6 papers
Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation
Ali Zia, Usman Ali, Abdul Rehman +5
Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models. However, existing TTA approaches for anomaly segmentation remain l…
Spectrally Distilled Representations Aligned with Instruction-Augmented LLMs for Satellite Imagery
Minh Kha Do, Wei Xiang, Kang Han +5
Vision-language foundation models (VLFMs) promise zero-shot and retrieval understanding for Earth observation. While operational satellite systems often lack full multi-spectral co…
GIFSplat: Generative Prior-Guided Iterative Feed-Forward 3D Gaussian Splatting from Sparse Views
Tianyu Chen, Wei Xiang, Kang Han +4
Feed-forward 3D reconstruction offers substantial runtime advantages over per-scene optimization, which remains slow at inference and often fragile under sparse views. However, exi…
SwiftNDC: Fast Neural Depth Correction for High-Fidelity 3D Reconstruction
Kang Han, Wei Xiang, Lu Yu +3
Depth-guided 3D reconstruction has gained popularity as a fast alternative to optimization-heavy approaches, yet existing methods still suffer from scale drift, multi-view inconsis…
Plasmonic Color Filters Enable Label-Free Plasmon-Enhanced Array Tomography with sub-diffraction limited resolution
Kristian Caracciolo, Eugeniu Balaur, Erinna F. Lee +10
Three-dimensional (3D) imaging of the subcellular organisation and morphology of cells and tissues is essential for understanding biological function. Although staining is the most…
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation
Jianan Liu, Hao Li, Tao Huang +6
High-resolution (HR) magnetic resonance imaging is critical in aiding doctors in their diagnoses and image-guided treatments. However, acquiring HR images can be time-consuming and…