21 citations · 61 across the 12 of their papers we have counts for
12 papers
GeoSURGE: Geo-localization using Semantic Fusion with Hierarchy of Geographic Embeddings
Angel Daruna, Nicholas Meegan, Han-Pang Chiu +2
Worldwide visual geo-localization aims to determine the geographic location of an image anywhere on Earth using only its visual content. Despite recent progress, learning expressiv…
Efficient Domain-Adaptive Multi-Task Dense Prediction with Vision Foundation Models
Beomseok Kang, Niluthpol Chowdhury Mithun, Mikhail Sizintsev +2
Multi-task dense prediction, which aims to jointly solve tasks like semantic segmentation and depth estimation, is crucial for robotics applications but suffers from domain shift w…
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
Nazmul Karim, Niluthpol Chowdhury Mithun, Abhinav Rajvanshi +3
Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a labeled source domain to an unlabeled target domain. UDA methods have a strong assumption that…
Cross-View Visual Geo-Localization for Outdoor Augmented Reality
Niluthpol Chowdhury Mithun, Kshitij Minhas, Han-Pang Chiu +4
Precise estimation of global orientation and location is critical to ensure a compelling outdoor Augmented Reality (AR) experience. We address the problem of geo-pose estimation by…
Incremental Learning with Differentiable Architecture and Forgetting Search
James Seale Smith, Zachary Seymour, Han-Pang Chiu
As progress is made on training machine learning models on incrementally expanding classification tasks (i.e., incremental learning), a next step is to translate this progress to i…
GraphMapper: Efficient Visual Navigation by Scene Graph Generation
Zachary Seymour, Niluthpol Chowdhury Mithun, Han-Pang Chiu +2
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. A sparse, u…
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
Muhammad Zubair Irshad, Niluthpol Chowdhury Mithun, Zachary Seymour +3
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…
Striking the Right Balance: Recall Loss for Semantic Segmentation
Junjiao Tian, Niluthpol Mithun, Zach Seymour +2
Class imbalance is a fundamental problem in computer vision applications such as semantic segmentation. Specifically, uneven class distributions in a training dataset often result…
MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation
Zachary Seymour, Kowshik Thopalli, Niluthpol Mithun +3
Visual navigation for autonomous agents is a core task in the fields of computer vision and robotics. Learning-based methods, such as deep reinforcement learning, have the potentia…
RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization
Niluthpol Chowdhury Mithun, Karan Sikka, Han-Pang Chiu +2
We study an important, yet largely unexplored problem of large-scale cross-modal visual localization by matching ground RGB images to a geo-referenced aerial LIDAR 3D point cloud (…
Semantically-Aware Attentive Neural Embeddings for Image-based Visual Localization
Zachary Seymour, Karan Sikka, Han-Pang Chiu +2
We present an approach that combines appearance and semantic information for 2D image-based localization (2D-VL) across large perceptual changes and time lags. Compared to appearan…
Utilizing Semantic Visual Landmarks for Precise Vehicle Navigation
Varun Murali, Han-Pang Chiu, Supun Samarasekera +2
This paper presents a new approach for integrating semantic information for vision-based vehicle navigation. Although vision-based vehicle navigation systems using pre-mapped visua…