Search Results for author:
Found 9 papers, 2 papers with code
Unsupervised Domain Adaptation for Semantic Segmentation with Pseudo Label Self-Refinement
Recent state-of-the-art (SOTA) UDA methods employ a teacher-student self-training approach, where a teacher model is used to generate pseudo-labels for the new data which in turn guide the training process of the student model.
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
In this regard, source-free domain adaptation (SFDA) excels as access to source data is no longer required during adaptation.
Ranked #5 on
Source-Free Domain Adaptation
on VisDA-2017
Cross-View Visual Geo-Localization for Outdoor Augmented Reality
Precise estimation of global orientation and location is critical to ensure a compelling outdoor Augmented Reality (AR) experience.
GraphMapper: Efficient Visual Navigation by Scene Graph Generation
Understanding the geometric relationships between objects in a scene is a core capability in enabling both humans and autonomous agents to navigate in new environments.
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language instructions in unseen environments.
MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation
Visual navigation for autonomous agents is a core task in the fields of computer vision and robotics.
RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization
To enable large-scale evaluation, we introduce a new dataset containing over 550K pairs (covering 143 km^2 area) of RGB and aerial LIDAR depth images.
Semantically-Aware Attentive Neural Embeddings for Image-based Visual Localization
Furthermore, we present an extensive study demonstrating the contribution of each component of our model, showing $8$--$15\%$ and $4\%$ improvement from adding semantic information and our proposed attention module.
Utilizing Semantic Visual Landmarks for Precise Vehicle Navigation
Experimental evaluations validate that the injection of semantic information associated with visual landmarks using our approach achieves substantial improvements in accuracy on GPS-denied navigation solutions for large-scale urban scenarios
