Portrait of Linlian Jiang is unavailable

Linlian Jiang

linlian.jiang@mila.quebec
PhD - Concordia University
Supervisor
Yang Wang
Research Topics
Applied Machine Learning
Computer Vision
Robotics
Test Time Adaptation
Website

Publications

FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting
Tianhao Xie
Linlian Jiang
Xinxin Zuo
Yang Wang
Tiberiu Popa
Realistic scene appearance modeling has advanced rapidly with Gaussian Splatting, which enables real-time, high-quality rendering. Recent ad… (see more)vances introduced per-primitive textures that incorporate spatial color variations within each Gaussian, improving their expressiveness. However, texture-based Gaussians parameterize appearance with a uniform per-Gaussian sampling grid, allocating equal sampling density regardless of local visual complexity, which leads to inefficient texture space utilization. We introduce FACT-GS, a Frequency-Aligned Complexity-Aware Texture Gaussian Splatting framework that allocates texture sampling density according to local visual frequency. Grounded in adaptive sampling theory, FACT-GS reformulates texture parameterization as a differentiable sampling-density allocation problem, replacing the uniform textures with a learnable frequency-aware allocation strategy implemented via a deformation field whose Jacobian modulates local sampling density. Built on 2D Gaussian Splatting, FACT-GS performs non-uniform sampling on fixed-resolution texture grids, preserving real-time performance while recovering sharper high-frequency details under the same parameter budget.
2026-09-20
IEEE/CVF Conference on Computer Vision and Pattern Recognition (published)
doi.org
openreview.net
No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation
Linlian Jiang
Wentao Ju
Sadman Rakib Pinon
Jianwei Xian
Zhixiang Chi
Xinxin Zuo
Yang Wang
LiDAR semantic segmentation often degrades under real-world deployment due to evolving sensing conditions, while collecting new annotations … (see more)for retraining is impractical. Test-time adaptation (TTA) updates model parameters online using pseudo-label supervision, but directly applying standard TTA strategies to LiDAR data is challenging. Because pseudo-label reliability is spatially heteroscedastic under range-dependent sparsity and occlusion, uniform updates on globally shared parameters can inject unstable gradients and destabilize adaptation. We propose a geometry-constrained test-time prompt tuning framework for LiDAR semantic segmentation. Our method estimates per-location sensing reliability from depth-consistent beam terminations and neighborhood support, and uses it to reweight spatial supervision. Adaptation is confined to lightweight prompt adapters inserted into a frozen backbone, with spatial gating to prevent unreliable regions from perturbing globally shared representations. A temporally smoothed prototype alignment strategy further stabilizes online updates by accumulating reliable semantic evidence over time. Experiments on standard LiDAR benchmarks demonstrate improved adaptation stability and segmentation performance under deployment variations without additional annotations.
2026-06-28
arXiv (preprint)
doi.org
arxiv.org
PointMAC: Meta-Learned Adaptation for Robust Test-Time Point Cloud Completion
Linlian Jiang
Rui Ma
Li Gu
Ziqiang Wang
Xinxin Zuo
Yang Wang
Point cloud completion is essential for robust 3D perception in safety-critical applications such as robotics and augmented reality. However… (see more), existing models perform static inference and rely heavily on inductive biases learned during training, limiting their ability to adapt to novel structural patterns and sensor-induced distortions at test time. To address this limitation, we propose PointMAC, a meta-learned framework for robust test-time adaptation in point cloud completion. It enables sample-specific refinement without requiring additional supervision. Our method optimizes the completion model under two self-supervised auxiliary objectives that simulate structural and sensor-level incompleteness. A meta-auxiliary learning strategy based on Model-Agnostic Meta-Learning (MAML) ensures that adaptation driven by auxiliary objectives is consistently aligned with the primary completion task. During inference, we adapt the shared encoder on-the-fly by optimizing auxiliary losses, with the decoder kept fixed. To further stabilize adaptation, we introduce Adaptive
2025-12-02
Conference on Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net