1 citations · 1 across the 9 of their papers we have counts for
13 papers
HiSC: Hierarchical Spatial Clustering Token Compression for Efficient 3D Scene Understanding
Jiuhe Qu, Yingping Liang, Ying Fu
3D vision-language models (3D VLMs) enable spatial reasoning over multi-view scenes but suffer from substantial token redundancy due to duplicated observations and large uninformat…
Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillation
Jiuhe Qu, Yingping Liang, Ying Fu
Change detection aims to identify semantic changes between remote sensing images. However, features from models are easily disturbed by non-semantic variations, such as illuminatio…
AquaStereo: Enabling Underwater Stereo Matching via Depth-Conditioned Diffusion and Geometry Self-Distillation
Qizhe Wei, Yingping Liang, Shaodi You +1
Learning-based stereo matching models struggle in underwater environments due to scarce in-domain data and the difficulty of extracting discriminative correspondences from degraded…
Boosting Text-Driven Video Segmentation via Geometry-Aware Distillation
Tianyu Zhu, Yingping Liang, Hesong Li +1
Text-driven Referring Video Object Segmentation (RVOS) aims to locate and segment target objects in videos given natural language. However, existing models are typically trained on…
A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design
Kai Gu, Yingping Liang, Senliang Peng +3
The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although dee…
Learning Dense Feature Matching via Lifting Single 2D Image to 3D Space
Yingping Liang, Yutao Hu, Wenqi Shao +1
Feature matching plays a fundamental role in many computer vision tasks, yet existing methods heavily rely on scarce and clean multi-view image collections, which constrains their…
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
Yuran Wang, Yingping Liang, Yutao Hu +1
Learning-based stereo matching models struggle in adverse weather conditions due to the scarcity of corresponding training data and the challenges in extracting discriminative feat…
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images
Yingping Liang, Ying Fu, Yutao Hu +3
Optical flow estimation is a crucial subfield of computer vision, serving as a foundation for video tasks. However, the real-world robustness is limited by animated synthetic datas…
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World
Yuran Wang, Yingping Liang, Ying Fu
Stereo matching methods rely on dense pixel-wise ground truth labels, which are laborious to obtain, especially for real-world datasets. The scarcity of labeled data and domain gap…
Relation-Guided Adversarial Learning for Data-free Knowledge Transfer
Yingping Liang, Ying Fu
Data-free knowledge distillation transfers knowledge by recovering training data from a pre-trained model. Despite the recent success of seeking global data diversity, the diversit…
Deep Learning Models for Colloidal Nanocrystal Synthesis
Kai Gu, Yingping Liang, Jiaming Su +7
Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multi-step crystallization processes. Despite the great success in the past 30 years, it remains…
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching
Yuran Wang, Yingping Liang, Hesong Li +1
The generalization and performance of stereo matching networks are limited due to the domain gap of the existing synthetic datasets and the sparseness of GT labels in the real data…
CascadeV-Det: Cascade Point Voting for 3D Object Detection
Yingping Liang, Ying Fu
Anchor-free object detectors are highly efficient in performing point-based prediction without the need for extra post-processing of anchors. However, different from the 2D grids,…