ordered Transport Spaces
Lei Luo, Jian Yang
A small Wasserstein distance does not certify that a transformation is admissible. In evidence-constrained, semantic, causal, physical, monotone, or risk-sensitive learning, one mu…
AnchorOPT: Towards Optimizing Dynamic Anchors for Adaptive Prompt Learning
Zheng Li, Yibing Song, Xin Zhang +3
Existing prompt learning methods, which are built upon CLIP models, leverage textual tokens as anchors to guide the learnable soft tokens. This guidance improves CLIP generalizatio…
Controllable-LPMoE: Adapting to Challenging Object Segmentation via Dynamic Local Priors from Mixture-of-Experts
Yanguang Sun, Jiawei Lian, Jian Yang +1
Large-scale foundation models provide powerful feature representations for downstream object segmentation tasks. However, when adapted to specific tasks through the full-parameter…
Straighten Viscous Rectified Flow via Noise Optimization
Jimin Dai, Jiexi Yan, Jian Yang +1
The Reflow operation aims to straighten the inference trajectories of the rectified flow during training by constructing deterministic couplings between noises and images, thereby…
Diff-Reg v2: Diffusion-Based Matching Matrix Estimation for Image Matching and 3D Registration
Qianliang Wu, Haobo Jiang, Yaqing Ding +5
Establishing reliable correspondences is crucial for all registration tasks, including 2D image registration, 3D point cloud registration, and 2D-3D image-to-point cloud registrati…
United Domain Cognition Network for Salient Object Detection in Optical Remote Sensing Images
Yanguang Sun, Jian Yang, Lei Luo
Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD me…
GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection
Yanguang Sun, Hanyu Xuan, Jian Yang +1
Recently, biological perception has been a powerful tool for handling the camouflaged object detection (COD) task. However, most existing methods are heavily dependent on the local…
Frequency-Spatial Entanglement Learning for Camouflaged Object Detection
Yanguang Sun, Chunyan Xu, Jian Yang +2
Camouflaged object detection has attracted a lot of attention in computer vision. The main challenge lies in the high degree of similarity between camouflaged objects and their sur…
Diff-Reg v1: Diffusion Matching Model for Registration Problem
Qianliang Wu, Haobo Jiang, Lei Luo +4
Establishing reliable correspondences is essential for registration tasks such as 3D and 2D3D registration. Existing methods commonly leverage geometric or semantic point features…
SGNet: Salient Geometric Network for Point Cloud Registration
Qianliang Wu, Yaqing Ding, Lei Luo +5
Point Cloud Registration (PCR) is a critical and challenging task in computer vision. One of the primary difficulties in PCR is identifying salient and meaningful points that exhib…