Search Results for author:
Found 9 papers, 2 papers with code
DaReNeRF: Direction-aware Representation for Dynamic Scenes
However, the straightforward decomposition of 4D dynamic scenes into multiple 2D plane-based representations proves insufficient for re-rendering high-fidelity scenes with complex motions.
Leveraging sinusoidal representation networks to predict fMRI signals from EEG
The ability to obtain fMRI information from EEG would enable cost-effective, imaging across a wider set of brain regions.
SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)
The accurate reconstruction of surgical scenes from surgical videos is critical for various applications, including intraoperative navigation and image-guided robotic surgery automation.
Inter-brain substrates of role switching during mother-child interaction
Mother-child interaction is highly dynamic and reciprocal.
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion.
One Million Scenes for Autonomous Driving: ONCE Dataset
To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules
It is achieved by predicting future displacement field of each voxel with a WarpNet.
Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph
On the one hand, we hide the secret information by coding the path in the knowledge graph, but not the conditional probability of each generated word; on the other hand, we can control the semantic expression of the generated steganographic text to a certain extent.
Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis
The final diagnosis is obtained by combining the ambiguity prior sample and lesion representation, and the whole network named $DenseSharp^{+}$ is end-to-end trainable.
