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Found 9 papers, 0 papers with code
Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction
To explore the generalizability of the foundation model in downstream applications, we then apply the model to an unseen TBI dataset for prediction of PTE using zero-shot learning.
SemST: Semantically Consistent Multi-Scale Image Translation via Structure-Texture Alignment
Unsupervised image-to-image (I2I) translation learns cross-domain image mapping that transfers input from the source domain to output in the target domain while preserving its semantics.
Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints
Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training.
A Study on Improving Realism of Synthetic Data for Machine Learning
Synthetic-to-real data translation using generative adversarial learning has achieved significant success in improving synthetic data.
LGSQE: Lightweight Generated Sample Quality Evaluatoin
Despite prolific work on evaluating generative models, little research has been done on the quality evaluation of an individual generated sample.
TGHop: An Explainable, Efficient and Lightweight Method for Texture Generation
Finally, texture patches are stitched to form texture images of a large size.
Evaluation of Multimodal Semantic Segmentation using RGB-D Data
Our goal is to develop stable, accurate, and robust semantic scene understanding methods for wide-area scene perception and understanding, especially in challenging outdoor environments.
CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networks
Current perception systems often carry multimodal imagers and sensors such as 2D cameras and 3D LiDAR sensors.
NITES: A Non-Parametric Interpretable Texture Synthesis Method
A non-parametric interpretable texture synthesis method, called the NITES method, is proposed in this work.
