6 papers
GlowGS: Generative Semantic Feature Learning for 3D Gaussian Splatting in Nighttime Glow Scenes
Beibei Lin, Xiao Cao, Jingyuan Guo +1
Existing 3DGS methods effectively render high-quality novel views in clear-day scenes. However, they struggle with night scenes, particularly in glow regions, due to the lack of st…
Learning What Helps: Task-Aligned Context Selection for Vision Tasks
Jingyu Guo, Emir Konuk, Fredrik Strand +2
Humans often resolve visual uncertainty by comparing an image with relevant examples, but ViTs lack the ability to identify which examples would improve their predictions. We prese…
Efficient Self-Supervised Adaptation for Medical Image Analysis
Moein Sorkhei, Emir Konuk, Jingyu Guo +3
Self-supervised adaptation (SSA) improves foundation model transfer to medical domains but is computationally prohibitive. Although parameter efficient fine-tuning methods such as…
Hyper3D: Efficient 3D Representation via Hybrid Triplane and Octree Feature for Enhanced 3D Shape Variational Auto-Encoders
Jingyu Guo, Sensen Gao, Jia-Wang Bian +4
Recent 3D content generation pipelines often leverage Variational Autoencoders (VAEs) to encode shapes into compact latent representations, facilitating diffusion-based generation.…
MAMOC: MRI Motion Correction via Masked Autoencoding
Lennart Alexander Van der Goten, Jingyu Guo, Kevin Smith
The presence of motion artifacts in magnetic resonance imaging (MRI) scans poses a significant challenge, where even minor patient movements can lead to artifacts that may compromi…
Random Token Fusion for Multi-View Medical Diagnosis
Jingyu Guo, Christos Matsoukas, Fredrik Strand +1
In multi-view medical diagnosis, deep learning-based models often fuse information from different imaging perspectives to improve diagnostic performance. However, existing approach…