This late-fusion block uses the dense context features to guide the depth prediction based on demonstrations by sparse depth features.
In this paper, we propose a Fusing-and-Filling Generative Adversarial Network (F2GAN) to generate realistic and diverse images for a new category with only a few images.
Based on our previous optimization/criteria-based L-Shape fitting algorithm, we here propose a data-driven and model-based method for robust vehicle segmentation and tracking.
Conclusions: A solution for the TCM syndrome classification problem associated with VMCI is established based on the latent tree analysis of unlabeled symptom survey data.
Conclusions: A data-driven method for TCM syndrome identification and classification is presented.