In the presence of unmeasured confounders, we address the problem of treatment effect estimation from data fusion, that is, multiple datasets collected under different treatment assignment mechanisms.
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography.
Meta-learning has emerged as a potent paradigm for quick learning of few-shot tasks, by leveraging the meta-knowledge learned from meta-training tasks.
We study the problem of efficient semantic segmentation of large-scale 3D point clouds.
Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse."
Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics.
The number of iterations is reduced about 36% by using transfer learning in our DIP process.
We then seek pairs of images by comparing the baseline model with a set of full-reference IQA methods in gMAD.
We study the problem of efficient semantic segmentation for large-scale 3D point clouds.
Ranked #2 on 3D Semantic Segmentation on SensatUrban
Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of high variance when applied in complex real world environments.
In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture.
This is further confounded by the fact that shape information about encountered objects in the real world is often impaired by occlusions, noise and missing regions e. g. a robot manipulating an object will only be able to observe a partial view of the entire solid.
Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots.
The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration.
The observed results from the experiments demonstrated that the LGMD collision detector is feasible to work as a vision module for the quadcopter's collision avoidance task.