PROJECT TITLE : Iterative Refinement for Multi-source Visual Domain Adaptation ABSTRACT: One of the most difficult aspects of multi-source domain adaptation is figuring out how to minimize the differences in domains that exist
PROJECT TITLE : Learning Versatile Convolution Filters for Efficient Visual Recognition ABSTRACT: This article presents versatile filters that can be used to construct efficient convolutional neural networks, which are widely
PROJECT TITLE : Deep Visual Odometry with Adaptive Memory ABSTRACT: A novel deep visual odometry (VO) method that takes into account global information by selecting memory and refining poses is presented here. The currently available
PROJECT TITLE : Iterative Refinement for Multi-source Visual Domain Adaptation ABSTRACT: One of the most difficult aspects of multi-source domain adaptation is figuring out how to minimize the differences in domains that exist
PROJECT TITLE : A Review of Single-Source Deep Unsupervised Visual Domain Adaptation ABSTRACT: Deep neural networks have been shown to perform exceptionally well across a broad spectrum of benchmark vision tasks as a result